A cold-chain logistics transportation single printing method and system

Abstract

The application discloses a cold-chain logistics transport single printing method and system, relates to the technical field of cold-chain logistics transport single processing, and comprises the following steps: receiving original business data containing goods categories, goods receiving addresses and distribution time period requirements, and generating dynamic temperature control strategy data; querying a goods biological characteristic database to obtain an allowed maximum temperature change rate and a cumulative temperature change threshold, and generating temperature-sensitive constraint data; and fusing the two to generate final temperature control execution parameters through a strategy adjustment algorithm. A transport single template library is called to format a template to fill structured data, key monitoring fields are extracted to generate a unique two-dimensional code embedded in a specified position, a printing device hardware characteristic is adapted to fine-tune a version to generate printing drive data, and a printing physical transport single is driven. The system realizes accurate customization of temperature control parameters in combination with goods biological characteristics and dynamic scenes, integrates a dynamic monitoring two-dimensional code into a transport single to improve on-site tracking and verification efficiency, and reduces goods loss risks.

Description

Technical Field

[0001] This invention belongs to the field of cold chain logistics transport document processing technology, specifically a method and system for printing cold chain logistics transport documents. Background Technology

[0002] Conventional cold chain logistics transport document printing often relies on directly calling fixed templates from order information. Temperature control strategies are typically set with uniform temperature ranges based on broad product categories, without considering the specific biological characteristics of the goods. The QR codes attached to transport documents usually only contain static information such as waybill numbers and sender / recipient addresses, failing to reflect key parameters that require dynamic monitoring during transport. Existing technologies, due to a lack of refined consideration of the maximum permissible rate of temperature change and cumulative temperature change thresholds for goods, easily lead to damage to highly sensitive goods during delivery due to exceeding temperature limits. Furthermore, the limited information in the transport document QR codes makes it difficult for on-site personnel to quickly obtain the current temperature control requirements and the product's tolerance limits, relying on manual verification of paper documents or additional system queries for tracking and verification.

[0003] A database of the biological characteristics of goods needs to be established to store the maximum temperature change rate and cumulative temperature change threshold corresponding to different categories of goods. After receiving an order, parameters are extracted based on the category of goods to generate temperature-sensitive constraint data, which is then integrated with a dynamic temperature control strategy based on the delivery address and delivery time requirements. The algorithm generates accurate temperature control execution parameters. At the same time, key monitoring fields such as temperature control execution parameters and temperature-sensitive constraints are extracted from the structured data of the transport order, a unique QR code is generated and embedded in a designated location on the transport order, so that the transport order carries dynamic monitoring information to assist on-site execution. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art; Therefore, this invention proposes a method for printing cold chain logistics transport documents, including: Receive raw business data containing cold chain logistics order information, and generate dynamic temperature control strategy data based on the raw business data. The cold chain logistics order information includes product category, delivery address, and delivery time requirements. Based on the product category, query the corresponding product biological characteristics database to obtain the maximum allowable temperature change rate and cumulative temperature change threshold of the product, and generate product temperature-sensitive constraint data. By integrating dynamic temperature control strategy data and product temperature-sensitive constraint data, the final temperature control execution parameters are generated through a strategy adjustment algorithm; Based on the final temperature control execution parameters and transportation route plan data, the formatted template in the transportation order template library is called to fill in and generate the structured data of the transportation order to be printed; Extract key monitoring fields from the structured data of the transport order to be printed, and generate unique QR code graphic encoding data based on the key monitoring fields; The QR code graphic encoding data is embedded in a specified position in the structured data of the shipping document to be printed, forming a complete shipping document page with a QR code; Based on the hardware characteristics of the printing equipment, the resolution of the complete shipping single page data with QR code is adapted and the layout is fine-tuned to generate the final printing driver data. The final print drive data is sent to the designated printing device, which then outputs the physical shipping document.

[0005] Furthermore, dynamic temperature control strategy data is generated based on the original business data, including: Receive raw business data containing cold chain logistics order information. The cold chain logistics order information is processed for cold chain routing planning to generate transportation route plan data that includes the nodes to be passed, the temperature requirements of the nodes, and the expected dwell time. Based on the transit nodes in the transportation route plan data, query the corresponding static cold chain equipment information, and combine it with the delivery time requirements to generate dynamic temperature control strategy data; The process of cold chain routing planning for the cold chain logistics order information includes: Parse the delivery address in the cold chain logistics order information and match it with standardized address codes in the geographic information service; Based on standardized address coding, the initial path from the starting point to the destination is calculated using the shortest path algorithm in the pre-set cold chain transit node network. Based on the product category and delivery time requirements, necessary refrigerated transfer stations and checkpoints are inserted into the initial route to form transportation route plan data containing multiple mandatory and optional nodes.

[0006] Furthermore, the step of querying the corresponding static cold chain equipment information based on the transit nodes in the transportation route plan data, and generating dynamic temperature control strategy data in conjunction with delivery time requirements, includes: Access the cold chain equipment information database to obtain the model, rated temperature range, and temperature control accuracy parameters of the refrigerated equipment or transportation vehicles equipped at each node in the transportation route plan data; Based on delivery time requirements, the entire transportation process is divided into multiple transportation stages according to time. For each transportation stage, based on the equipment parameters of the nodes it passes through, the recommended target temperature and allowable temperature fluctuation bandwidth for the transportation stage are calculated and set, and the temperature control settings of all stages are summarized to form dynamic temperature control strategy data.

[0007] Furthermore, the step of querying the corresponding product biometric database based on the product category to obtain the maximum allowable temperature change rate and cumulative temperature change threshold of the product, and generating product temperature-sensitive constraint data, includes: Based on the product category in the cold chain logistics order information, retrieve predefined product temperature-sensitive characteristic records from the product biometric database; Quantitative parameters characterizing the goods' ability to withstand temperature changes are extracted from the goods' temperature-sensitive characteristic records. These include the maximum allowable temperature change per unit time as the maximum temperature change rate, and the upper limit of the total allowable temperature deviation from the standard value throughout the entire transportation cycle as the cumulative temperature change threshold. The maximum temperature change rate and the cumulative temperature change threshold are encapsulated as temperature-sensitive constraint data for goods.

[0008] Furthermore, the fusion of dynamic temperature control strategy data and product temperature-sensitive constraint data, through a strategy adjustment algorithm, generates the final temperature control execution parameters, including: Compare the target temperature of each transportation stage in the dynamic temperature control strategy data with the maximum temperature change rate in the cargo temperature-sensitive constraint data. If the rate of change required for the target temperature difference between adjacent stages exceeds the maximum temperature change rate, a temperature buffer stage is inserted to reduce the temperature change rate. Based on the cumulative temperature change threshold in the temperature-sensitive constraint data of the goods, the total theoretical temperature change value of the entire transportation route plan is checked. If it exceeds the threshold, the target temperature of some stages in the dynamic temperature control strategy data is adjusted to smooth it out. The target temperature, duration, and temperature change rate limits for each stage, adjusted and conforming to all constraints, are output as the final temperature control execution parameters.

