Self-adaptive cold chain equipment data transmission method

Abstract

The invention relates to the technical field of data transmission, in particular to a self-adaptive cold chain equipment data transmission method. Comprising the following steps: opening a serial port corresponding to the cold chain equipment and setting a connection parameter in a communication initialization stage, sending a metadata request packet in a metadata acquisition stage, and receiving and analyzing the total data packet number and the total record number returned by the equipment, in the data packet request stage, data requests are circularly sent according to packet serial numbers, in the data receiving and processing stage, data are read, integrity verification is executed, and the method further comprises the steps that dynamic buffer area monitoring is conducted, and the change of the data size of the buffer area is detected in real time to replace fixed delay waiting; intelligent waiting adjustment: stopping waiting and setting a maximum monitoring period when no new data is continuously detected for multiple times; accurate retransmission: when a single packet is wrong, positioning the serial number of a problem packet, only retransmitting a failed data packet, and simultaneously retaining a successfully received data packet; according to the invention, the inherent defects of a fixed delay mechanism can be solved.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and more specifically to an adaptive data transmission method for cold chain equipment. Background Technology

[0002] In the field of cold chain equipment data transmission, the real-time performance and integrity of data are crucial for equipment monitoring and operational decisions. Current technologies often rely on fixed-delay mechanisms for data transmission between cold chain equipment and host computers: after sending a data request, the system waits for a preset fixed duration before reading the returned data to ensure complete data reception. However, this fixed-delay strategy has significant drawbacks. In cold chain scenarios, the data response speed of equipment fluctuates greatly due to factors such as ambient temperature and electromagnetic interference: when equipment is in ultra-low temperature environments or areas with strong electromagnetic interference, chip processing performance degrades, data response delays increase, and the fixed delay may result in incomplete data packets due to insufficient duration; conversely, in stable environments, the fixed delay can lead to low transmission efficiency due to excessive waiting time, especially in scenarios with large data volumes, where accumulated waiting time can severely slow down the overall transmission process.

[0003] The aforementioned problems make it difficult for existing transmission methods to achieve a balance between data integrity and transmission efficiency. There is an urgent need for a transmission scheme that can dynamically adapt to the device response state in order to solve the inherent defects of the fixed delay mechanism. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an adaptive cold chain equipment data transmission method, comprising: The communication initialization phase involves opening the serial port corresponding to the cold chain equipment and setting connection parameters; the metadata acquisition phase involves sending metadata request packets, receiving and parsing the total number of data packets and total number of records returned by the equipment; the data packet request phase involves sending data requests cyclically according to the packet sequence number; and the data reception and processing phase involves reading data and performing integrity checks. It also includes: Dynamic buffer monitoring detects changes in buffer data volume in real time to replace fixed-delay waiting; Intelligent waiting adjustment: terminates the waiting and sets the maximum monitoring period when no new data is detected after multiple consecutive checks. Precise retransmission locates the sequence number of the problematic packet when a single packet error occurs and only retransmits the failed data packet while retaining the successfully received data packets.

[0005] Preferably, the communication initialization phase also includes adaptive configuration of serial communication baud rate, data bits, stop bits and parity method. During the configuration process, the parameter compatibility is verified by sending test data packets. If multiple tests fail consecutively, the system automatically switches to a preset backup parameter combination to retry the connection.

[0006] In a further preferred embodiment, the metadata acquisition stage also includes verifying the total number of returned data packets and the total number of records using a checksum. The checksum is calculated using a hash algorithm. If the verification fails, multiple retransmissions of the metadata request packets are performed first. Only if the retransmissions still fail is a global termination and error reporting process executed.

[0007] In a further preferred embodiment, the packet sequence number management adopts a two-byte circular counting mechanism. When the low-order byte overflows, it triggers the carry of the high-order byte. The high-order byte is set with a limited range. When the high-order byte reaches the upper limit and the low-order byte overflows again, the high-order byte is reset to the initial value and the loop count is recorded. The loop count is used as an auxiliary parameter for data integrity verification.

[0008] Furthermore, an interference-temperature coupling coefficient is introduced during data transmission to quantify the impact of low-temperature environments on transmission performance. The formula for calculating the coupling coefficient is as follows: ; in, The interference-temperature coupling coefficient, Indicates the intensity of the ambient electromagnetic field. Indicates the chip junction temperature. The electromagnetic susceptibility coefficient, This is the temperature compensation reference value. Temperature is a factor that affects the environment. This is the chip's reference operating temperature.

[0009] A further preferred method is to adjust the data packet fragment size based on the coupling coefficient. The fragment size calculation formula is as follows: ; in, For packet fragment size, Maximum slice size, The preset baseline coupling coefficient is used when At that time, the fragment size remains at 1. , The interference-temperature coupling coefficient is currently calculated.

[0010] In a further optimized approach, the number of retransmissions is dynamically determined based on the coupling coefficient, and the calculation formula is as follows: ; in, For the number of retransmissions, The interference-temperature coupling coefficient is currently calculated. This is the retransmission coefficient. This indicates a rounding up operation. If the result is less than 1, the number of retransmissions will be forced to be 1.

