Distributed charging pile cooperative control method and system, storage medium and electronic device

Abstract

The application relates to a distributed charging pile cooperative control method and system, a storage medium and an electronic device, and relates to the technical field of charging piles. The method comprises the following steps: determining the perishable goods risk value of a partition according to the actual temperature and the actual goods types, obtaining the perishable goods spread risk value of the partition, and obtaining the value degree of the goods stored in the partition; determining the goods damage risk value of the partition according to the perishable goods spread risk value, the value degree and the perishable goods risk value, and determining the overall goods damage risk value of a target refrigerated vehicle according to the goods damage risk value of each partition; determining the overall power urgency index of the target refrigerated vehicle according to the overall goods damage risk value, the actual remaining power and the distribution estimated departure time; determining the initial distribution power of the target charging pile according to the overall power urgency index and the power distribution demand; and uploading the initial distribution power to a consensus network to determine the final distribution power of the target charging pile. The application has the effect of improving the charging pile power distribution accuracy.

Description

Technical Field

[0001] This application relates to the field of charging pile technology, specifically to a distributed charging pile collaborative control method, system, storage medium, and electronic device. Background Technology

[0002] Charging piles are the core infrastructure for providing electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), equivalent to "gas stations" for traditional gasoline vehicles. They are specialized devices that safely and controllably transmit grid power to the vehicle's battery. Distributed charging piles, in contrast to centralized charging piles (such as large charging stations with centralized power supply clusters), refer to a charging pile network characterized by decentralized layout, localized power supply, and autonomous management. Essentially, they treat charging piles as independent nodes in a distributed energy network, achieving multi-pile collaboration through communication and consensus mechanisms, rather than relying on a single central controller for centralized management.

[0003] Currently, the common approach for distributed charging pile collaborative control is as follows: each charging pile acts as an independent node, and based on the electrical parameter data uploaded by the battery management system of the vehicle (such as a refrigerated truck) charging at the charging pile, the power allocation is initially determined and uploaded to the consensus network, and finally the power allocated to the charging pile is determined. In this approach, the initial power allocation process only considers the single dimension of electrical parameter data, which is too few dimensions to consider, resulting in poor accuracy of power allocation for the charging pile. Summary of the Invention

[0004] To improve the accuracy of power allocation for charging piles, this application provides a distributed charging pile collaborative control method, system, storage medium, and electronic device.

[0005] The first aspect of this application provides a distributed charging pile cooperative control method, specifically including: Obtain the actual temperature of each compartment in the target refrigerated truck to be charged at the target charging station, as well as at least one actual type of goods stored therein; Based on the actual temperature and each of the actual goods types, determine the goods spoilage risk value of the zone, obtain the spoilage spread risk value of the zone, and obtain the value of the goods stored in the zone. Based on the risk value of spoilage spread, the value, and the risk value of spoilage of the goods, the risk value of cargo damage for each zone is determined, and based on the risk value of cargo damage for each zone, the overall risk value of cargo damage for the target refrigerated truck is determined. Obtain the actual remaining power of the target refrigerated truck and the estimated departure time for delivery. Based on the overall risk value of cargo damage, the actual remaining power, and the estimated departure time for delivery, determine the overall power urgency index of the target refrigerated truck. The power allocation requirements of other charging piles within the charging area to which the target charging pile belongs are obtained through a consensus network. Based on the overall power urgency index and each of the power allocation requirements, the initial power allocation for the target charging pile is determined. The initial allocated power is uploaded to the consensus network. Based on a preset consensus algorithm, the final allocated power of the target charging pile is determined, and the power of the target charging pile is adjusted according to the final allocated power.

[0006] By employing the aforementioned technical solution, after obtaining the actual temperature of each zone and the actual types of goods stored therein, the risk of goods spoilage in each zone is analyzed based on the temperature and goods type. This allows for a relatively accurate determination of the spoilage risk value for each zone. Next, by combining the risk value of spoilage spread and the value of the goods within each zone, the spread of spoilage in the event of spoilage is analyzed, further determining the extent of loss. This allows for a more reasonable determination of the damage risk value for each zone, which is then aggregated to obtain the overall damage risk value for the target refrigerated truck. This enables precise quantification of the safety priority of the goods in the target refrigerated truck, providing a core basis for power allocation. Subsequently, by combining the actual remaining battery power of the target refrigerated truck and the estimated delivery departure time, the damage safety requirements, power replenishment requirements, and time urgency are organically integrated to determine an overall power urgency index that reflects the charging urgency of the target refrigerated truck. This avoids misjudgment of priority due to single-dimensional demand assessment and ensures a precise match between power allocation and the actual charging needs of the target refrigerated truck. Furthermore, based on the overall power urgency index and combined with the demand differences of other charging piles, the initial power allocation of the target charging pile is determined more accurately. Finally, based on the uploaded initial power allocation, the consensus network can determine the final power allocation of the target charging pile more accurately, thereby improving the accuracy of power allocation for charging piles.

[0007] In one implementation, determining the spoilage risk value of the goods in the zone based on the actual temperature and each of the actual goods types specifically includes: Obtain multiple historical cargo types from which goods have rotted in refrigerated trucks, and select at least one important cargo type from these multiple historical cargo types; The system obtains multiple historical temperatures at which goods of the important cargo types in the historical refrigerated trucks rotted, performs cluster analysis on the multiple historical temperatures to obtain multiple temperature ranges, and selects at least one important temperature range from the multiple temperature ranges. Determine a first risk factor for the spoilage of goods of the aforementioned important goods types, and determine a second risk factor for the spoilage of goods within each of the aforementioned important temperature ranges; The spoilage risk value of the goods in the zone is determined based on the actual temperature, the actual types of goods, the first risk coefficient, and the second risk coefficients.

[0008] In one implementation, determining the spoilage risk value of the goods in the zone based on the actual temperature, each of the actual goods types, the first risk coefficient, and each of the second risk coefficients specifically includes: If the actual product type is the important product type, then the actual product type is determined as the target product type. If the actual temperature is included in the important temperature range corresponding to the target product type, then the important temperature range including the actual temperature is determined as the target temperature range. Multiply at least one first risk coefficient of the target product type by a second risk coefficient of the target temperature range to obtain at least one coefficient product; The risk value of goods spoilage in the zone is determined by multiplying all the coefficients.

