Detailed Description
The embodiment of the application provides a commodity logistics tracking method based on a blockchain and related equipment, which can solve the problem that the blockchain cannot verify the authenticity of input data in a commodity tracing system.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments.
Referring to fig. 1, a flowchart of a method for tracking commodity circulation based on blockchain according to an embodiment of the present application may specifically include:
S110-S130。
s110, logistics transportation information of the cold chain commodities is obtained, wherein the logistics transportation information comprises a transportation time interval, a transportation path and transportation vehicles associated with the cold chain commodities.
By way of example, the logistical transportation information includes the transportation time interval and transportation path of the cold chain commodity and the basic information (such as vehicle volume, license plate number, etc.) of the transportation vehicle with which the commodity is associated. For example, a fresh food distribution company is transporting frozen seafood, and the logistics information comprises information of the starting time, the ending time, the transportation route and the cold chain transportation vehicle of the transportation of the seafood.
For example, during transportation, the internet of things device may collect information such as the number of cold chain commodities, the package size, and the vehicle volume in real time. The sensor directly uploads the data, so that errors of manual input are reduced. The accuracy of the data can be ensured through the automatic acquisition of the sensor, and the data can be immediately chained up after each acquisition, so that the transparency is ensured.
And S120, inquiring commodity information of other cold chain commodities matched with the logistics transportation information, wherein the commodity information comprises the types, the quantity and the package size data of the commodities. Matching according to commodity logistics information on the same cold chain transport vehicle, for example, frozen meat and frozen vegetables are also transported on the same vehicle. The types, package specifications and quantities of the commodities can be collected through a database or a sensor and matched with logistics information. Assuming that the frozen seafood and the frozen meat and vegetables share one refrigerated vehicle, the number and packaging size of each type of commodity (such as 0.5 cubic meter for seafood, 0.6 cubic meter for meat, and 0.4 cubic meter for vegetables) are recorded according to the system data.
Exemplary commodity information includes the type, quantity, and package size data of the cold chain commodity.
And S130, generating a logistics transportation data authenticity alarm and recording the logistics transportation data authenticity alarm to a blockchain system under the condition that the total volume calculated based on the quantity of the commodities and the package size data is larger than the volume of the transportation vehicle associated with the cold chain commodity.
Illustratively, the total volume of all cold chain goods loaded on the vehicle is calculated from the acquired goods quantity and package size data. Comparing the calculated total volume of the goods with the maximum volume of the transport vehicle, and checking whether the loaded cold chain goods exceeds the carrying capacity of the vehicle. Assuming that the maximum volume of the cold chain transporter is 70 cubic meters and the calculated total volume of goods is 73 cubic meters, the volume of the vehicle is exceeded. At this time, the system may determine that there is an abnormality in these logistics data, which may be a case where the number of goods is tampered with, input errors, or there is an overload. And under the condition that the total volume of the commodity is larger than the volume of the vehicle, generating an authenticity alarm of the logistics transportation data, and prompting that the unrealistic logistics information possibly exists. The warning information can be 'overload of cold chain transport vehicles, total volume of goods exceeds the maximum volume of vehicles, check quantity of goods and package size', and send to related management staff through the system to further check the root cause of the problem. The alarm information is stored in the blockchain system, so that the tamper-proof alarm record can be ensured to provide reference for future audit or disputes. Such alert records help to trace back and confirm whether a problem is caused by data entry errors or tampering actions. For example, a record is generated on the blockchain containing detailed information such as warning time, vehicle number, commodity type, volume calculation data, etc. Because of the transparency of the blockchain, the interested parties can view and track these records, ensuring timely resolution of the problem.
For example, the blockchain intelligence contract may automatically perform calculations and comparisons of commodity volumes according to set rules. Once the total volume of the commodity exceeds the volume of the vehicle, the intelligent contract automatically generates alarm information and records related data in the blockchain, so that timeliness and tamper resistance of the alarm are ensured. The data verification is automatically processed through intelligent contracts, so that manual intervention is reduced, and transparency and credibility of each step are ensured.
For example, a standardized inventory information database may be created containing package size data for each cold chain inventory for logistics system calls. This can prevent a size error at the time of data input. The database may also be combined with a blockchain to ensure that each package size is well-known. The standardized commodity information reduces the size error caused by human input, and the data is stored through the blockchain, so that the transparency and traceability of the information are ensured.