[0009] Furthermore, based on the final temperature control execution parameters and transportation route plan data, the process of calling a formatted template from the transportation order template library and filling it with structured data for the transportation order to be printed includes: Based on the business type in the cold chain logistics order information, select a matching preset template from the transport order template library; Fill in the text information in the cold chain logistics order information and transportation route plan data, as well as the numerical information in the final temperature control execution parameters, one by one according to the predefined field placeholders in the preset template; Generates structured data for the shipping order to be printed, including all filler content, data structure, and formatting.

[0010] Furthermore, the step of extracting key monitoring fields from the structured data of the transport document to be printed, and generating unique QR code graphic encoding data based on the key monitoring fields, includes: From the structured data of the waybill to be printed, key fields for transportation process tracking and temperature verification are selected, including the unique waybill number, the sequence of numbers of major transit nodes, and the target temperature value at each stage. The selected key fields are serialized and concatenated into a one-dimensional data string according to preset encoding rules; The one-dimensional data string is converted into dot matrix data corresponding to the QR code image using a QR code generation algorithm, i.e., QR code graphic encoding data.

[0011] Furthermore, the step of embedding the QR code graphic encoding data into a designated location within the structured data of the transport document to be printed, forming a complete transport document page with a QR code, includes: In the page layout corresponding to the structured data of the transport order to be printed, locate the coordinates of the rectangular area reserved for the QR code; The dot matrix image represented by the QR code graphic encoding data is scaled and aligned proportionally according to the size of the rectangular area coordinates. The scaled and aligned QR code dot matrix image is layered with the page image data of the structured data of the shipping document to be printed, generating complete page image data containing visual text information and QR code graphics.

[0012] Furthermore, based on the hardware characteristics of the printing device, the resolution of the complete shipping single-page data with QR code is adapted and the layout is fine-tuned to generate the final print driver data, including: Obtain the resolution, paper size, and print margin parameters supported by the target printing device; Based on the resolution supported by the printing equipment, the complete shipping manifest data with QR code is resampled to ensure image clarity; Based on the paper size and printing margin parameters, the absolute position and relative layout of all content in the complete shipping single-page data are adjusted to adapt the content to the target paper, generating page description language data that can directly drive the printing device as the final printing drive data.

[0013] Furthermore, the present invention also includes a cold chain logistics transport document printing system, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the cold chain logistics transport document printing method described above.

[0014] Compared with the prior art, the beneficial effects of the present invention are: A database of product biological characteristics is established to store the maximum allowable temperature change rate and cumulative temperature change threshold for different product categories. After receiving raw business data containing product category, delivery address, and delivery time requirements, the database is queried to extract corresponding parameters and generate product temperature-sensitive constraint data. Dynamic temperature control strategy data generated based on delivery address and delivery time is integrated, and the final temperature control execution parameters are generated through a strategy adjustment algorithm. This allows temperature control parameters to break through the limitations of uniformly setting parameters for broad categories, closely combining the specific biological tolerance characteristics of the products with the dynamic needs of the delivery scenario. The operating frequency and temperature range control of refrigeration equipment are more closely aligned with the actual tolerance capacity of the products, avoiding the risk of product damage due to excessively rapid temperature change rates or cumulative exceeding of limits.

[0015] Key monitoring fields are extracted from the structured data of the transport document to be printed, including final temperature control execution parameters, cargo temperature sensitivity constraint data, delivery time requirements, and delivery address. Based on these fields, a unique QR code is generated and embedded in a designated location within the transport document's structured data to form a complete page. The transport document thus becomes a carrier of dynamic monitoring information. On-site personnel can scan the code to obtain real-time temperature control parameters and cargo tolerance limits without switching systems, improving the directness of status tracking and constraint verification during transportation and reducing information lag and errors in manual verification. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the cold chain logistics transport document printing method described in this invention. Figure 2 Generate detailed flowcharts for dynamic temperature control strategy data; Figure 3 A flowchart for generating temperature-sensitive constraint data for goods; Figure 4 A multi-dimensional radar chart for the cold chain logistics transportation stage; Figure 5 This is a graph showing the temperature-sensitive characteristics of the goods. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] See Figure 1This invention provides a method for printing cold chain logistics transport orders, with the following specific steps: Receiving raw business data containing cold chain logistics order information; generating dynamic temperature control strategy data based on this raw business data; the cold chain logistics order information includes product category, delivery address, and delivery time requirements. Querying the corresponding product biometric database based on the product category to obtain the maximum allowable temperature change rate and cumulative temperature change threshold, generating product temperature-sensitive constraint data. Integrating the dynamic temperature control strategy data and product temperature-sensitive constraint data, generating final temperature control execution parameters through a strategy adjustment algorithm. Based on the final temperature control execution parameters and transport route plan data, calling a formatted template from the transport order template library to fill and generate structured transport order data to be printed. Extracting key monitoring fields from the structured transport order data to be printed, generating unique QR code graphic encoding data based on these key monitoring fields. Embedding the QR code graphic encoding data into a specified position in the structured transport order data to be printed, forming a complete transport order page with a QR code. Adapting the resolution and fine-tuning the layout of the complete transport order page data with the QR code according to the hardware characteristics of the printing device, generating the final print driver data. The final print drive data is sent to the designated printing device, which then outputs the physical shipping document.

[0019] See Figure 2 In one embodiment of the present invention, dynamic temperature control strategy data is generated based on original business data. The process involves receiving original business data containing cold chain logistics order information, performing cold chain routing planning on the cold chain logistics order information to generate transportation route plan data including transit nodes, node temperature requirements, and estimated dwell time. The cold chain routing planning process parses the delivery address in the cold chain logistics order information and matches it with standardized address codes in a geographic information service. Based on the standardized address codes, the initial path from the origin to the destination is calculated using a shortest path algorithm within a pre-defined cold chain transit node network. Necessary refrigeration transit stations and checkpoints are inserted into the initial path according to the product category and delivery time requirements, forming transportation route plan data containing multiple mandatory and optional nodes. The corresponding static cold chain equipment information is queried based on the transit nodes in the transportation route plan data, and combined with the delivery time requirements, dynamic temperature control strategy data is generated. A cold chain equipment information database is accessed to obtain the model, rated temperature range, and temperature control accuracy parameters of the refrigeration equipment or transportation vehicle equipped at each transit node in the transportation route plan data. Based on delivery time requirements, the entire transportation process is divided into multiple transportation stages. For each transportation stage, the recommended target temperature and allowable temperature fluctuation bandwidth are calculated and set according to the equipment parameters of the nodes it passes through. The temperature control settings of all stages are then aggregated to form dynamic temperature control strategy data.

[0020] In specific implementation, the embodiment of the cold chain logistics transport order printing method involves generating dynamic temperature control strategy data. The input to this process is cold chain logistics order information that includes the product category, delivery address, and delivery time requirements. A specific example scenario is processing a batch of medical vaccine orders shipped from a Shanghai warehouse to Haidian District, Beijing, with a delivery time requirement of 48 hours.