[0009] In one implementation, obtaining the decay spread risk value of the partition specifically includes: If the product of the coefficients is greater than a preset product threshold, the corresponding target product type is determined to be a potential spoilage type. When goods of the potential type of spoilage in the partition are found to be spoiled, the overall impact coefficient on the remaining goods in the partition is not less than 1. When the number of potential decay types is single, the product of the coefficients corresponding to the potential decay types is multiplied by the overall influence coefficient to obtain a first multiplication result. Based on the first multiplication result, the decay spread risk value of the partition is determined. The larger the first multiplication result, the larger the decay spread risk value. The decay spread risk value is not less than 1. When there are multiple potential decay types, the product of the coefficients corresponding to the same potential decay type and the overall influence coefficient are multiplied together to obtain the second multiplication result; The rot spread risk value of the partition is determined based on the largest second multiplication result among all the second multiplication results.

[0010] In one implementation, determining the overall cargo damage risk value of the target refrigerated truck based on the cargo damage risk value of each of the partitions specifically includes: The summation result of the partition is obtained by summing the first risk coefficients of each important commodity type among all the actual commodity types. The ratio of the summation result to the summation result of all partitions is determined as the decay weight of the partition. The overall risk value of cargo damage for the target refrigerated truck is obtained by multiplying the cargo damage risk value and the spoilage weight of each partition and summing them.

[0011] In one implementation, determining the overall power urgency index of the target refrigerated truck based on the overall cargo damage risk value, the actual remaining power, and the estimated delivery departure time specifically includes: Obtain the delivery route and required delivery time of the target refrigerated truck; Based on the actual remaining power, the estimated delivery departure time, the required delivery time, and the delivery route, a first power urgency index for the target refrigerated truck is determined, and a second power urgency index for the target refrigerated truck is determined based on the overall risk value of cargo damage. The first power urgency index and the second power urgency index are summed to obtain the overall power urgency index of the target refrigerated truck.

[0012] In one implementation, determining the first power urgency index of the target refrigerated truck based on the actual remaining power, the estimated delivery departure time, the required delivery time, and the delivery route specifically includes: Subtract the estimated delivery departure time from the required delivery time to obtain the estimated delivery time, and determine the average speed of the target refrigerated truck based on the delivery route and the estimated delivery time; Based on the average vehicle speed and the estimated delivery time, the estimated driving power consumption of the target refrigerated vehicle is determined, and based on the ambient temperature during the delivery period, the estimated refrigeration power consumption of the target refrigerated vehicle is determined. Based on the estimated driving power consumption and the estimated refrigeration power consumption, the minimum safe delivery power of the target refrigerated truck is determined, and the estimated charging time is obtained by subtracting the current time from the estimated departure time. Multiply the estimated charging time by the actual current output power of the target charging pile to obtain the estimated charging amount, and sum the estimated charging amount and the actual remaining power to obtain the estimated power level of the target refrigerated truck; The first power urgency index of the target refrigerated truck is determined based on the difference between the minimum safe delivery power and the expected power. The larger the difference, the higher the first power urgency index.

[0013] A second aspect of this application provides a distributed charging pile collaborative control system, specifically including: The information acquisition module is used to acquire the actual temperature of each compartment in the target refrigerated truck to be charged at the target charging pile, as well as at least one actual type of goods stored therein. The risk determination module is used to determine the spoilage risk value of the goods in the partition based on the actual temperature and each of the actual goods types, and to obtain the spoilage spread risk value of the partition and the value of the goods stored in the partition. The cargo damage assessment module is used to determine the cargo damage risk value of the zone based on the risk value of spoilage spread, the value, and the risk value of cargo spoilage, and to determine the overall cargo damage risk value of the target refrigerated truck based on the cargo damage risk value of each zone. The index determination module is used to obtain the actual remaining power of the target refrigerated truck and the estimated departure time of delivery, and to determine the overall power urgency index of the target refrigerated truck based on the overall risk value of cargo damage, the actual remaining power, and the estimated departure time of delivery. The power determination module is used to obtain the power allocation requirements of other charging piles in the charging area to which the target charging pile belongs through a consensus network, and determine the initial allocation power of the target charging pile based on the overall power urgency index and each of the power allocation requirements. The power adjustment module is used to upload the initial allocated power to the consensus network, determine the final allocated power of the target charging pile based on a preset consensus algorithm, and adjust the power of the target charging pile according to the final allocated power.

[0014] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when loaded and executed by a processor, performs the steps of the method described in any one of the first aspects.

[0015] A fourth aspect of this application provides an electronic device, specifically comprising: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0016] In summary, this application includes at least one of the following beneficial technical effects: After obtaining the actual temperature of the partition and the actual type of goods stored therein, based on the temperature conditions and the type of goods stored in the partition, the risk of goods spoilage in the partition is analyzed, thereby accurately determining the spoilage risk value of the partition. Then, combining the risk value of spoilage spread and the value of the goods in the partition, the spread of spoilage in the event of spoilage is analyzed, further analyzing the extent of loss of goods in the partition. This allows for a more reasonable determination of the damage risk value of each partition, which is then aggregated to obtain the overall damage risk value of the target refrigerated truck. This achieves precise quantification of the safety priority of the goods in the target refrigerated truck, providing a core demand basis for power allocation. Subsequently, by combining the actual remaining power of the target refrigerated truck and the estimated departure time for delivery, the damage safety requirements, power replenishment requirements, and time urgency are organically integrated to determine an overall power urgency index that reflects the charging urgency of the target refrigerated truck. This avoids priority misjudgment caused by single-dimensional demand assessment and ensures accurate matching of power allocation with the actual charging needs of the target refrigerated truck. Furthermore, based on the overall power urgency index and combined with the demand differences of other charging piles, the initial power allocation of the target charging pile is determined more accurately. Finally, based on the uploaded initial power allocation, the consensus network can determine the final power allocation of the target charging pile more accurately, thereby improving the accuracy of power allocation for charging piles. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a distributed charging pile collaborative control method provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the relationship between important product types and important temperature ranges, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of a distributed charging pile collaborative control system provided in an embodiment of this application.