In summary, according to the blockchain-based commodity logistics tracking method provided by the embodiment of the application, logistics transportation information of cold chain commodities is obtained, wherein the logistics transportation information comprises a transportation time interval, a transportation path and transportation vehicles associated with the cold chain commodities, commodity information of other cold chain commodities matched with the logistics transportation information is queried, the commodity information comprises types, numbers and package size data of the commodities, and when the total volume calculated based on the numbers and the package size data of the commodities is larger than the volume of the transportation vehicles associated with the cold chain commodities, a logistics transportation data authenticity alarm is generated and recorded to a blockchain system. Therefore, through the rationality verification of the commodity quantity, the package size and the vehicle volume, the possible problems of data tampering or inaccurate input can be effectively identified. For example, if the total volume of the goods of a cold chain transport vehicle is found to exceed its volume, it may mean that the input data is not authentic, helping to find problems ahead of time. Once a problem is detected, a system generated alert is recorded on the blockchain, ensuring that the alert can be traced back and confirmed in subsequent reviews. This transparent record provides additional trust assurance that the data tampering behavior can be tracked. Through the sensor and the intelligent contract, the system can realize a full-automatic checking process, reduce errors caused by manual intervention and ensure data consistency. Each operation is transparently recorded on the blockchain, providing proof of tamper-proof verification.
In some examples, further comprising:
And under the condition that the receiving end receives the quality complaint information of the cold chain commodity or the other cold chain commodities, retrieving the logistic transportation data authenticity alarm record for the receiving end.
For example, the receiving end may submit complaints about the quality of the cold chain commodity through the system, and the complaint information may include whether the commodity is in an optimal eating state, whether there is damage or deterioration. After receiving the complaint message from the receiving end, the system compares the complaint message with the logistics transportation data stored in the blockchain previously to determine whether abnormal conditions (such as improper temperature, transportation delay and the like) related to logistics transportation exist. This process ensures that the system is informed when tracing merchandise problems.
Illustratively, the system retrieves logistics transportation data associated with the batch of cold chain merchandise, as well as a previously generated authenticity alert record, based on the received quality complaint message. When a quality complaint occurs, the system can call all logistics information alarm records related to the commodity batch. For example, if an alert that the volume of merchandise exceeds the volume of the vehicle has been previously triggered, such an alert record may provide a cue for potential problems in the transportation of the merchandise. Such a retrieval mechanism may help to quickly locate whether the merchandise has experienced a problem during transportation that may affect quality (e.g., overload or other anomalies). The system further judges whether the problem is derived from the logistic transportation process by analyzing the association between the warning information in the transportation process and the complaint content of the receiving end. For example, if the alarm records indicate that the temperature during the transportation of the commodity exceeds the standard, or that the commodity is excessively loaded, resulting in reduced sealability, these problems may be associated with reduced commodity quality. By combining the logistic data authenticity warning with the complaint content, the system can analyze whether the commodity quality problem is caused by improper operation in the transportation process. The alarm records provide evidence of non-tampering and can be used as a basis for handling complaints, compensating or taking corrective action. Based on the analysis results, the system may provide feedback to the receiving end and provide an operable solution to the supply chain manager. For example, if alert information does exist, the responsible party can be determined according to the transportation record and corresponding compensation measures can be taken, and if no abnormality is found, it can be judged that the problem is likely to originate from other links (such as quality problems of warehouse or commodity itself). The receiving end and supply chain parties can clearly see the course of the transportation at each stage through transparent records on the blockchain. If the alert information is associated with a quality problem, the system may push the supply chain manager to take action to ameliorate the problem or to make reimbursement for the receiving end. Therefore, by combining the quality complaint information of the receiving end with the logistics transportation alarm record, the system can automatically trace the problem after receiving the complaint, and quickly locate the potential quality problem source. The automatic traceability mechanism reduces the workload of manual analysis and improves the efficiency of solving the problem of the supply chain. The blockchain ensures transparency and non-tamper resistance of the alert record and the transportation data. Whenever quality problems occur, both the receiving end and the supply chain manager can fairly view relevant data in the logistics process, ensuring that the allocation of responsibility is based on real and reliable data. When the commodity quality problem is found, the system can quickly provide non-tamperable evidence (such as alarm records) to determine responsible parties, so that disputes caused by opaque logistics transportation data are reduced. The alarm information can be used as strong evidence for tracing quality problems, so that invalid complaints or improper responsibility allocation are avoided. Through the combined analysis of the complaints and the alarm records of the receiving end, the supply chain manager can continuously optimize the logistics management flow, and ensure that similar problems are avoided in the future. For example, if it is often found that some type of alert is associated with a quality issue for a particular commodity, the manager may optimize the transportation process, adjust the vehicle load, or upgrade the cold chain equipment in advance.