[0021] In practice, cold chain logistics order information undergoes cold chain routing planning to generate transportation route plan data. The delivery address "Haidian District, Beijing" in the cold chain logistics order information is parsed and matched to a standardized address code "110108" by calling a geographic information service interface. Based on the standardized address code "110108", the initial path from the starting point "Shanghai Warehouse" to the destination "Beijing Haidian Distribution Station" is calculated using the Dijkstra shortest path algorithm within the pre-defined cold chain transit node network. The initial path calculation result may show as "Shanghai Warehouse - Nanjing Transit Station - Jinan". Based on the product category "medical vaccines" and the delivery time requirement of "48 hours", the initial route includes necessary refrigerated transit stations and checkpoints. Medical vaccines require temperature control and recording throughout the entire process, so a checkpoint with high-precision temperature recording function, "Tianjin Cold Chain Checkpoint", is inserted between "Jinan Transit Station" and "Beijing Central Warehouse". The resulting transportation route plan data includes the sequence of nodes "Shanghai Warehouse, Nanjing Transit Station, Jinan Transit Station, Tianjin Cold Chain Checkpoint, Beijing Central Warehouse, Beijing Haidian Distribution Station", and each node is assigned a temperature requirement and an estimated dwell time.

[0022] In some embodiments, based on the transit nodes in the transportation route plan data, the corresponding static cold chain equipment information is queried and dynamic temperature control strategy data is generated in combination with the delivery time requirements. The cold chain equipment information database is accessed and queried to obtain that the refrigerated truck equipped at the "Nanjing Transfer Station" is of type A, with a rated temperature range of 2℃ to 8℃ and a temperature control accuracy of ±0.5℃, and the cold storage at the "Jinan Transfer Station" is of type B, with a rated temperature range of 2℃ to 8℃ and a temperature control accuracy of ±0.3℃. In combination with the "48-hour" delivery time requirement, the entire transportation process from the Shanghai warehouse to the Beijing Haidian distribution station is divided into five transportation stages in chronological order: Shanghai warehouse to Nanjing transfer station transportation stage, Nanjing transfer station operation stage, Nanjing transfer station to Jinan transfer station transportation stage, Jinan transfer station operation stage, and Jinan transfer station to final destination transportation stage. For the "Shanghai warehouse to Nanjing transit station transportation phase," the relevant nodes are in transit. Based on the equipment parameters of the Type A refrigerated trucks used, the recommended target temperature for this phase is calculated and set to 5℃, with an allowable temperature fluctuation bandwidth of ±1℃. For the "Jinan transit station operation phase," the relevant node is the Jinan transit station cold storage. Based on the equipment parameters of the Type B cold storage, the recommended target temperature for this phase is set to 3℃, with an allowable temperature fluctuation bandwidth of ±0.5℃. The temperature control settings for all five phases are summarized to form dynamic temperature control strategy data, including phase number, start and end nodes, recommended target temperature, and allowable temperature fluctuation bandwidth.

[0023] It is understandable that the generation of dynamic temperature control strategy data depends on accurate path and equipment information. In other data comparison scenarios, if the delivery address of the same batch of medical vaccines changes to Yuexiu District, Guangzhou, the standardized address code matched by geographic information services will be different. The initial path calculated based on the cold chain transit node network will become a southern route, and the nodes along the way and the queried static cold chain equipment information will change accordingly. The temperature settings at each stage in the final generated dynamic temperature control strategy data will also be different due to equipment differences. For example, it may involve C-type refrigerated containers that are suitable for the high-temperature environment in the south, whose rated temperature range and temperature control accuracy parameters are different from those of A-type refrigerated trucks.

[0024] Optionally, when using the shortest path algorithm in cold link planning, the weight calculation of path cost can comprehensively consider distance and time factors, and be evaluated using the following formula:

[0025] Where: character C represents the total cost of the route, character D represents the physical distance of the route (in kilometers), character T represents the estimated travel time of the route (in hours), and character... The weighting coefficients for distance and time are β and β, respectively. Both are real numbers greater than 0 and less than or equal to 1. They are configured by the system according to the urgency of the delivery time period. The algorithm calculates the initial path with the goal of minimizing the overall path cost C.

[0026] Optionally, when dividing the transportation stages in conjunction with delivery time requirements, the granularity of the stage division can be adjusted according to the sensitivity of the product category. For highly sensitive products such as medical vaccines, the system will adopt a more refined division method, and the inbound, storage, and outbound operations of each transit node may be broken down into independent micro-stages for temperature control settings, thereby generating more detailed and stringent dynamic temperature control strategy data.

[0027] See Figure 3 In one embodiment of the present invention, the corresponding product biometric database is queried based on the product category to obtain the maximum permissible temperature change rate and cumulative temperature change threshold of the product, thereby generating product temperature-sensitive constraint data. Based on the product category in the cold chain logistics order information, predefined product temperature-sensitive characteristic records are retrieved from the product biometric database. Quantitative parameters characterizing the product's tolerance to temperature changes are extracted from these records, including the maximum permissible temperature change value per unit time as the maximum temperature change rate, and the upper limit of the sum of permissible temperature deviations from the standard value throughout the entire transportation cycle as the cumulative temperature change threshold. The maximum temperature change rate and the cumulative temperature change threshold are then encapsulated into product temperature-sensitive constraint data.

[0028] In specific implementation, the embodiment of the cold chain logistics transport order printing method involves generating temperature-sensitive constraint data for goods. The input to this process is the category of goods in the cold chain logistics order information. A specific example scenario is processing a batch of orders with the category of goods labeled as "fresh salmon slices". The system performs subsequent queries based on the category of "fresh salmon slices".

[0029] In practice, based on the product category "fresh salmon slices" in the cold chain logistics order information, the system retrieves predefined temperature-sensitive characteristic records from the product biological characteristics database. The product biological characteristics database is a structured database that stores preset biological and physicochemical characteristics of various product categories. The system executes a structured query language statement, using "fresh salmon slices" as the keyword to perform an exact match query in the "category name" field of the database. The query returns a product temperature-sensitive characteristic record that completely corresponds to "fresh salmon slices". This record contains multiple tolerance parameters for fish muscle tissue in aquatic products under cold chain conditions.

[0030] Quantitative parameters characterizing the product's tolerance to temperature changes are extracted from the product's temperature-sensitive characteristic records. These include the maximum allowable temperature change per unit time as the maximum temperature change rate, and the upper limit of the total allowable temperature deviation from the standard value throughout the entire transportation cycle as the cumulative temperature change threshold. For "fresh salmon slices," the product's temperature-sensitive characteristic record shows that to maintain its cell structure and prevent protein denaturation, the maximum allowable temperature change rate is 0.5 degrees Celsius per hour. This means that within any one-hour time window during transportation, the ambient temperature change should not exceed 0.5 degrees Celsius. The same record also shows that to control microbial growth and maintain sensory quality, the allowable cumulative temperature change threshold is 3 degrees Celsius. This means that from the origin to the destination of transportation, the sum of the absolute values ​​of all temperature fluctuations experienced by the product (deviations above or below the standard storage temperature by 2 degrees Celsius) cannot exceed 3 degrees Celsius. The extracted maximum temperature change rate "0.5 degrees Celsius per hour" and cumulative temperature change threshold "3 degrees Celsius" are encapsulated according to the system's predefined data structure to generate a formatted product temperature-sensitive constraint data object.

[0031] In some embodiments, the record structure of the product biological characteristics database is standardized. Each record of product temperature sensitivity characteristics contains the fields of "maximum temperature change rate" and "cumulative temperature change threshold". After the system retrieves the record through the database interface, it directly reads the values ​​from these two named fields. For "fresh salmon slices", the read field values ​​are "0.5℃ / h" and "3℃" respectively. The system encapsulates these two values ​​together with the corresponding physical units.