[0018] Explanation of reference numerals in the attached diagram: 11. Information acquisition module; 12. Risk determination module; 13. Cargo damage assessment module; 14. Index determination module; 15. Power determination module; 16. Power adjustment module. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0020] In the description of the embodiments of this application, words such as "exemplarily," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0021] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, or A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0022] See Figure 1 This application discloses a flowchart of a distributed charging pile collaborative control method, which can be implemented using a computer program or run on a distributed charging pile collaborative control system based on the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application, specifically including: S101: Obtain the actual temperature of each compartment in the target refrigerated truck to be charged at the target charging station, as well as at least one actual type of goods stored therein.

[0023] Specifically, in the embodiments of the present application, the target charging pile is a charging pile that is ready to charge the target refrigerated truck in the charging area. The charging area includes a plurality of charging piles, and the brands of the charging piles can be the same or different. The charging area may be a charging station supporting the cold chain logistics site to which the target refrigerated truck belongs, or may be an external public charging station. Each charging pile in the charging area can be regarded as an independent distributed node, with complete capabilities of data collection, local calculation, communication interaction and consensus participation. Data interaction can be performed between each distributed node, breaking information islands, enabling scattered nodes to form global perception, thereby reaching consensus, executing cooperative control, and finally achieving the overall goal of the cluster. All distributed nodes adopt a distributed communication architecture, each distributed node is peer-to-peer, forms a consensus through a distributed consensus algorithm / smart contract, and is self-organizing and self-repairing. In other words, all distributed nodes form a decentralized, peer-to-peer interconnected logical network, so even if some distributed nodes fail, the operation will not be affected.

[0024] Further, the target refrigerated truck is a multi-zone new energy refrigerated truck that currently needs charging. Each zone in the carriage stores goods to be delivered. For example, the zones in the target refrigerated truck include but are not limited to a freezing zone and a refrigerating zone. The freezing zone usually stores meat, seafood or quick-frozen food; the refrigerating zone usually stores goods such as dairy products, fruits and vegetables.

[0025] A feasible way to obtain the actual temperature in a single zone of the target refrigerated truck is: during the charging handshake or charging process between the target refrigerated truck and the target charging pile, send an instruction of "requesting zone temperature" to the target refrigerated truck based on a preset communication protocol. After the Vehicle Control Unit (VCU) of the target refrigerated truck receives the instruction, it obtains the actual temperature in each zone through temperature sensors preset in each zone. The preset communication protocol may be ISO15118 or GB / T 27930. In addition, a feasible way to obtain the actual goods type of goods stored in a single zone is: when the owner of the target refrigerated truck scans the code through a terminal to start charging the vehicle by the target charging pile, with the owner's consent, receive the actual goods type of goods stored in each zone sent by the owner's terminal. In other embodiments, with authorization, the actual goods type of goods stored in a single zone of the target refrigerated truck can also be obtained through the Cold Chain Management System (CCMS) supporting the cold chain logistics site. The cold chain management system is an intelligent management platform integrating hardware perception, software control and management, and data interaction. Its core goal is to monitor and optimize the whole process of all cold chain operation links (warehousing, storage, sorting, loading, temperature control) of the site.

[0026] S102: Based on the actual temperature and the actual types of goods, determine the spoilage risk value of the goods in each zone, obtain the spoilage spread risk value of each zone, and obtain the value of the goods stored in each zone.

[0027] Specifically, after determining the actual temperature and the actual types of goods stored in a single zone, it is necessary to determine the spoilage risk value of the goods in that zone. The spoilage risk value represents the degree of risk of spoilage in that zone. One feasible implementation method is as follows: Based on the refrigerated truck cargo spoilage records, multiple historical cargo types that have experienced spoilage in refrigerated trucks are identified. These historical cargo types may contain identical items. Then, the number of times a single historical cargo type appears repeatedly is counted. If this number exceeds a preset threshold, it indicates that the cargo type of that historical cargo type has spoiled frequently. This historical cargo type is then identified as an important cargo type, meaning it is prone to spoilage when there are temperature fluctuations inside the refrigerated truck. At least one important cargo type must be present. It should be noted that the refrigerated truck cargo spoilage records include, but are not limited to, the historical cargo types that have experienced spoilage and the zone temperature at the time of spoilage. Furthermore, the refrigerated truck cargo spoilage records can be obtained through the aforementioned cold chain management system.

[0028] Furthermore, based on the aforementioned records of refrigerated truck cargo spoilage, multiple historical temperatures at which spoilage occurred for a single important cargo type in historical refrigerated trucks were obtained. Then, using the K-Means clustering algorithm, cluster analysis was performed on these historical temperatures to divide them into multiple temperature intervals covering all historical temperatures. The clustering analysis process can be summarized as follows: multiple historical temperatures are divided into multiple clusters, with the smallest temperature difference within the same cluster and the largest temperature difference between different clusters. The extreme temperature values ​​of each cluster are extracted to obtain multiple temperature intervals. This clustering analysis process is existing technology and will not be elaborated further. Next, the number of historical temperatures contained in each temperature interval is counted. If the number exceeds a preset threshold, then that temperature interval is determined as an important temperature interval corresponding to a single important cargo type, i.e., a temperature interval that is likely to induce spoilage.

[0029] A first risk coefficient for spoilage is determined for each important commodity type. This first risk coefficient is the ratio of the number of recurring occurrences of a single important commodity type to the sum of the number of recurring occurrences of all important commodity types, macroscopically reflecting the overall probability of spoilage for a single important commodity type. A second risk coefficient for spoilage caused by each important temperature range corresponding to each important commodity type is then determined. This second risk coefficient is the ratio of the number of occurrences of a single important temperature range to the sum of the number of occurrences of all important temperature ranges, characterizing the probability of spoilage when the temperature of the zone containing the important commodity type falls within the corresponding important temperature range. For example, there are three important commodity types: A, B, and C. Commodity type A appears 65 times, commodity type B appears 25 times, and commodity type C appears 10 times. Therefore, the first risk coefficient for commodity type A is 65 / (65+25+10) = 0.65. Furthermore, commodity type A corresponds to important temperature ranges A1, A2, and A3; commodity type B corresponds to important temperature ranges B1 and B2; and commodity type C corresponds to important temperature ranges C1 and C2. Specifically, important temperature range A1 appears 25 times, important temperature range A2 appears 35 times, and important temperature range A3 appears 40 times. Therefore, the second risk coefficient for important temperature range A1 is 25 / (25+35+40) = 0.25. (See [link to relevant documentation] for details.) Figure 2 .