For example, after a restaurant receives a batch of cold chain seafood, it is found that some seafood is spoiled, and quality complaints are submitted. After the system receives complaints, the transport data and the alarm records of the batch of cold chain seafood are retrieved, and the alarm that the total volume of the commodity exceeds the volume of the vehicle is found to be triggered before, possibly because the cold chain equipment cannot keep low temperature due to overload, thereby causing the quality of the seafood to be reduced. The system feeds this alert back to the catering company and recommends the supply chain manager to make reimbursements.
In some examples, further comprising:
acquiring a first temperature of the cold chain commodity after the cold chain commodity reaches a dispatch site;
Determining a second temperature of an optimal mouthfeel of the cold chain commodity based on commodity information of the cold chain commodity, the second temperature being higher than the first temperature;
Calculating ideal distribution time of the cold chain commodity according to the first temperature, the second temperature, the current environment temperature and the type of the cold chain commodity, wherein the ideal distribution time is time when the cold chain commodity is approaching to the second temperature from the first temperature under the influence of the current environment temperature;
and carrying out dispatch of the cold chain commodity based on the ideal dispatch duration.
It will be appreciated that in general, the delivery is performed by a mobile tool such as an electric vehicle without a cold chain or thermal insulation, and the temperature of the product is easily affected by the ambient temperature during the delivery process.
Illustratively, when the cold chain product arrives at the dispensing station, the system obtains the current temperature of the product, referred to as the "first temperature", via the sensor. This is the temperature that is first obtained after the article leaves the cold chain environment. The temperature information of the cold chain commodity can be acquired in real time by a sensor or temperature monitoring equipment by the system, so that the temperature record during distribution is ensured to be accurate. This temperature data will be the basis for subsequent calculations. Based on information about the commodity (e.g., raw food or chilled beverage), the system queries the database for the optimal serving or drinking temperature of the commodity, which is referred to as the "second temperature", and is typically higher than the current first temperature. Different cold chain products have respective optimal mouth feel temperatures. For example, the optimal mouthfeel temperature of some ice cream is minus 5 degrees celsius, and the optimal drinking temperature of some beverage is 10 degrees celsius. The system can automatically determine the second temperature by querying a database or a pre-set criteria. Based on the first temperature (current temperature), the second temperature (optimal mouth feel temperature), the current ambient temperature, and the type of the commodity, the system calculates the time (ideal dispensing duration) required for the commodity to gradually approach its optimal mouth feel temperature from the current temperature. By combining the influence of the ambient temperature through the difference between the first temperature of the commodity and the optimal taste temperature, the system can calculate the ideal distribution duration required by the commodity. This time will ensure that the temperature of the product is closest to the optimal serving temperature when it reaches the customer's hand. And according to the calculated ideal distribution time length, the system arranges the distribution sequence and the distribution route of the cold chain commodities. The system will be optimized by scheduling to ensure that the temperature of the product gradually approaches its optimal mouth feel temperature during the dispensing process. According to the calculated ideal delivery duration, the system can dynamically adjust the delivery sequence to preferentially deliver the commodities with the temperature close to the target temperature, so that the temperature of the commodities meets the optimal taste requirement when delivering. This approach combines temperature and time factors to optimize the accuracy of the dispensing.
It can be understood that by considering the actual temperature change process of the commodity, the temperature of the commodity is ensured to be closer to the optimal taste temperature when the commodity arrives in the client, and the user experience is improved. By scheduling the delivery based on temperature changes, the system can ensure that the temperature changes of the commodity during transportation are more natural, so that the optimal temperature is reached when delivering, and temperature deviation caused by early or late delivery is avoided. By calculating the ideal delivery time length, the system can dynamically adjust the delivery route to preferentially deliver the commodities with faster temperature change or to schedule the commodities with temperature adjustment time in advance. The dynamic adjustment of the distribution route can maximize logistics efficiency and reduce unnecessary cold chain equipment use, thereby reducing energy consumption and ensuring that commodities are delivered at the most appropriate temperature. By ensuring that the commodity reaches the ideal temperature during distribution, the user experience is enhanced, and the quality and the taste of the commodity are ensured to the greatest extent.