[0032] It is understandable that the generation of temperature-sensitive constraint data for goods depends heavily on the accuracy of the category query and the completeness of the database records. In a data comparison scenario, if the category of goods in the cold chain logistics order information is "fresh spinach leaves", the system will re-query the goods biological characteristics database using "fresh spinach leaves" as the keyword. The temperature-sensitive characteristic records returned by the query will contain different parameters for leafy green vegetables. For example, the record for "fresh spinach leaves" may show that its maximum temperature change rate is 1.0 degrees Celsius per hour (because it is more sensitive to rapid temperature changes and is prone to chilling injury), and the cumulative temperature change threshold is 5 degrees Celsius, thus generating a set of temperature-sensitive constraint data for goods that is completely different from that for "fresh salmon fillets".

[0033] Optionally, the calculation model for the cumulative temperature change threshold can be defined in the database records through explicit monitoring rules and calculation methods. Before extracting the final value of the "cumulative temperature change threshold," the system performs monitoring and calculation according to a unified standard. Specifically, the entire transportation cycle is divided into continuous and equally long monitoring periods at fixed time intervals. The fixed time intervals are preset based on the temperature-sensitive characteristics of the goods. Shorter monitoring intervals are used for highly sensitive goods, while standard monitoring intervals are used for ordinary temperature-sensitive goods. The duration of all monitoring periods remains consistent to ensure uniform statistical standards for single monitoring and multiple consecutive monitoring sessions. One calculation method for assessing the cumulative effect of temperature fluctuations is as follows:

[0034] Where: character A represents the theoretical cumulative temperature change value, character i represents the i-th monitoring period, character Ti represents the actual average temperature of the i-th monitoring period, character Ts represents the standard storage temperature of the goods, and the "cumulative temperature change threshold" stored in the goods biological characteristics database is the allowable upper limit of the theoretical cumulative temperature change value. In subsequent processes, the system will ensure that the theoretical cumulative temperature change value calculated according to the unified monitoring period does not exceed the allowable upper limit.

[0035] Optionally, for rare or novel product categories not predefined in the product biological characteristics database, the system can enable alternative mapping rules. For example, it can match a conservative default product temperature-sensitive characteristic record based on the product category entered by the user (such as "biological agents" or "high-end fruits and vegetables"), or prompt the user to manually enter relevant parameters to create a new database record, thereby ensuring the universality of the generated product temperature-sensitive constraint data.

[0036] In one embodiment of the invention, dynamic temperature control strategy data and cargo temperature-sensitive constraint data are integrated, and the final temperature control execution parameters are generated through a strategy adjustment algorithm. The target temperature of each transportation stage in the dynamic temperature control strategy data is compared with the maximum temperature change rate in the cargo temperature-sensitive constraint data. If the required rate of change for the target temperature difference between adjacent stages exceeds the maximum temperature change rate, a temperature buffer stage is inserted to reduce the temperature change rate. Based on the cumulative temperature change threshold in the cargo temperature-sensitive constraint data, the total theoretical temperature change value of the entire transportation route plan is checked. If it exceeds the threshold, the target temperatures of some stages in the dynamic temperature control strategy data are adjusted to smooth them out. The target temperature, duration, and temperature change rate limit of each stage, after adjustment and conforming to all constraints, are output as the final temperature control execution parameters.

[0037] In specific implementation, the cold chain logistics transport document printing method involves generating the final temperature control execution parameters. This process integrates dynamic temperature control strategy data and product temperature-sensitive constraint data through a strategy adjustment algorithm. A specific example scenario is processing an order for "fresh salmon slices". The maximum temperature change rate in the product temperature-sensitive constraint data is 0.5 degrees Celsius per hour, and the cumulative temperature change threshold is 3 degrees Celsius. The dynamic temperature control strategy data includes five transportation stages and their target temperatures.

[0038] In practice, the target temperature of each transportation stage in the dynamic temperature control strategy data was compared with the maximum temperature change rate in the cargo temperature-sensitive constraint data. The dynamic temperature control strategy data indicated that the target temperature for the second stage, "Nanjing transfer station operation stage," was 5 degrees Celsius, and the target temperature for the third stage, "Nanjing transfer station to Jinan transfer station transportation stage," was adjusted to 3 degrees Celsius. The original planned transition time between the two stages was 3 hours. The calculated required temperature change rate was |3°C-5°C| / 3 hours ≈ 0.67°C / hour. This rate exceeded the maximum temperature change rate of "fresh salmon slices," which was 0.5 degrees Celsius per hour. Therefore, the strategy adjustment algorithm inserted a temperature buffer stage between the second and third stages, extending the total time for the temperature to transition from 5 degrees Celsius to 3 degrees Celsius to 4 hours, reducing the temperature change rate to 0.5°C / hour, which met the constraint of the maximum temperature change rate.

[0039] The total theoretical temperature change value of the entire transportation route plan is verified based on the cumulative temperature change threshold in the cargo temperature-sensitive constraint data. The dynamic temperature control strategy data defines the deviation of the target temperature from the standard storage temperature by 2 degrees Celsius for each stage. The sum of the absolute values ​​of the temperature deviation values ​​of all stages is calculated. If the initially calculated total theoretical temperature change value is 3.8 degrees Celsius, this value exceeds the cumulative temperature change threshold of 3 degrees Celsius for "fresh salmon slices". The strategy adjustment algorithm then makes a smoother adjustment to the target temperature of some stages in the dynamic temperature control strategy data. For example, the target temperature of the fourth stage, "Jinan transfer station operation stage", is slightly adjusted from 3 degrees Celsius to 2.5 degrees Celsius. At the same time, the target temperature of a sub-stage of the fifth stage is adjusted from 4 degrees Celsius to 3.5 degrees Celsius. After recalculation, the total theoretical temperature change value can be reduced to 2.9 degrees Celsius, thus meeting the constraint of the cumulative temperature change threshold.

[0040] The final temperature control execution parameters are output as the target temperature, duration, and temperature change rate limit for each stage, after adjustment and compliance with all constraints. The final temperature control execution parameters are a structured data list. Each item in the list defines in detail the sequence number of a transportation stage, the start node, the end node, the target temperature value, the stage duration, and the maximum allowable temperature change rate limit for that stage. For example, the output includes complete information such as "Stage 1: Shanghai warehouse to Nanjing transit station, target temperature 5°C, duration 4 hours, temperature change rate limit 0.5°C / h", "Stage 2: Nanjing transit station operation, target temperature 5°C, duration 2 hours, temperature change rate limit N / A", and the newly added "Stage 2.5: Temperature buffer stage, target temperature linearly transitions from 5°C to 3°C, duration 1 hour, temperature change rate limit 0.5°C / h".

[0041] In some embodiments, the specific implementation of inserting a temperature buffer phase is to allocate a transition period in the original transportation route plan timeline by adjusting the operating parameters of the cold chain equipment. During this transition period, the cooling power and air supply frequency of the cold chain transportation or storage equipment are adjusted to make the ambient temperature change smoothly according to the maximum allowable temperature change rate of the goods, rather than by extending the node operation time or reducing the transportation speed. The start and end times of the buffer phase are clearly marked on the timeline of the transportation route plan data. The system limits the temperature change rate within the allowable range of the goods by controlling the temperature control output rhythm of the cold chain equipment. This buffer phase is incorporated as an independent logical phase into the final temperature control execution parameters for management.