[0030] Further, based on the actual temperature of the zone, each actual product type, and the first and second risk coefficients, the spoilage risk value of the goods in that zone is determined. Specifically, if a single actual product type is an important product type, then that actual product type is designated as a target product type; at least one target product type exists. When the actual temperature is included within the important temperature range corresponding to the target product type, the important temperature range containing the actual temperature is designated as the target temperature range. Next, the first risk coefficient of the single target product type is multiplied by the second risk coefficient of the corresponding target temperature range to obtain the coefficient product corresponding to that target product type. This coefficient product represents the probability of spoilage of the target product type within the zone at the current actual temperature. Finally, the coefficient products corresponding to each target product type are summed to obtain the spoilage risk value of the goods in that zone, representing the overall probability of spoilage of the goods within that zone at the actual temperature.

[0031] Furthermore, to obtain the risk value of spoilage spread in a single zone, one feasible method is to compare the product of each coefficient with a preset product threshold. If the product of the coefficients exceeds the product threshold, it indicates that the corresponding target product type in the zone is more likely to rot under the actual temperature, and the target product type is then identified as a potential spoilage type. Next, determine the overall impact coefficient of the remaining goods when a potentially rotten type of goods in a zone rots. The overall impact coefficient should not be less than 1. The larger the overall impact coefficient, the greater the likelihood that the rotten type of goods will breed a large number of microorganisms, thus causing other goods in the zone to rot. One feasible method is to determine the overall impact coefficient corresponding to the potential rotten type through a preset impact coefficient matching table. The impact coefficient matching table includes different types of goods in a single zone and their corresponding impact coefficients. For example, the impact coefficient matching table includes strawberries in the refrigerated zone, whose rot spreads faster and has a greater impact on other goods in the zone, with a corresponding impact coefficient of 1.5; apples in the refrigerated zone, with a corresponding impact coefficient of 1.3; and carrots in the refrigerated zone, with a corresponding impact coefficient of 1.1. It should be noted that the degree of impact on other goods varies depending on the type of rotten goods in the zone.

[0032] If the number of potential decay types in a zone is single, then the product of the coefficients corresponding to the potential decay types is directly multiplied by the overall influence coefficient to obtain the first multiplication result, which represents the extent of decay spread when goods decay in the zone at the actual temperature. Then, based on the first multiplication result, the decay spread risk value is determined. The decay spread risk value is not less than 1. The larger the first multiplication result, the larger the decay spread risk value.

[0033] If there are multiple potential decay types in a partition, the product of the coefficients corresponding to the same potential decay type is multiplied by the overall impact coefficient to obtain a second multiplication result. Then, the largest second multiplication result is selected from all the second multiplication results, and the decay spread risk value of the partition is determined based on the largest second multiplication result. It should be noted that the decay spread risk value is determined in both cases as follows: a preset mapping table is used to determine the corresponding decay spread risk value. The mapping table includes the mapping relationship between different multiplication result ranges and their corresponding decay spread risk values. For example, a multiplication result range of 0-0.2 corresponds to a decay spread risk value of 1; a multiplication result range of 0.2-0.4 corresponds to a decay spread risk value of 1.1; a multiplication result range of 0.4-0.6 corresponds to a decay spread risk value of 1.2, and so on. If the first multiplication result or the largest second multiplication result is within the range of 0.2-0.4, then the decay spread risk value of the partition is 1.1.

[0034] Furthermore, to obtain the value of the goods stored in the partition, one feasible method is as follows: using the aforementioned cold chain management system, obtain the unit price of each item stored in the partition, determine the average unit price of each item, and map the average unit price to the corresponding value using a preset value mapping table. The value mapping table includes different unit price ranges and their corresponding value values. For example, a unit price range of ≤1 yuan corresponds to a value of 1; a unit price range of 1-10 yuan corresponds to a value of 1.25; a unit price range of 10-20 yuan corresponds to a value of 1.5; a unit price range of 20-30 yuan corresponds to a value of 1.75, and so on. If the average unit price of the goods in the partition is within 20-30 yuan, then the corresponding value is 1.75.

[0035] S103: Based on the risk value of spoilage spread, value, and risk value of goods spoilage, determine the risk value of cargo damage for each zone, and based on the risk value of cargo damage for each zone, determine the overall risk value of cargo damage for the target refrigerated truck.

[0036] Specifically, the spoilage spread risk value, value, and goods spoilage risk value of the same partition are multiplied to obtain the goods damage risk value of that partition, representing the extent of goods loss in that partition. Finally, the goods damage risk values ​​of all partitions are summed to obtain the overall goods damage risk value of the target refrigerated truck, representing the overall extent of goods loss in the target refrigerated truck. In other embodiments, the spoilage weight of each partition can be determined, and then the goods damage risk values ​​of each partition can be summed according to the spoilage weight to obtain the overall goods damage risk value. The spoilage weight of a single partition is determined as follows: the first risk coefficients of each important goods type among all actual goods types stored in the partition are summed to obtain the summation result corresponding to that partition. Then, the ratio of the summation result to the summation results of all partitions is determined as the spoilage weight of the single partition. The larger the spoilage weight, the greater the overall risk of goods spoilage in that partition.

[0037] In other embodiments, if the risk value of spoilage of goods in a zone exceeds a preset threshold, it indicates that the actual temperature within the zone is unsuitable. In this case, a temperature adjustment command is sent to the terminal of the target refrigerated truck owner. Simultaneously, the adjusted target temperature of the zone is verified. Specifically, if the important temperature range corresponding to the target goods type contains the target temperature, this important temperature range is designated as the key temperature range. The product of the first risk coefficient of the target goods type and the second risk coefficient of the key temperature range is calculated and summed to obtain a comprehensive result. This comprehensive result represents the probability of spoilage of goods within the zone at the target temperature. If the comprehensive result does not exceed the preset threshold, it indicates that the temperature adjustment is reasonable, and the target temperature is determined to be suitable. The terminal can be a smartphone.