Assuming that a batch of chilled beverage arrives at a dispensing station, the first temperature is 5 ℃, the optimal drinking temperature is 10 ℃, and the current ambient temperature is 25 ℃. The system calculates from these parameters that the ideal dispensing time for the beverage to gradually warm from 5 ℃ to 10 ℃ is 2 hours in the current environment. The system schedules the serving based on this time period, ensuring that the beverage approaches 10 ℃ when it arrives in the customer's hand, providing the best drinking experience.
While some frozen foods are stored in a frozen environment at-18 ℃ with an optimal serving temperature of-5 ℃. The temperature at which the product arrives at the dispensing station is-10 ℃ and the current ambient temperature is 20 ℃. The system calculates that a delivery time of about 3 hours is required for the frozen food to warm from-10 ℃ to-5 ℃. The system thus schedules the dispensing sequence and path of the product to ensure that the frozen food product is at an optimal serving temperature when delivered.
In some examples, the delivering of the cold chain commodity based on the ideal delivery duration includes:
Generating an ideal delivery path based on ideal delivery time lengths and receiving addresses of different cold chain commodities delivered in the same batch;
And carrying out the dispatch of the cold chain commodity based on the ideal dispatch path.
For example, each item may be subjected to the temperature calculation process described above to obtain an ideal delivery time period representing the time required for the item to approach the optimal mouthfeel temperature from the initial temperature at the current ambient temperature. For example, different types of frozen or refrigerated goods have different rates of temperature change and thus the desired dispensing time period may vary. The shipping address of each item provides the geographic location of the delivery. These addresses are combined with the ideal delivery duration to determine the order and path of delivery. By taking as input the ideal delivery duration and the shipping address, the path planning algorithm is used to generate the optimal delivery path. These algorithms can balance the length of the delivery path, the ideal delivery duration for each item, and the distance between the final shipping addresses. The ideal path generation process considers the ideal delivery time of each commodity, so that the delivery route is not only based on the planning of the shortest distance, but also dynamically adjusts the path sequence in combination with the time requirement of each commodity. For example, products with a shorter ideal delivery time are delivered first, ensuring that they arrive within an ideal time, or products requiring a longer temperature rise are delivered first, giving them more time to temperature adjustment. After the ideal dispatch path is determined, the system schedules dispatch of the cold chain commodity according to the generated route. The path planning not only optimizes the temperature control of the commodity, but also ensures the high efficiency of the dispatching process, and reduces the running time and mileage of the dispatching vehicle as much as possible. In the dispatching process, the dispatching vehicle dispatches different commodities in turn according to the generated paths, and each commodity is ensured to be dispatched according to the ideal dispatching time. The optimization of the vehicle path can also reduce the running time of the cold chain system and save energy consumption.
Taking a batch of cold chain delivery tasks comprising three fresh foods as an example, refrigerating beverages (optimal drinking temperature 10 ℃, current temperature 5 ℃, ideal delivery time period 2 hours), freezing seafood (optimal eating temperature-5 ℃, current temperature-10 ℃ and ideal delivery time period 3 hours), refrigerating vegetables (optimal preservation temperature 4 ℃, current temperature 0 ℃ and ideal delivery time period 1.5 hours).
The delivery addresses of each commodity were beverage customer a (2 km from warehouse), seafood customer B (5 km from warehouse) and vegetables customer C (3 km from warehouse).
By calculating the ideal dispensing time, the system finds that the ideal dispensing time for the chilled vegetables is the shortest, should deliver first, then the chilled beverage, and finally the chilled seafood. Based on the balance of address and duration, the system generates a dispatch route from warehouse to customer C, to customer A, and finally to customer B, ensuring that each commodity arrives within the optimal time.
It can be appreciated that by combining the ideal delivery time and the receiving address of different cold chain commodities, the path generated by the system not only considers the geographical distance, but also considers the temperature requirement of the commodities, ensures that the commodities are delivered at the best moment, and maintains the best quality of the commodities. Unlike traditional shortest path dispatch, the method can optimize the delivering time of each commodity, avoid the temperature deviation from the optimal taste temperature when the commodity arrives, and further improve the user experience. Because the path planning algorithm also considers the shortest optimization target, the system can ensure the maximization of the efficiency of delivering the path and reduce the running time and mileage of the vehicle on the premise of meeting the commodity temperature requirement. By reducing the running time of the vehicle, optimizing the dispatch path is conducive to reducing the logistics cost, reducing the energy consumption of the cold chain equipment, and simultaneously ensuring the commodity quality. When a plurality of cold chain commodities are in the same batch, the system can dynamically adjust the sending sequence according to the ideal sending time of each commodity, firstly send the commodities needing shorter sending time, and delay sending the commodities with longer ideal time. The flexible dispatching and scheduling mode ensures the preferential dispatching of commodities with higher temperature sensitivity, and reduces the quality loss caused by too fast temperature change of the commodities in transportation. By the aid of the temperature and time combined delivery mode, a customer can experience the temperature which is more in line with the expected temperature when receiving the cold chain commodity, such as the optimal drinking temperature of cold drink or the optimal preservation temperature of frozen food, and satisfaction of the customer on cold chain logistics service is remarkably improved. By optimizing the distribution time and the distribution path of the commodity, the temperature of the commodity received by the customer is closer to the optimal use temperature, and the overall quality of the logistics service is improved.