[0042] It is understandable that the execution result of the strategy adjustment algorithm varies depending on the input data. In a data comparison scenario, if the product is changed to "frozen blueberries", the maximum temperature change rate in its product temperature-sensitive constraint data may be 2.0 degrees Celsius per hour, and the cumulative temperature change threshold may be 8 degrees Celsius. For the same dynamic temperature control strategy data, since the maximum temperature change rate limit is more lenient, the algorithm may determine that no temperature buffering stage needs to be inserted. At the same time, the total theoretical temperature change value of 3.8 degrees Celsius does not exceed the cumulative temperature change threshold of 8 degrees Celsius. Therefore, the dynamic temperature control strategy data may be directly used as the final temperature control execution parameter output without adjustment. This reflects the characteristic of the strategy adjustment algorithm to adaptively adjust according to the specific product constraints.

[0043] Optionally, the calculation of the time required for inserting the temperature buffer phase in the strategy adjustment algorithm can be based on an explicit formula, expressed as follows:

[0044] Where: character Δt buf The character T represents the duration of the temperature buffer phase that needs to be inserted. n The character T represents the target temperature for the nth transportation stage. n+1Represents the target temperature for the (n+1)th transportation stage, character V max The character Δt represents the maximum temperature change rate in the temperature-sensitive constraint data of the goods. orig This represents the original time allocated for the transition from stage n to stage n+1 in the original transportation route plan, when the calculated Δt buf When the value is greater than zero, the algorithm inserts a time period of Δt. buf The buffer phase.

[0045] Optionally, when smoothing out the target temperature of some stages in the dynamic temperature control strategy data, the algorithm can use an iterative approximation method. Under the premise of not exceeding the cumulative temperature change threshold, it prioritizes adjusting the target temperature of those stages where the temperature deviates from the standard value more or for a shorter period of time. After each fine adjustment, the total theoretical temperature change value is recalculated until it meets the condition of being less than or equal to the cumulative temperature change threshold. Finally, all adjusted stage parameters are output.

[0046] In one embodiment of the present invention, based on the final temperature control execution parameters and transportation route plan data, a formatted template from the transport order template library is called to fill and generate the structured transport order data to be printed. According to the business type in the cold chain logistics order information, a matching preset template is selected from the transport order template library. The text information in the cold chain logistics order information and transportation route plan data, as well as the numerical information in the final temperature control execution parameters, are filled one-to-one according to the predefined field placeholders in the preset template. This generates the structured transport order data to be printed, containing all the filled content, data structure, and format. Key monitoring fields are extracted from the structured transport order data to be printed, and unique QR code graphic encoding data is generated based on these key monitoring fields. Key fields for transportation process tracking and temperature verification are selected from the structured transport order data to be printed, including the unique waybill number, the sequence of main transit node numbers, and the target temperature value for each stage. The selected key fields are serialized and concatenated into a one-dimensional data string according to preset encoding rules. A QR code generation algorithm is used to convert the one-dimensional data string into dot matrix data corresponding to the QR code image, i.e., QR code graphic encoding data.

[0047] In specific implementation, the embodiment of the cold chain logistics transport document printing method involves generating structured data and QR code graphic encoding data of the transport document to be printed. A specific example scenario is to complete the filling and encoding of the transport document based on the cold chain logistics order information of the "fresh salmon slices" order, the transportation route plan data, and the final temperature control execution parameters.

[0048] In practice, based on the final temperature control execution parameters and transportation route plan data, the system calls a formatted template from the transport order template library. According to the business type "fresh food delivery" in the cold chain logistics order information, the system selects a preset template named "Fresh Food Standard Template" from the transport order template library. This preset template is a formatted document that defines field positions, font styles, and table structures, containing elements such as... " " Predefined field placeholders, such as "...", are used to fill in the text information in the cold chain logistics order information and transportation route plan data, as well as the numerical information in the final temperature control execution parameters, according to the predefined field placeholders in the preset template. For example, "Delivery address: No. xx, xxx Road, Haidian District, Beijing" in the cold chain logistics order information is filled into the "..." field placeholders. The placeholder text in the transportation route planning data, "Passing through nodes: Shanghai warehouse, Nanjing transit station, Jinan transit station, Tianjin cold chain checkpoint, Beijing main warehouse, Beijing Haidian distribution station," was filled into the placeholder text. The placeholder position was filled in the final temperature control execution parameters where "Stage 1 target temperature: 5℃" was filled with "Placeholder". "Placeholder position. After the system performs the fill operation, it generates a structured data of the shipping order to be printed, which contains all text and numerical content and has a specific data structure and format. This data is an internal representation that includes the logical coordinates, content and style attributes of each data element on the page."

[0049] In practice, key monitoring fields for tracking the transportation process and verifying temperatures are selected from the structured data of the transport order to be printed. These key monitoring fields include the unique waybill number "SF20260328123456", the sequence of major transit node numbers "SHA-NKG-TNA-BJS", and the target temperature values ​​for each stage "5.0, 5.0, 3.0, 2.5, 3.5". The selected key fields are serialized and concatenated into a one-dimensional data string according to a preset encoding rule. The preset encoding rule stipulates that fields are separated by a vertical bar "|", and temperature sequences are separated by commas ",". The concatenated one-dimensional data string is "SF20260328123456|SHA-NKG-TNA-BJS|5.0, 5.0, 3.0, 2.5, 3.5". The one-dimensional data string "SF20260328123456|SHA-NKG-TNA-BJS|5.0,5.0,3.0,2.5,3.5" is converted into dot matrix data corresponding to a QR code image using a QR code generation algorithm. This process calls a standard QR code generation library, specifies the error correction level as M, and generates a QR code dot matrix data of version 5.

[0050] In some embodiments, the transport order template library stores a variety of preset templates, which the system selects based on the business type, such as a "biological preparation-specific template" or a "general template for bulk commodities." These different preset templates have different sets of field placeholders and page layouts to adapt to the information display needs of different business scenarios, but the field filling logic is consistent with the data generation logic. See Table 1.

[0051] Table 1: Field Mapping Table

[0052] It is understandable that the content of the QR code graphic encoding data depends on the key monitoring fields selected from the structured data of the transport order to be printed. In the data comparison scenario, if the unique number of the transport order changes to "JD202603280001", or if nodes are added to the transport route, the content of the selected fields will be different, and the one-dimensional data string concatenated according to the same encoding rules will also change accordingly, for example, becoming "JD202603280001|SHA-NKG-JNZ-TNA-BJS|5.0,5.0,3.0,2.5,3.5". The dot matrix image pattern corresponding to the final generated QR code graphic encoding data will also be completely different.

[0053] Optionally, the encoding rules for serializing and concatenating the selected key fields into a one-dimensional data string can adopt a more compact format. For example, binary encoding can be used for certain fixed-length fields to shorten the total string length. One encoding formula for generating a compact string is as follows:

[0054] Where: character S represents the final generated serialized one-dimensional data string; character B represents the substring after Base32 encoding of the unique waybill number; character P represents the substring after Huffman-coded compression of the sequence of transit node numbers; character L represents the substring directly represented by concatenating the target temperature values ​​of each stage without compression; and character... This represents the string concatenation operator. Using this formula, one-dimensional data strings with higher information density can be generated, resulting in smaller QR code images.