[0038] In another embodiment, if the spoilage risk value of goods in each zone does not exceed a preset threshold, then a first inspection order is determined for each zone. The higher the spoilage risk value, the earlier the corresponding first inspection order, and the more preferentially the goods are inspected through the monitoring video of the corresponding zone. Furthermore, for a single zone, a second inspection order is determined based on the product of various coefficients for the corresponding target goods type. The higher the coefficient product, the earlier the corresponding second inspection order, and the more preferentially the corresponding goods are inspected through the monitoring video. Finally, the first and second inspection orders corresponding to each zone are sent to the terminal of the target refrigerated truck owner, thereby reminding the owner to check the monitoring video in a timely manner to avoid spoilage going undetected.

[0039] S104: Obtain the actual remaining power of the target refrigerated truck and the estimated departure time for delivery. Based on the overall risk value of cargo damage, the actual remaining power, and the estimated departure time for delivery, determine the overall power urgency index of the target refrigerated truck.

[0040] Specifically, the actual remaining battery power of the target refrigerated truck is obtained through its BMS (Battery Management System). Simultaneously, the estimated departure time for delivery is obtained through the aforementioned cold chain management system. In other embodiments, the estimated departure time sent by the vehicle owner's terminal can also be received. Then, based on the overall cargo damage risk value, the actual remaining battery power, and the estimated departure time, the overall power urgency index of the target refrigerated truck is determined. The higher the overall power urgency index, the higher the charging power requirement of the target refrigerated truck. One feasible method for determining this index is as follows: The aforementioned cold chain management system obtains the delivery route and required delivery time (the time required for the goods in the target refrigerated truck to be delivered) of the target refrigerated truck. The estimated delivery time is obtained by subtracting the estimated departure time from the required delivery time. Using a pre-set electronic map tool, the distance of the delivery route is determined, and the distance is divided by the estimated delivery time to obtain the average speed of the target refrigerated truck during delivery. Then, based on pre-set refrigerated truck energy consumption records, the historical driving energy consumption at the average speed during the target refrigerated truck's historical deliveries is obtained. The frequency of occurrence of a single historical driving energy consumption value is counted, and the most frequently occurring historical driving energy consumption value is multiplied by the distance to obtain the estimated driving energy consumption of the target refrigerated truck. The refrigerated truck energy consumption records include, but are not limited to, the average speed of the target refrigerated truck for each delivery, the historical ambient temperature during delivery, and the corresponding historical driving energy consumption and historical refrigeration energy consumption. Furthermore, based on the preset meteorological tools, the ambient temperature during the delivery period from the estimated departure time to the required delivery time is obtained. Then, based on the above refrigerated vehicle energy consumption records, the historical refrigeration energy consumption of the refrigerated vehicle when the temperature is the ambient temperature during the historical delivery process is obtained. The frequency of occurrence of a single historical refrigeration energy consumption is counted among all historical refrigeration energy consumptions. The historical refrigeration energy consumption with the highest frequency is multiplied by the distance to obtain the estimated refrigeration power consumption of the target refrigerated vehicle.

[0041] Furthermore, the estimated driving power consumption and estimated refrigeration power consumption are summed to obtain the minimum safe delivery power of the target refrigerated truck, that is, the minimum power required for the target refrigerated truck to deliver the goods on time. Next, the estimated departure time is subtracted from the current time to obtain the estimated charging time. Then, the actual current and voltage data are collected by the electrical sampling unit (current / voltage sensor) inside the target charging pile to determine the current actual output power. The estimated charging time is multiplied by the actual output power to obtain the estimated charging amount. The estimated charging amount is added to the actual remaining power to obtain the estimated power of the target refrigerated truck when it departs for delivery.

[0042] The difference between the minimum safe delivery capacity and the expected delivery capacity is calculated. A larger difference means that charging will continue at the current actual output power, reducing the likelihood that the remaining battery power of the refrigerated truck at the start of delivery will be sufficient to deliver the goods. Consequently, the demand for charging power is more urgent, and the first power urgency index is higher. Specifically, a preset urgency index matching table is used to determine the first power urgency index based on this difference. This table includes different difference ranges and their corresponding power urgency indices, all set based on human experience. The power urgency index corresponding to the range of differences is determined as the first power urgency index.

[0043] Furthermore, the overall risk value of cargo damage is directly determined as the second power urgency index of the target refrigerated truck. The higher the overall risk value of cargo damage, the greater the risk and extent of damage to the goods stored in the target refrigerated truck during charging. To reduce the risk or extent of cargo damage, more electricity is needed for zoned refrigeration, and correspondingly, the urgency of the target refrigerated truck's charging power demand is also higher, thus the second power urgency index is higher. Finally, the first and second power urgency indices are summed to obtain the overall power urgency index of the target refrigerated truck, which characterizes the urgency of the target refrigerated truck's charging power demand.

[0044] S105: Obtain the power allocation requirements of other charging piles within the charging area to which the target charging pile belongs through the consensus network, and determine the initial power allocation of the target charging pile based on the overall power urgency index and the power allocation requirements of each pile.

[0045] Specifically, the target charging pile connects to the consensus network through its built-in edge computing module. This consensus network is a distributed communication network composed of all charging piles within the charging area to which the target charging pile belongs. Each charging pile node has a pre-set periodic data reporting mechanism. The target charging pile collects the power allocation requirements of all other charging piles in operation (including those waiting to be charged and those charging) within its charging area through the real-time data interaction channel of the consensus network. The power allocation requirements include at least the overall power urgency index of the vehicles waiting to be charged for each other charging pile, the current acceptable maximum charging power reported by the vehicle's BMS system, the vehicle's remaining battery power, and the estimated charging completion time. The power allocation requirements of each other charging pile are calculated and generated by its own edge computing module and uploaded to the consensus network in real time for distributed storage. During the collection process, the target charging pile verifies the completeness and validity of the power allocation requirements of other charging piles through the verification mechanism of the consensus network, and removes invalid data that is missing or abnormal.