In some examples, further comprising:
determining a second temperature of the optimal mouthfeel of different cold chain commodities based on commodity information of the cold chain commodities;
and calculating ideal placement positions of the cold chain commodities in the delivery vehicle according to the first temperature, the second temperature, the current environment temperature and the types of the cold chain commodities, wherein the cold chain commodities in different placement positions are affected differently by the environment temperature and the temperatures of adjacent cold chain commodities, so that after ideal delivery time, different cold chain commodities are affected by the current environment temperature and are approaching to the second temperature time from the first temperature.
Illustratively, the system obtains the optimal mouthfeel temperature (second temperature) of the cold chain commodity from the database according to the types of different commodities and the temperature requirements thereof. For example, frozen foods typically have an optimal eating temperature that is lower than that of a chilled beverage, and the storage temperature of the chilled vegetables may be different. Different cold chain products have respective optimal preservation or eating temperatures. For example, ice cream has an optimal serving temperature of-10 ℃ and a certain juice beverage has an optimal serving temperature of 5 ℃. By inquiring the types of the commodities and the optimal taste temperature of the commodities, the system can determine the second temperature and provide data support for subsequent temperature control and placement optimization. The current temperature at which each article arrives at the delivery vehicle is referred to as the "first temperature", while the system acquires the current external ambient temperature (the temperature outside the vehicle). These temperature parameters will serve as the basis for influencing the calculation of the placement position of the goods. According to the first temperature, the second temperature, the current environment temperature and the type of the commodity, the system calculates the ideal placement position of the commodity in the vehicle. Different placement locations (e.g., front, rear, up and down of the car) may be affected by differences in the outside ambient temperature and the adjacent commodity temperature. The system needs to calculate a reasonable placement position according to the influences, so that the temperature of the commodity can gradually approach the optimal temperature in the distribution process.
By way of example, the factors considered in the calculation may include the degree of contact of the article with the external environment, such as where certain locations of the vehicle (e.g., near doors, roof) may be more affected by ambient temperature, while articles located inside the cabin may be less affected by temperature changes. The effect of adjacent products, such as some frozen products (e.g., ice cream), may affect the temperature of nearby refrigerated products (e.g., beverages), and therefore the temperature transfer between adjacent products also needs to be taken into account. The system can reasonably distribute the placement positions of commodities in the carriage based on the temperature requirements of different commodities and the influence of external temperature. For example, frozen goods that are at a higher temperature and require maintenance of a low temperature may be placed at the bottom of the vehicle or at a location remote from the door, while goods that are relatively temperature insensitive may be placed near the door to facilitate preferential dispensing. According to the temperature distribution in the vehicle and the placement position of the commodities, the system can calculate the temperature change speed of the commodities in the dispatching process, and ensure that the temperature gradually approaches the optimal taste temperature of the commodities in the ideal dispatching duration. Through reasonable commodity placement, the system can optimize the temperature distribution in the carriage, so that the commodity can naturally heat up or cool down in the whole distribution process, and finally, the temperature approaches to the optimal taste temperature when the commodity is delivered to a customer. Such temperature variation not only depends on the cold chain equipment, but also utilizes the temperature environment in the vehicle to achieve precise control.
Assuming that a cold chain vehicle carries the following goods, namely frozen seafood (current temperature-18 ℃ and optimal eating temperature-5 ℃), frozen milk (current temperature 2 ℃ and optimal eating temperature 4 ℃), frozen ice cream (current temperature-20 ℃ and optimal eating temperature-10 ℃) and refrigerated vegetables (current temperature 1 ℃ and optimal preserving temperature 4 ℃) and the system calculates ideal placement positions according to the temperature requirements of the goods, namely the frozen seafood and the ice cream need to be placed in a colder area of a carriage, such as a position far away from a car door, so as to reduce the influence of external temperature. Refrigerated milk and vegetables can be placed in close proximity to the vehicle door because these products are insensitive to temperature variations and require preferential distribution. By the arrangement mode, the temperature of the ice cream and the seafood can be kept in a lower range, and the temperature of the milk and the vegetables can be gradually increased to approach the optimal taste temperature. The method fully utilizes the temperature difference between the inside and outside of the carriage and the mutual temperature influence between commodities, optimizes the temperature control in the cold chain distribution process, improves the logistics efficiency and finally provides higher-quality cold chain service for users.