[0055] Optionally, when generating QR code graphic encoding data, the QR code version and size can be automatically selected based on the length of the one-dimensional data string. The system calculates the byte length of the string and then determines the minimum version number and corresponding module size that can accommodate the data volume according to the QR code standard lookup table, thereby optimizing the printing size and scannability of the QR code graphic while ensuring data capacity.

[0056] See Figure 4This is a multi-dimensional radar chart of the cold chain logistics transportation stages, used to display the core temperature control monitoring indicators of the Jinan transit station. Dwell time accounts for the highest proportion among all dimensions, indicating that this transit station is a crucial long-term node in cold chain transportation. Deviation threshold accounts for the second highest proportion, indicating that this stage requires high precision in temperature control. The close proximity of the target temperature and actual temperature indicates good temperature control performance. The low proportion of temperature change rate meets the requirement of gradual temperature changes in cold chain transportation. The high degree of match between the actual and target temperatures, and the low temperature change rate, indicate that the temperature control strategy at this stage is effective and meets the temperature-sensitive constraints of the goods. The temperature deviation is far below the deviation threshold, indicating that the temperature control risk at this stage is controllable and does not threaten the quality of the goods. The relatively long dwell time can serve as a key focus for subsequent optimization of transportation efficiency, such as shortening the dwell time by optimizing loading and unloading processes or equipment scheduling.

[0057] In one embodiment of the present invention, QR code graphic encoding data is embedded at a designated position in the structured data of the transport document to be printed, forming complete transport document page data with QR code. In the page layout corresponding to the structured data of the transport document to be printed, the coordinates of a rectangular area reserved for the QR code are located. The dot matrix image represented by the QR code graphic encoding data is scaled and aligned proportionally according to the size of the rectangular area coordinates. The scaled and aligned QR code dot matrix image is layer-composite with the page image data of the structured data of the transport document to be printed, generating complete page image data containing visual text information and QR code graphics. Based on the hardware characteristics parameters of the printing device, the resolution adaptation and layout fine-tuning of the complete transport document page data with QR code are performed to generate final print driver data. The resolution, paper size, and print margin parameters supported by the target printing device are obtained. Based on the resolution supported by the printing device, the complete transport document page data with QR code is resampled. Based on the paper size and print margin parameters, the absolute position and relative layout of all content in the complete transport document page data are adjusted to adapt the content to the target paper, generating page description language data that can directly drive the printing device as the final print driver data.

[0058] In specific implementation, the embodiment of the cold chain logistics transport document printing method involves forming complete transport document page data with QR code and generating final print driving data. A specific example scenario is to perform page synthesis and device adaptation based on the generated transport document structured data to be printed and the QR code graphic encoding data.

[0059] In practice, the QR code graphic encoding data is embedded in a designated location within the structured data of the transport document to be printed. The page layout corresponding to the structured data of the transport document to be printed defines a rectangular area coordinate reserved for the QR code. This coordinate system takes the top left corner of the page as the origin, with the top left corner coordinates being (150 mm, 200 mm) and the bottom right corner coordinates being (190 mm, 240 mm). The system locates this rectangular area coordinate. The dot matrix image represented by the QR code graphic encoding data is scaled and aligned proportionally according to the size of the rectangular area coordinate. The original QR code graphic encoding data corresponds to a standard QR code image of a 40x40 module. The system calculates the width and height of the rectangular area coordinate to be 40 mm. Based on the target printing resolution (e.g., 300 dots per inch), the rectangular area size is converted into pixel values. Then, the scaling factor required to scale the original 40x40 module image to the target pixel size is calculated, and the center of the scaled QR code image is aligned with the center of the rectangular area coordinate. The scaled and aligned QR code dot matrix image is combined with the page image data of the structured data of the transport document to be printed. The page image data of the structured data of the transport document to be printed contains all text, table and border information. The system overlays the QR code dot matrix image as an independent layer on the corresponding coordinate position of the page image data in memory to generate complete page image data that contains both visible text information and QR code graphics.

[0060] In practice, the resolution and layout of the complete shipping manifest data with QR codes are adapted based on the hardware characteristics of the printing device. The system determines that the target printing device supports a resolution of 300 dots per inch (dP), a paper size of A4 (210 mm x 297 mm), and printing margins of 10 mm on each side. The complete shipping manifest data with QR codes is then resampled based on the 300 DP resolution supported by the printing device. Since the logical resolution of the complete page image data may be 200 DP, the resampling algorithm uses bilinear interpolation to upsample the image data to 300 DP to ensure image clarity on the target printing device. Based on the A4 paper size and a 10mm printing margin parameter, the system adjusts the absolute position and relative layout of all content in the complete shipping single-page data. The system calculates a rectangular area of ​​190mm x 277mm that can be printed. The system scales and translates the overall bounding box of all elements (including text blocks, tables, and QR codes) in the complete page image data to adapt the overall content to the printable area of ​​the target paper. Finally, it generates page description language data that can directly drive the printing device as the final print driving data, such as generating an instruction stream in HPPCL or PDF format suitable for the target printer.

[0061] In some embodiments, when scaling the dot matrix image represented by the QR code graphic encoding data proportionally, the scaling factor is calculated according to a geometric transformation formula to ensure that the image is not distorted:

[0062] Where: character K represents the final scaling factor used, character W rect The width (in pixels) of the rectangular area reserved for the QR code, represented by the character H. rect This represents the height (in pixels) of the rectangular area reserved for the QR code, represented by the character W. qr The width (in pixels) of the original dot matrix image corresponding to the QR code graphic encoding data, represented by the character H. qr This represents the height (in pixels) of the original dot matrix image corresponding to the QR code graphic encoding data. The system uses the width scaling ratio. With height scaling ratio The smaller value in the matrix is ​​used as character K, and character K is used to scale the original bitmap image proportionally.

[0063] It is understandable that the generation of the final print driver data depends entirely on the hardware characteristics of the target printing device. In a data comparison scenario, if the target printing device is replaced with a portable device that supports thermal printing, its hardware characteristics may be 203 dots per inch resolution, 80 mm wide roll paper size, 2 mm left and right margins, and no top or bottom margins. The system will acquire these new parameters and re-execute the adaptation process. Based on the 203 dots per inch resolution, the complete shipping document page data will be resampled, and the layout of all content will be recalculated based on the narrow paper size and different margin parameters. The final generated page description language data will be the ESC / POS instruction set format suitable for this thermal printer, thereby driving different devices to output physical shipping documents adapted to their paper.

[0064] Optionally, when adjusting the layout based on paper size and printing margin parameters, the system can adopt a content-box-based relative positioning strategy, treating all page content as a logical content box, calculating the bounding rectangle of the content box in the current layout, then calculating the transformation matrix required to translate and scale this bounding rectangle to fit the target printable area, and applying this transformation matrix to each page element (including the position of each character, the coordinates of each line, and the coordinates of the QR code image) to achieve overall layout fine-tuning.

[0065] Optionally, when generating the final print driver data, the selection of the page description language is automatically determined by the system based on the model of the printing device. The system maintains a mapping table between the printing device model and the supported page description languages ​​(such as PostScript, PCL, PDF, ESC / POS). After obtaining the target printing device model, the system queries the mapping table and calls the corresponding driver generation module to convert the adapted page image data into a specific instruction sequence that can be parsed by the device.