[0046] Subsequently, the edge computing module of the target charging pile normalizes the collected effective power allocation demands, and quantifies and ranks its own overall power urgency index in a unified manner with the overall power urgency indices of other charging piles. Simultaneously, it extracts key parameters such as the maximum acceptable charging power for vehicles waiting to be charged and the estimated charging completion time for each charging pile to construct a power allocation evaluation model. This model aims to optimize the overall charging efficiency within the charging area (i.e., the shortest total charging completion time) and prioritize charging high-urgency vehicles. It sets weight coefficients for the overall power urgency index, the acceptable maximum charging power constraint threshold, and the estimated charging completion time, with the overall power urgency index having a higher weight than other parameters. This model prioritizes the power demands of the target charging pile itself and other charging piles. The initial power allocation for the target charging pile is calculated based on the ranking and the real-time total power limit of the charging area (determined by the grid load monitoring data synchronized by the consensus network). The specific calculation process is as follows: First, according to the overall power urgency index ranking result, the charging piles corresponding to high urgency are given priority to be allocated a power limit not exceeding the maximum charging power that the vehicle to be charged can accept. After the power allocation of the high urgency charging piles is completed, if there is still a remaining total power limit in the charging area, the power is then allocated to the low urgency charging piles in order of priority. The initial power allocation for the target charging pile is the power value determined under this allocation logic, combined with its own overall power urgency index and the maximum charging power that the corresponding refrigerated vehicle to be charged can accept, and the initial power allocation does not exceed the hardware rated output power limit of the target charging pile itself.

[0047] S106: Upload the initial allocated power to the consensus network, determine the final allocated power of the target charging pile based on the preset consensus algorithm, and adjust the power of the target charging pile according to the final allocated power.

[0048] Specifically, the edge computing module of the target charging pile uploads the determined initial power allocation to the consensus network in a standardized data format. Each charging pile node in this consensus network has the same pre-set consensus algorithm (such as the improved PBFT consensus algorithm) and consensus verification rules. Upon uploading the initial power allocation, the consensus verification process of the consensus network is triggered. The accounting nodes in the consensus network (selected according to a pre-set polling mechanism or node credit rating) perform a preliminary verification of the initial power allocation data uploaded by the target charging pile. The verification includes data format standardization, whether the initial power allocation exceeds the rated output power limit of the target charging pile's hardware, and whether it exceeds the maximum acceptable charging power for the refrigerated vehicle being charged. If the verification fails, a verification failure message is returned to the target charging pile, requesting resubmission. If the verification passes, the initial power allocation data is synchronized to all other distributed nodes in the consensus network. After receiving the data, each distributed node combines its own uploaded initial power allocation with the initial power allocation collected from other distributed nodes to calculate the sum of the initial power allocation of all nodes. It then determines whether this sum exceeds the real-time total power limit of the charging area, and simultaneously verifies the data based on pre-set consensus verification rules (including a power allocation fairness threshold). The system verifies the rationality of the initial power allocation for the target charging pile by checking the power urgency index of the target charging pile against its initial power allocation ratio, and whether there is any preemption of necessary charging power from low-urgency nodes. If the total power exceeds the upper limit or the initial power allocation of individual distributed nodes is unreasonable, each node interacts with the consensus network to make correction suggestions. The ledger node summarizes all correction suggestions and generates a power allocation adjustment plan, which is then synchronized to all nodes for secondary verification until all nodes reach a consensus on the power allocation for each node, including the target charging pile. When more than a preset proportion (e.g., more than 2 / 3) of the distributed nodes in the consensus network reach a consensus on the power allocation for the target charging pile, the power value that has reached a consensus is determined as the final power allocation for the target charging pile. The ledger node writes the final power allocation and consensus result into the distributed ledger and synchronizes it to the target charging pile. After receiving the final power allocation, the edge computing module of the target charging pile establishes communication with its own power control unit through the CAN bus and sends a power adjustment command to the power control unit to achieve a smooth adjustment of the output power to the final power allocation.

[0049] The implementation principle of the distributed charging pile collaborative control method in this application embodiment is as follows: After obtaining the actual temperature of the zone and the actual type of goods stored therein, the risk of goods spoilage in the zone is analyzed based on the temperature and type of goods stored therein. This allows for a relatively accurate determination of the spoilage risk value for each zone. Next, by combining the risk value of spoilage spread and the value of the goods within the zone, the spread of spoilage is analyzed once it occurs. This further analyzes the extent of loss of goods within the zone, thus reasonably determining the damage risk value for each zone and summarizing it to obtain the overall damage risk value for the target refrigerated truck. This achieves precise quantification of the safety priority of the goods in the target refrigerated truck, providing a core demand basis for power allocation. Subsequently, by combining the actual remaining power of the target refrigerated truck and the estimated departure time for delivery, the damage safety requirements, power replenishment requirements, and time urgency are organically integrated to determine an overall power urgency index that reflects the charging urgency of the target refrigerated truck. This avoids priority misjudgment caused by single-dimensional demand assessment and ensures accurate matching of power allocation with the actual charging needs of the target refrigerated truck. Furthermore, based on the overall power urgency index and combined with the demand differences of other charging piles, the initial power allocation of the target charging pile is determined more accurately. Finally, based on the uploaded initial power allocation, the consensus network can determine the final power allocation of the target charging pile more accurately, thereby improving the accuracy of power allocation for charging piles.

[0050] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0051] Please see Figure 3 This is a schematic diagram of the distributed charging pile collaborative control system provided in this application embodiment. This distributed charging pile collaborative control system can be implemented as all or part of a system through software, hardware, or a combination of both. The system includes an information acquisition module 11, a risk determination module 12, a cargo damage determination module 13, an index determination module 14, a power determination module 15, and a power adjustment module 16.

[0052] The information acquisition module 11 is used to acquire the actual temperature of each compartment in the target refrigerated truck to be charged at the target charging pile and at least one actual type of goods stored therein. The risk determination module 12 is used to determine the spoilage risk value of goods in each zone based on the actual temperature and the actual types of goods, and to obtain the spoilage spread risk value of each zone and the value of the goods stored in each zone. The cargo damage assessment module 13 is used to determine the cargo damage risk value of each zone based on the risk value of spoilage spread, value, and cargo spoilage risk value, and to determine the overall cargo damage risk value of the target refrigerated truck based on the cargo damage risk value of each zone. The index determination module 14 is used to obtain the actual remaining power of the target refrigerated truck and the estimated departure time of delivery. Based on the overall risk value of cargo damage, the actual remaining power and the estimated departure time of delivery, the overall power urgency index of the target refrigerated truck is determined. The power determination module 15 is used to obtain the power allocation requirements of other charging piles in the charging area to which the target charging pile belongs through a consensus network, and determine the initial allocation power of the target charging pile based on the overall power urgency index and the power allocation requirements of each charging pile. The power adjustment module 16 is used to upload the initial allocated power to the consensus network, determine the final allocated power of the target charging pile based on the preset consensus algorithm, and adjust the power of the target charging pile according to the final allocated power.