It can be understood that the system can utilize the temperature difference between the inside and the outside of the carriage and the temperature influence of adjacent commodities to realize the dynamic control of the temperatures of different commodities by reasonably distributing the cold chain commodities at different positions in the carriage. This approach reduces reliance on cold chain equipment while ensuring that the product gradually approaches its optimal mouth feel temperature during distribution. By optimizing the temperature environment in the carriage, the system can improve the precision of cold chain logistics, reduce the energy consumption of the cold chain and simultaneously ensure that the goods keep the best quality when delivered. The system not only considers the effect of ambient temperature, but also analyzes the temperature transfer between goods. For example, articles with higher temperatures may affect the temperature variation of adjacent articles, so that such interference can be reduced by reasonable placement. By reducing temperature interference between the products, the system can more effectively control temperature changes, ensuring that different products can each reach their ideal temperature conditions. According to the ideal placement position, the system can optimize the loading process, so that the commodity can naturally reach the target temperature in the transportation process. The refrigeration function of the cold chain equipment is not needed to be excessively relied on, and the energy efficiency and commodity quality in the logistics process are effectively improved. Through reasonable arrangement and temperature control, the system can optimize the space utilization of the cold chain carriage under the premise of ensuring proper commodity temperature, and reduces the transportation cost. Through the temperature and placement optimized dispatch mode, the client can experience the temperature more in line with the expectations when receiving the commodity. For example, cold drinks can be delivered at a suitable drinking temperature, while frozen foods can remain in their low temperature state, significantly improving user satisfaction. The accuracy of temperature control not only improves commodity quality, but also enhances the trust and satisfaction degree of customers on cold chain logistics service.
In some examples, the calculating the ideal placement position of the cold chain commodity in the dispatch vehicle based on the first temperature, the second temperature, the current ambient temperature, and the type of the cold chain commodity includes:
and calculating the ideal placement position of the cold chain commodity in the delivery vehicle through a particle swarm optimization algorithm according to the first temperature, the second temperature, the current environment temperature and the type of the cold chain commodity.
Illustratively, it is assumed that each particle represents a candidate solution (in a cold chain scenario, one particle may represent the placement of an item in a vehicle). The particles iteratively search for the optimal solution in the solution space through updates of velocity and position. The update of the position and velocity of the particle is jointly affected by the current particle position, the individual optimal solution of the particle, and the global optimal solution. By continuously updating the position of the particles, the global optimal solution (an ideal placement scheme, so that the commodity reaches the optimal temperature in distribution) is gradually approached. In a cold chain scenario, particles represent the placement of cold chain merchandise in a distribution vehicle. It is assumed that the cabin can be divided into a plurality of different temperature-affected zones (e.g., near the door, near the middle of the cabin, upper and lower floors, etc.), each zone having a temperature that is affected differently by the outside ambient temperature and the temperature of adjacent goods. The particles may be represented as a position vector, with each dimension corresponding to a placement location of the cold chain commodity. Assuming that the compartment of the delivery vehicle is divided into n regions available for placement, each dimension of the particle may represent a region number. Position vector xi=(xi1,xi2,…,xin), where xij represents the placement position of the ith particle (cold chain commodity) as region j. Particle swarm optimized particle velocity can be redefined as the step size or the amplitude of variation of the adjustment to the merchandise placement plan. The method has the significance of not controlling the movement of the physical positions of the commodities, but guiding an algorithm to search a solution space for a better commodity placement scheme, and indicating the approaching degree of the current commodity placement scheme to a better solution. Velocity vector vi=(vi1,vi2,…,vin), where vij represents the velocity of the ith particle in the jth dimension (region number).