[0066] See Figure 5 This is a chart analyzing the temperature-sensitive characteristics of goods, used to compare two core temperature-sensitive constraints for different product categories in cold chain transportation. It clearly quantifies the temperature-sensitive characteristics of different product categories, providing data for transportation prioritization, resource allocation, and risk assessment. Product categories with low temperature change rates but high cumulative temperature change thresholds require more precise segmented temperature control during long-distance transportation to prevent quality degradation due to the accumulation of small temperature deviations over extended periods. When generating cold chain transport manifests, the corresponding temperature control constraint parameters should be automatically filled in based on the temperature-sensitive characteristics of different product categories. For example, stricter temperature change rate limits and more frequent temperature monitoring points should be set for fresh food.

[0067] In one embodiment of the present invention, in a practical application scenario, the present invention relies on integrated IoT devices to construct a temperature data acquisition and processing system for the entire cold chain process. This system exhibits rich application forms in specific implementations. Temperature measuring terminals are deployed in cold chain-related scenarios such as insulated boxes, freezers, refrigerated trucks, and cold storage facilities. They acquire temperature data from each measuring point through deployed gateways, Bluetooth, or WiFi communication methods. The temperature measuring terminals themselves have edge data caching capabilities. When the wireless signal is unstable, they can store the raw temperature data locally and transmit it after the signal is restored, thereby improving the reliability of data acquisition. After the gateway acquires the data, it transmits it to the server. The server applies the data to different business scenarios based on a series of judgment rules. For example, insulated boxes are used for temperature monitoring in conventional logistics transportation, freezers and cold storage facilities mainly serve the temperature recording of the monitoring system, and refrigerated trucks simultaneously undertake the functions of logistics transportation and temperature monitoring. Through this deployment method, the monitoring data of the entire cold chain process is integrated to form a complete temperature data chain. When consuming this data, it can be processed and displayed according to different business needs. Taking the large-scale transportation of pharmaceuticals as an example, the system requires continuous temperature data and cargo location information. From the moment the customer loads the vehicle to the final delivery, the system records temperature changes and cargo location throughout the entire process. Since the cargo may flexibly pass through different warehouses, change vehicles, or change routes, the system needs to automatically integrate the temperature data from these different scenarios to generate a complete report. This report can be generated in PDF format or printed on-site and shared with the customer for their records. Throughout this process, the collection, transmission, processing, and display of temperature data are all based on the technical solution of this invention, ensuring the integrity and usability of the data.

[0068] Furthermore, the deployment of temperature measurement terminals covers all aspects of cold chain transportation, ensuring comprehensive temperature data collection. The introduction of gateways makes data transmission more stable and reliable, guaranteeing data integrity even in high-interference environments. Server-side judgment rules classify and process data according to business needs, enabling the data to better serve different application scenarios. For example, in pharmaceutical transportation, the system needs to ensure the traceability of temperature data throughout the entire process; therefore, it records and integrates the data in detail, ultimately generating a temperature report for customer retention. This report not only includes temperature data but may also include cargo location information, providing customers with a comprehensive cold chain transportation record. The technical solution of this invention demonstrates flexibility and adaptability in practical applications, providing customized temperature data printing and sharing solutions based on different cold chain scenarios and business needs. By integrating IoT devices and edge computing capabilities, it achieves refined temperature monitoring of the entire cold chain process, providing technical support for the efficient operation of cold chain logistics.

[0069] In practical implementation, temperature monitoring terminals inside insulated boxes can monitor the internal temperature in real time. If the temperature exceeds a preset range, the terminal can locally cache the data and mark it as abnormal, then synchronize it to the gateway after the signal is restored. Temperature monitoring terminals in freezers and cold storage continuously record changes in ambient temperature, providing a basis for temperature control management in the warehousing process. Temperature monitoring terminals on refrigerated trucks simultaneously collect the temperature inside the truck during transportation, and combine this with vehicle location information to form a correlation between transportation trajectory and temperature changes. The gateway, acting as a data relay hub, needs to adapt to multiple communication protocols to ensure stable data access from different terminals and forward the data to the server via wired or wireless means. After receiving the data, the server classifies and processes it according to preset business rules, such as distinguishing between logistics transportation data, warehouse monitoring data, and vehicle transportation data, and then calls the corresponding processing logic according to the needs of different business scenarios. In the pharmaceutical transportation scenario, the system continuously collects temperature data inside insulated boxes or refrigerated trucks from the loading stage, and combines this with the vehicle's GPS location information to record the temperature status of the goods at different times and locations. When goods need to be transferred to different vehicles or routes, the system automatically links data from the preceding and following transportation stages to ensure the continuity of temperature records. Before the goods arrive at the delivery stage, the system automatically extracts and integrates temperature data from the garage, vehicles, and other environments the goods have experienced throughout the transportation process, generating a complete report that includes a timeline, temperature curves, and location information. This report can be exported as a PDF for customers to view or download online, or it can be printed directly at the delivery site for customer records and future traceability.

[0070] The edge data caching mechanism of the temperature measurement terminal plays a crucial role in areas with weak signals, such as underground parking garages, remote road sections, or areas with strong signal interference. The terminal can temporarily store temperature data in local storage and upload it immediately after the signal is restored, avoiding data loss due to communication interruptions. During data transmission, the gateway performs data verification and compression to ensure transmission efficiency while reducing data volume, adapting to transmission needs in different network environments. When processing data, the server dynamically adjusts its data processing strategy based on different business scenarios. For example, in warehouse monitoring scenarios, the focus is on temperature stability and abnormal alarms; in logistics and transportation scenarios, the focus is on the continuity of temperature changes and location correlation. By integrating data from different scenarios, the system can provide users with a more comprehensive view of cold chain temperature management, meeting diverse business needs. In the report generation stage, the system automatically extracts key data, such as start time, end time, highest temperature, lowest temperature, average temperature, and location changes, and formats it according to a preset template to generate a clear and complete temperature data report. Whether exported as a PDF or printed on-site, the report accurately reflects the temperature status of goods throughout the entire transportation process, supporting customers' quality control and compliance audits.

[0071] Throughout the process, the collection, transmission, processing, integration, and report generation of temperature data are closely linked, forming a complete closed loop for cold chain temperature data management. The implementation of this invention not only covers the main scenarios of cold chain logistics but also achieves precise responses to different business needs through flexible business rules and multi-scenario data fusion. In practical applications, users can configure different terminal deployment schemes, data processing rules, and report templates according to their own business characteristics, enabling the system to better adapt to their business processes. For example, in the food cold chain scenario, the focus may be more on the temperature fluctuation range and duration; in the pharmaceutical cold chain scenario, the emphasis is on temperature compliance and data traceability. The solution of this invention, through modular design and scalable architecture, can meet the cold chain temperature data management needs of different industries and scenarios, providing technical support for the standardized and information-based operation of cold chain logistics. By deploying integrated IoT devices, a comprehensive temperature monitoring network covering insulated boxes, freezers, refrigerated trucks, and cold storage is built. Combined with edge computing and data caching capabilities, reliable data collection and stable transmission are achieved. Then, through intelligent processing on the server side and multi-scenario data fusion, customized temperature data reports that meet business needs are finally generated, providing a data foundation for the whole process management of cold chain logistics.