[0053] Optional, the risk assessment module 12 is specifically used for: Obtain multiple historical cargo types from which goods have rotted in refrigerated trucks, and select at least one important cargo type from these historical cargo types; The system obtains multiple historical temperatures at which important types of goods in historical refrigerated trucks rotted. It then performs cluster analysis on these historical temperatures to obtain multiple temperature ranges and selects at least one important temperature range from these ranges. Determine the first risk factor for spoilage of goods of important product types, and determine the second risk factor for spoilage of goods in each important temperature range; The risk value of goods spoilage in each zone is determined based on the actual temperature, the actual types of goods, the first risk coefficient, and the second risk coefficients.

[0054] Optional, the risk assessment module 12 is specifically used for: If the actual product type is an important product type, then the actual product type is determined as the target product type. If the actual temperature is included in the important temperature range corresponding to the target product type, then the important temperature range including the actual temperature is determined as the target temperature range. Multiply the first risk coefficient of at least one target product type by the second risk coefficient of the target temperature range to obtain at least one coefficient product; The risk value of goods spoilage in each zone is determined by multiplying all the coefficients.

[0055] Optional, the risk assessment module 12 is specifically used for: If the product of the coefficients is greater than the preset product threshold, the corresponding target product type will be identified as a potential spoilage type. When goods with potential spoilage types within a zone spoil, the overall impact coefficient on the remaining goods within that zone is determined, and the overall impact coefficient is not less than 1. When there is only one potential decay type, the product of the coefficients corresponding to the potential decay type is multiplied by the overall influence coefficient to obtain the first multiplication result. Based on the first multiplication result, the decay spread risk value of the partition is determined. The larger the first multiplication result, the larger the decay spread risk value. The decay spread risk value is not less than 1. When there are multiple potential decay types, the product of the coefficients corresponding to the same potential decay type is multiplied by the overall influence coefficient to obtain the second multiplication result; The risk value of decay spread in a zone is determined based on the largest second multiplication result among all the second multiplication results.

[0056] Optional, the cargo damage assessment module 13 is specifically used for: The summation of the first risk coefficients for each important product type in all actual product types is used to obtain the summation result for each region. The ratio of the summation result to the summation result of all partitions is determined as the decay weight of the partition. Multiply the damage risk value and the spoilage weight of each zone and sum them to obtain the overall damage risk value of the target refrigerated truck.

[0057] Optionally, the index determination module 14 is specifically used for: Obtain the delivery route and required delivery time for the target refrigerated truck; Based on the actual remaining power, the estimated departure time of delivery, the required delivery time and delivery route, determine the first power urgency index of the target refrigerated truck, and determine the second power urgency index of the target refrigerated truck based on the overall risk value of cargo damage. The overall power urgency index of the target refrigerated truck is obtained by summing the first power urgency index and the second power urgency index.

[0058] Optionally, the index determination module 14 is specifically used for: Subtract the estimated departure time from the required delivery time to obtain the estimated delivery time, and determine the average speed of the target refrigerated truck based on the delivery route and the estimated delivery time; Based on average vehicle speed and estimated delivery time, determine the estimated driving power consumption of the target refrigerated vehicle, and based on the ambient temperature during delivery, determine the estimated refrigeration power consumption of the target refrigerated vehicle. Based on the estimated power consumption of the drive and the estimated power consumption of the refrigeration, determine the minimum safe delivery power of the target refrigerated truck, and subtract the current time from the estimated departure time to obtain the estimated charging time; Multiply the estimated charging time by the actual output power of the target charging pile to obtain the estimated charging amount, and sum the estimated charging amount and the actual remaining power to obtain the estimated power of the target refrigerated truck. The first power urgency index of the target refrigerated truck is determined by the difference between the minimum safe delivery power and the expected power. The larger the difference, the higher the first power urgency index.

[0059] It should be noted that the distributed charging pile collaborative control system provided in the above embodiments is only illustrated by the division of the above functional modules when executing the distributed charging pile collaborative control method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the distributed charging pile collaborative control system and the distributed charging pile collaborative control method embodiment provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiment, which will not be repeated here.

[0060] This application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements a distributed charging pile collaborative control method as described in the above embodiments.

[0061] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0062] The distributed charging pile collaborative control method of the above embodiment is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the above method.

[0063] This application also discloses an electronic device in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, it implements the above-mentioned distributed charging pile collaborative control method.

[0064] The electronic device can be a desktop computer, a laptop computer, or a cloud server, and includes, but is not limited to, a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and buses.

[0065] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0066] The memory can be an internal storage unit of an electronic device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the electronic device. Furthermore, the memory can be a combination of an internal storage unit and an external storage device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0067] In this electronic device, the distributed charging pile collaborative control method of the above embodiment is stored in the memory of the electronic device and loaded and executed on the processor of the electronic device for convenient use.

[0068] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A distributed charging pile collaborative control method, characterized in that, The method includes: Obtain the actual temperature of each compartment in the target refrigerated truck to be charged at the target charging station, as well as at least one actual type of goods stored therein; Based on the actual temperature and each of the actual goods types, determine the goods spoilage risk value of the zone, obtain the spoilage spread risk value of the zone, and obtain the value of the goods stored in the zone. Based on the risk value of spoilage spread, the value, and the risk value of spoilage of the goods, the risk value of cargo damage for each zone is determined, and based on the risk value of cargo damage for each zone, the overall risk value of cargo damage for the target refrigerated truck is determined. Obtain the actual remaining power of the target refrigerated truck and the estimated departure time for delivery. Based on the overall risk value of cargo damage, the actual remaining power, and the estimated departure time for delivery, determine the overall power urgency index of the target refrigerated truck. The power allocation requirements of other charging piles within the charging area to which the target charging pile belongs are obtained through a consensus network. Based on the overall power urgency index and each of the power allocation requirements, the initial power allocation for the target charging pile is determined. The initial allocated power is uploaded to the consensus network. Based on a preset consensus algorithm, the final allocated power of the target charging pile is determined, and the power of the target charging pile is adjusted according to the final allocated power.