The objective of the particle swarm optimization algorithm is to find an optimal placement scheme, so that the temperature of each cold chain commodity is close to the optimal taste temperature in the distribution process. For this purpose, an fitness function needs to be defined to measure the quality of a certain placement scheme. The fitness function may be designed based on the first temperature T1, the current temperature of the commodity. The second temperature T2 is the optimal taste temperature of the commodity. Current ambient temperature Te ambient temperature outside the vehicle. The temperature of the adjacent articles affects the heat transfer between the adjacent articles Tadj. The objective of the fitness function is to minimize two factors, namely temperature deviation, the difference between the final temperature of a certain commodity and the optimal temperature of the commodity, and the temperature change rate, namely, the temperature change rate of the current commodity is estimated according to the placement position of the commodity in the vehicle. This rate of change is determined by both the external ambient temperature and the temperature of the adjacent commodity. The fitness function F (x) may be expressed as:
Where f is a function representing the relationship between the rate of change of temperature at different locations in the vehicle and the external ambient temperature and the temperature of the adjacent commodity. Alpha and beta are weight parameters for adjusting the influence of temperature deviation and temperature change rate in the optimization process. Ti1 and Ti2 are the first temperature and the second temperature, respectively, of the ith commodity.
The velocity and position of each particle are updated according to the individual optimal solution and the global optimal solution. For the j-th dimension of the i-th particle, the updated formula for position and velocity is as follows: Xij(t+1)=xij(t)+vij (t+1), where ω is the inertial weight, for controlling the variation of the particle velocity. c1 and c2 are learning factors, respectively, that control the speed at which particles approach the individual optimal solution and the global optimal solution. r1 and r2 are random numbers that enhance the randomness of the algorithm. pij is the individual optimal solution of particle i in the j-th dimension. gj is the global optimal solution.
The method comprises the steps of randomly generating a plurality of particles (namely a placement scheme of cold chain commodities), calculating an fitness value of each particle according to a fitness function, evaluating the advantages and disadvantages of the placement scheme, adjusting the placement position of each commodity according to a speed and position updating formula of particle swarm optimization, repeatedly executing speed updating, position updating and fitness calculation until preset iteration times or fitness convergence is achieved, and finding an optimal commodity placement scheme to enable the temperature of each commodity to be close to the optimal taste temperature in the distribution process.
The block chain-based commodity circulation tracking method in the embodiment of the present application is described above, and the block chain-based commodity circulation tracking system in the embodiment of the present application is described below.
Referring to FIG. 2, one embodiment of a blockchain-based commodity circulation tracking system is described in accordance with embodiments of the present application and may include:
an acquiring unit 201, configured to acquire logistics transportation information of a cold chain commodity, where the logistics transportation information includes a transportation time interval, a transportation path, and a transportation vehicle associated with the cold chain commodity;
a query unit 202, configured to query commodity information of other cold chain commodities matching the logistics transportation information, where the commodity information includes a type, a number, and package size data of the commodity;
And an alarm unit 203, configured to generate a logistics transportation data authenticity alarm and record the logistics transportation data authenticity alarm to a blockchain system when the total volume calculated based on the number of the commodities and the package size data is greater than the volume of the transportation vehicle associated with the cold chain commodity.
In summary, the commodity logistics tracking system based on the blockchain provided by the embodiment of the invention obtains logistics transportation information of the cold chain commodity, wherein the logistics transportation information comprises a transportation time interval, a transportation path and a transportation vehicle associated with the cold chain commodity, inquires commodity information of other cold chain commodities matched with the logistics transportation information, wherein the commodity information comprises types, numbers and package size data of the commodity, and generates a logistics transportation data authenticity alarm and records to the blockchain system under the condition that the total volume calculated based on the numbers and the package size data of the commodity is larger than the volume of the transportation vehicle associated with the cold chain commodity. Therefore, through the rationality verification of the commodity quantity, the package size and the vehicle volume, the possible problems of data tampering or inaccurate input can be effectively identified. For example, if the total volume of the goods of a cold chain transport vehicle is found to exceed its volume, it may mean that the input data is not authentic, helping to find problems ahead of time. Once a problem is detected, a system generated alert is recorded on the blockchain, ensuring that the alert can be traced back and confirmed in subsequent reviews. This transparent record provides additional trust assurance that the data tampering behavior can be tracked. Through the sensor and the intelligent contract, the system can realize a full-automatic checking process, reduce errors caused by manual intervention and ensure data consistency. Each operation is transparently recorded on the blockchain, providing proof of tamper-proof verification.
In some examples, further comprising:
and displaying the recommended placement sequence and the ideal placement position of the commodity storage space of the matched delivery vehicle on the delivery terminal based on the determined ideal placement position.