[0072] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for printing cold chain logistics transport documents, characterized in that, include: Receive raw business data containing cold chain logistics order information, and generate dynamic temperature control strategy data based on the raw business data. The cold chain logistics order information includes product category, delivery address, and delivery time requirements. Based on the product category, query the corresponding product biological characteristics database to obtain the maximum allowable temperature change rate and cumulative temperature change threshold of the product, and generate product temperature-sensitive constraint data. By integrating dynamic temperature control strategy data and product temperature-sensitive constraint data, the final temperature control execution parameters are generated through a strategy adjustment algorithm; Based on the final temperature control execution parameters and transportation route plan data, the formatted template in the transportation order template library is called to fill in and generate the structured data of the transportation order to be printed; Extract key monitoring fields from the structured data of the transport order to be printed, and generate unique QR code graphic encoding data based on the key monitoring fields; The QR code graphic encoding data is embedded in a specified position in the structured data of the shipping document to be printed, forming a complete shipping document page with a QR code; Based on the hardware characteristics of the printing equipment, the resolution of the complete shipping single page data with QR code is adapted and the layout is fine-tuned to generate the final printing driver data. The final print drive data is sent to the designated printing device, which then outputs the physical shipping document.

2. The method for printing cold chain logistics transport documents according to claim 1, characterized in that, Dynamic temperature control strategy data is generated based on the original business data, including: Receive raw business data containing cold chain logistics order information. The cold chain logistics order information is processed for cold chain routing planning to generate transportation route plan data that includes the nodes to be passed, the temperature requirements of the nodes, and the expected dwell time. Based on the transit nodes in the transportation route plan data, query the corresponding static cold chain equipment information, and combine it with the delivery time requirements to generate dynamic temperature control strategy data; The process of cold chain routing planning for the cold chain logistics order information includes: Parse the delivery address in the cold chain logistics order information and match it with standardized address codes in the geographic information service; Based on standardized address coding, the initial path from the starting point to the destination is calculated using the shortest path algorithm in the pre-set cold chain transit node network. Based on the product category and delivery time requirements, necessary refrigerated transfer stations and checkpoints are inserted into the initial route to form transportation route plan data containing multiple mandatory and optional nodes.

3. The method for printing cold chain logistics transport documents according to claim 2, characterized in that, The process of querying the corresponding static cold chain equipment information based on the transit nodes in the transportation route plan data, and generating dynamic temperature control strategy data in conjunction with delivery time requirements, includes: Access the cold chain equipment information database to obtain the model, rated temperature range, and temperature control accuracy parameters of the refrigerated equipment or transportation vehicles equipped at each node in the transportation route plan data; Based on delivery time requirements, the entire transportation process is divided into multiple transportation stages according to time. For each transportation stage, based on the equipment parameters of the nodes it passes through, the recommended target temperature and allowable temperature fluctuation bandwidth for the transportation stage are calculated and set, and the temperature control settings of all stages are summarized to form dynamic temperature control strategy data.

4. The method for printing cold chain logistics transport documents according to claim 1, characterized in that, The step involves querying the corresponding biological characteristic database of the goods based on the product category to obtain the maximum allowable temperature change rate and cumulative temperature change threshold of the goods, and generating temperature-sensitive constraint data for the goods, including: Based on the product category in the cold chain logistics order information, retrieve predefined product temperature-sensitive characteristic records from the product biometric database; Quantitative parameters characterizing the goods' ability to withstand temperature changes are extracted from the goods' temperature-sensitive characteristic records. These include the maximum allowable temperature change per unit time as the maximum temperature change rate, and the upper limit of the total allowable temperature deviation from the standard value throughout the entire transportation cycle as the cumulative temperature change threshold. The maximum temperature change rate and the cumulative temperature change threshold are encapsulated as temperature-sensitive constraint data for goods.

5. The method for printing cold chain logistics transport documents according to claim 1, characterized in that, The fusion of dynamic temperature control strategy data and product temperature-sensitive constraint data generates the final temperature control execution parameters through a strategy adjustment algorithm, including: Compare the target temperature of each transportation stage in the dynamic temperature control strategy data with the maximum temperature change rate in the cargo temperature-sensitive constraint data. If the rate of change required for the target temperature difference between adjacent stages exceeds the maximum temperature change rate, a temperature buffer stage is inserted to reduce the temperature change rate. Based on the cumulative temperature change threshold in the temperature-sensitive constraint data of the goods, the total theoretical temperature change value of the entire transportation route plan is checked. If it exceeds the threshold, the target temperature of some stages in the dynamic temperature control strategy data is adjusted to smooth it out. The target temperature, duration, and temperature change rate limits for each stage, adjusted and conforming to all constraints, are output as the final temperature control execution parameters.

6. The method for printing cold chain logistics transport documents according to claim 1, characterized in that, Based on the final temperature control execution parameters and transportation route plan data, the system calls a formatted template from the transportation order template library to fill in and generate structured transportation order data to be printed, including: Based on the business type in the cold chain logistics order information, select a matching preset template from the transport order template library; Fill in the text information in the cold chain logistics order information and transportation route plan data, as well as the numerical information in the final temperature control execution parameters, one by one according to the predefined field placeholders in the preset template; Generates structured data for the shipping order to be printed, including all filler content, data structure, and formatting.

7. The method for printing cold chain logistics transport documents according to claim 6, characterized in that, The process of extracting key monitoring fields from the structured data of the transport document to be printed, and generating unique QR code graphic encoding data based on these key monitoring fields, includes: From the structured data of the waybill to be printed, key fields for transportation process tracking and temperature verification are selected, including the unique waybill number, the sequence of numbers of major transit nodes, and the target temperature value at each stage. The selected key fields are serialized and concatenated into a one-dimensional data string according to preset encoding rules; The one-dimensional data string is converted into dot matrix data corresponding to the QR code image using a QR code generation algorithm, i.e., QR code graphic encoding data.

8. The method for printing cold chain logistics transport documents according to claim 1, characterized in that, The step of embedding the QR code graphic encoding data into a specified position in the structured data of the transport document to be printed, forming a complete transport document page with a QR code, includes: In the page layout corresponding to the structured data of the transport order to be printed, locate the coordinates of the rectangular area reserved for the QR code; The dot matrix image represented by the QR code graphic encoding data is scaled and aligned proportionally according to the size of the rectangular area coordinates. The scaled and aligned QR code dot matrix image is layered with the page image data of the structured data of the shipping document to be printed, generating complete page image data containing visual text information and QR code graphics.

9. The method for printing cold chain logistics transport documents according to claim 8, characterized in that, The process involves adapting the resolution and fine-tuning the layout of the complete shipping single-page data with QR codes based on the hardware characteristics of the printing device, generating the final print driver data, including: Obtain the resolution, paper size, and print margin parameters supported by the target printing device; Based on the resolution supported by the printing equipment, the complete shipping manifest data with QR code is resampled to ensure image clarity; Based on the paper size and printing margin parameters, the absolute position and relative layout of all content in the complete shipping single-page data are adjusted to adapt the content to the target paper, generating page description language data that can directly drive the printing device as the final printing drive data.

10. A cold chain logistics transport document printing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the cold chain logistics transport document printing method as described in any one of claims 1 to 9.

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