2. The distributed charging pile collaborative control method according to claim 1, characterized in that, The step of determining the spoilage risk value of the goods in the zone based on the actual temperature and the actual types of goods specifically includes: Obtain multiple historical cargo types from which goods have rotted in refrigerated trucks, and select at least one important cargo type from these multiple historical cargo types; The system obtains multiple historical temperatures at which goods of the important cargo types in the historical refrigerated trucks rotted, performs cluster analysis on the multiple historical temperatures to obtain multiple temperature ranges, and selects at least one important temperature range from the multiple temperature ranges. Determine a first risk factor for the spoilage of goods of the aforementioned important goods types, and determine a second risk factor for the spoilage of goods within each of the aforementioned important temperature ranges; The risk value of goods spoilage in the zone is determined based on the actual temperature, the actual types of goods, the first risk coefficient, and the second risk coefficients.

3. The distributed charging pile collaborative control method according to claim 2, characterized in that, The step of determining the spoilage risk value of the goods in the zone based on the actual temperature, the actual types of goods, the first risk coefficient, and the second risk coefficients specifically includes: If the actual product type is the important product type, then the actual product type is determined as the target product type. If the actual temperature is included in the important temperature range corresponding to the target product type, then the important temperature range including the actual temperature is determined as the target temperature range. Multiply at least one first risk coefficient of the target product type by a second risk coefficient of the target temperature range to obtain at least one coefficient product; The risk value of goods spoilage in the zone is determined by multiplying all the coefficients.

4. The distributed charging pile collaborative control method according to claim 3, characterized in that, The process of obtaining the decay spread risk value of the partition specifically includes: If the product of the coefficients is greater than a preset product threshold, the corresponding target product type is determined to be a potential spoilage type. When goods of the potential type of spoilage in the partition are found to be spoiled, the overall impact coefficient on the remaining goods in the partition is not less than 1. When the number of potential decay types is single, the product of the coefficients corresponding to the potential decay types is multiplied by the overall influence coefficient to obtain a first multiplication result. Based on the first multiplication result, the decay spread risk value of the partition is determined. The larger the first multiplication result, the larger the decay spread risk value. The decay spread risk value is not less than 1. When there are multiple potential decay types, the product of the coefficients corresponding to the same potential decay type and the overall influence coefficient are multiplied together to obtain the second multiplication result; The rot spread risk value of the partition is determined based on the largest second multiplication result among all the second multiplication results.

5. The distributed charging pile collaborative control method according to claim 2, characterized in that, The step of determining the overall cargo damage risk value of the target refrigerated truck based on the cargo damage risk value of each of the aforementioned zones specifically includes: The summation result of the partition is obtained by summing the first risk coefficients of each important product type among all the actual product types. The ratio of the summation result to the summation result of all partitions is determined as the decay weight of the partition. The overall risk value of cargo damage for the target refrigerated truck is obtained by multiplying the cargo damage risk value and the spoilage weight of each partition and summing them.

6. The distributed charging pile collaborative control method according to claim 1, characterized in that, The determination of the overall power urgency index of the target refrigerated truck based on the overall cargo damage risk value, the actual remaining power, and the estimated delivery departure time specifically includes: Obtain the delivery route and required delivery time of the target refrigerated truck; Based on the actual remaining power, the estimated delivery departure time, the required delivery time, and the delivery route, a first power urgency index for the target refrigerated truck is determined, and a second power urgency index for the target refrigerated truck is determined based on the overall risk value of cargo damage. The first power urgency index and the second power urgency index are summed to obtain the overall power urgency index of the target refrigerated truck.

7. The distributed charging pile collaborative control method according to claim 6, characterized in that, The determination of the first power urgency index of the target refrigerated truck based on the actual remaining power, the estimated delivery departure time, the required delivery time, and the delivery route specifically includes: Subtract the estimated delivery departure time from the required delivery time to obtain the estimated delivery time, and determine the average speed of the target refrigerated truck based on the delivery route and the estimated delivery time; Based on the average vehicle speed and the estimated delivery time, the estimated driving power consumption of the target refrigerated vehicle is determined, and based on the ambient temperature during the delivery period, the estimated refrigeration power consumption of the target refrigerated vehicle is determined. Based on the estimated driving power consumption and the estimated refrigeration power consumption, the minimum safe delivery power of the target refrigerated truck is determined, and the estimated charging time is obtained by subtracting the current time from the estimated departure time. Multiply the estimated charging time by the actual current output power of the target charging pile to obtain the estimated charging amount, and sum the estimated charging amount and the actual remaining power to obtain the estimated power level of the target refrigerated truck; The first power urgency index of the target refrigerated truck is determined based on the difference between the minimum safe delivery power and the expected power. The larger the difference, the higher the first power urgency index.

8. A distributed charging pile collaborative control system, characterized in that, include: The information acquisition module (11) is used to acquire the actual temperature of each compartment in the target refrigerated truck to be charged on the target charging pile and at least one actual type of goods stored therein. The risk determination module (12) is used to determine the spoilage risk value of the goods in the partition based on the actual temperature and each of the actual goods types, and to obtain the spoilage spread risk value of the partition and the value of the goods stored in the partition. The cargo damage assessment module (13) is used to determine the cargo damage risk value of the partition based on the risk value of the spoilage spread, the value, and the risk value of the spoilage of the goods, and to determine the overall cargo damage risk value of the target refrigerated truck based on the cargo damage risk value of each partition. The index determination module (14) is used to obtain the actual remaining power of the target refrigerated truck and the estimated departure time of delivery, and to determine the overall power urgency index of the target refrigerated truck based on the overall risk value of cargo damage, the actual remaining power and the estimated departure time of delivery. The power determination module (15) is used to obtain the power allocation requirements of other charging piles in the charging area to which the target charging pile belongs through a consensus network, and determine the initial allocation power of the target charging pile based on the overall power urgency index and each of the power allocation requirements. The power adjustment module (16) is used to upload the initial allocated power to the consensus network, determine the final allocated power of the target charging pile based on a preset consensus algorithm, and adjust the power of the target charging pile according to the final allocated power.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the method of any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it implements the method of any one of claims 1-7.

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