The blockchain-based commodity circulation tracking system in the embodiment of the present application is described above in terms of a modularized functional entity in fig. 2, and the blockchain-based commodity circulation tracking system in the embodiment of the present application is described below in detail in terms of hardware processing, referring to fig. 3, an embodiment of the blockchain-based commodity circulation tracking system 300 in the embodiment of the present application includes:
Input device 301, output device 302, processor 303, and memory 304, wherein the number of processors 303 may be one or more, one processor 303 being exemplified in fig. 3. In some embodiments of the present application, the input device 301, the output device 302, the processor 303, and the memory 304 may be connected by a bus or other means, where a bus connection is illustrated in FIG. 3.
Wherein, by calling the operation instruction stored in the memory 304, the processor 303 is configured to execute the following steps:
Acquiring logistics transportation information of cold chain commodities, wherein the logistics transportation information comprises a transportation time interval, a transportation path and transportation vehicles associated with the cold chain commodities;
Inquiring commodity information of other cold chain commodities matched with the logistics transportation information, wherein the commodity information comprises the types, the quantity and the package size data of the commodities;
And generating a logistics transportation data authenticity alarm and recording the logistics transportation data to a blockchain system under the condition that the total volume calculated based on the quantity of the commodities and the package size data is larger than the volume of the transportation vehicle associated with the cold chain commodity.
The processor 303 is further configured to execute any of the embodiments corresponding to fig. 1 by calling the operation instructions stored in the memory 304.
Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the application.
As shown in fig. 4, an embodiment of the present application provides an electronic device, including a memory 410, a processor 420, and a computer program 411 stored on the memory 410 and executable on the processor 420, wherein the processor 420 implements the following steps when executing the computer program 411:
Acquiring logistics transportation information of cold chain commodities, wherein the logistics transportation information comprises a transportation time interval, a transportation path and transportation vehicles associated with the cold chain commodities;
Inquiring commodity information of other cold chain commodities matched with the logistics transportation information, wherein the commodity information comprises the types, the quantity and the package size data of the commodities;
And generating a logistics transportation data authenticity alarm and recording the logistics transportation data to a blockchain system under the condition that the total volume calculated based on the quantity of the commodities and the package size data is larger than the volume of the transportation vehicle associated with the cold chain commodity.
In a specific implementation, when the processor 420 executes the computer program 411, any implementation of the embodiment corresponding to fig. 1 may be implemented.
Since the electronic device described in this embodiment is a device for implementing a blockchain-based commodity circulation tracking system according to the embodiment of the present application, based on the method described in this embodiment of the present application, those skilled in the art can understand the specific implementation of the electronic device of this embodiment and various modifications thereof, so how the electronic device implements the method in this embodiment of the present application will not be described in detail herein, and only those devices for implementing the method in this embodiment of the present application will belong to the scope of protection intended by those skilled in the art.
Referring to fig. 5, fig. 5 is a schematic diagram of an embodiment of a computer readable storage medium according to an embodiment of the application.
As shown in fig. 5, the present embodiment provides a computer-readable storage medium 500 having stored thereon a computer program 511, which computer program 511 when executed by a processor implements the steps of:
Acquiring logistics transportation information of cold chain commodities, wherein the logistics transportation information comprises a transportation time interval, a transportation path and transportation vehicles associated with the cold chain commodities;
Inquiring commodity information of other cold chain commodities matched with the logistics transportation information, wherein the commodity information comprises the types, the quantity and the package size data of the commodities;
And generating a logistics transportation data authenticity alarm and recording the logistics transportation data to a blockchain system under the condition that the total volume calculated based on the quantity of the commodities and the package size data is larger than the volume of the transportation vehicle associated with the cold chain commodity.
The processor 303 is further configured to execute any of the embodiments corresponding to fig. 1 by calling the operation instructions stored in the memory 304.
Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the application.
As shown in fig. 4, an embodiment of the present application provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored on the memory 410 and executable on the processor 420, wherein the processor 420 implements the following steps when executing the computer program 411:
Acquiring logistics transportation information of cold chain commodities, wherein the logistics transportation information comprises a transportation time interval, a transportation path and transportation vehicles associated with the cold chain commodities;
Inquiring commodity information of other cold chain commodities matched with the logistics transportation information, wherein the commodity information comprises the types, the quantity and the package size data of the commodities;
And generating a logistics transportation data authenticity alarm and recording the logistics transportation data to a blockchain system under the condition that the total volume calculated based on the quantity of the commodities and the package size data is larger than the volume of the transportation vehicle associated with the cold chain commodity.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Drive (SSD)), etc.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
While the application has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modification or substitution does not depart from the spirit and scope of the embodiments of the application in nature.