[0011] Further preferred, the integrity verification during the data reception and processing stage adopts a multi-layer verification mechanism. The first layer is CRC16 cyclic redundancy check, the second layer is data length matching check, and the third layer is key field range check. Key fields include temperature value, humidity value and equipment operating status code. Each layer of verification is set with an independent failure threshold. When any layer of verification fails multiple times in a row, the corresponding data packet retransmission process is triggered.

[0012] In a further preferred embodiment, the buffer monitoring adopts sliding window mean filtering. The size of the filtering window is a preset number of continuous detection cycles, and each detection cycle is a preset duration. The data growth status is judged by the change rate of the filtered buffer data volume. The change rate is calculated by the ratio of the difference between the current data volume and the data volume of the previous cycle to the data volume of the previous cycle. When the change rate is less than the preset value multiple times in a row, it is determined that there is no new data.

[0013] Further preferred embodiments include a dynamic evaluation step for transmission performance. Evaluation metrics include data packet reception success rate, average transmission latency, and data integrity score. The success rate is the ratio of the number of successfully received data packets to the total number of requested data packets. The average transmission latency is the average time from sending a request to receiving a data packet. The integrity score is the weighted sum of the number of fields that pass verification and the total number of fields. The weights are pre-configured based on the importance of the fields. When any metric fails to meet the preset threshold for multiple consecutive monitoring periods, the transmission parameter optimization process is automatically initiated.

[0014] Technical effects: This invention effectively solves the core problems of existing fixed-delay mechanisms through innovative technologies such as dynamic buffer monitoring, intelligent waiting adjustment, and precise retransmission. Dynamic buffer monitoring replaces fixed delay, adapting to data transmission status in real time; intelligent waiting adjustment balances data integrity and transmission efficiency by terminating waiting after no new data is added and by setting a maximum period constraint; precise retransmission only operates on failed data packets, reducing resource waste. The synergistic effect of these technologies completely overcomes the shortcomings of fixed delay in adapting to environmental fluctuations, significantly improving the adaptability and reliability of cold chain data transmission. Attached Figure Description

[0015] Figure 1 This is a flowchart of the adaptive cold chain equipment data transmission method of this application. Detailed Implementation

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] Traditional cold chain equipment data transmission suffers from the following technical problems: First, the fixed delay waiting mechanism cannot be dynamically adjusted according to the actual data transmission situation, which can easily lead to excessively long waiting times that reduce transmission efficiency or excessively short waiting times that cause data loss. Second, error retransmission usually adopts a full retransmission mode, which requires retransmission of all data packets even if only a single packet is faulty, resulting in a waste of bandwidth and time resources. Third, buffer monitoring lacks real-time performance and intelligent judgment, making it difficult to accurately identify the data transmission status and affecting the integrity of data reception.

[0018] Based on this, please refer to Figure 1 This embodiment provides an adaptive cold chain equipment data transmission method, including a dynamic buffer monitoring step, an intelligent waiting adjustment step based on the buffer state, and a precise retransmission step under error isolation. The dynamic buffer monitoring step detects changes in the amount of data in the buffer in real time to replace fixed delay waiting. The intelligent waiting adjustment step terminates the waiting when no new data is detected for several consecutive times and sets a maximum monitoring period. The precise retransmission step locates the sequence number of the problematic packet when a single packet error occurs and only retransmits the failed data packet while retaining the successfully received data packet.

[0019] In this scheme, "consecutive" is defined as three or more consecutive times, and it is linked to the maximum monitoring period. In the intelligent waiting mechanism, if no new data is added in three consecutive checks and the maximum monitoring period has not been reached, the waiting will be terminated early; if no new data is added in three consecutive checks but the maximum period has been reached, the waiting will also be forcibly terminated.

[0020] This technical solution forms a collaborative mechanism through three core improvements: the dynamic buffer monitoring step breaks away from the mechanical nature of traditional fixed delays, directly reflecting the real-time status of data transmission by tracking changes in the amount of data in the buffer in real time, providing accurate basis for subsequent waiting and adjustment; the intelligent waiting and adjustment step sets termination conditions and a maximum monitoring period for multiple consecutive periods without new data, avoiding the waste of resources in infinite waiting and ensuring that no data is missed due to premature termination; the precise retransmission step adopts an error isolation strategy, retransmitting only problematic packets, completely abandoning the inefficient mode of full retransmission, while retaining successful data packets to maintain transmission continuity.

[0021] The solution achieves significant technical benefits: dynamic buffer monitoring enables on-demand adjustment of the waiting mechanism, resolving the mismatch between fixed latency and actual transmission rhythm; intelligent waiting adjustment, through dual constraints—including no new data and a maximum period—balances data integrity and transmission efficiency, avoiding invalid waiting or data loss; precise retransmission significantly reduces the amount of retransmitted data, lowering bandwidth consumption and transmission time, while retaining successful data to ensure the continuity of the transmission process. The combination of these three features makes cold chain equipment data transmission environmentally adaptable and resource-efficient, particularly suitable for the characteristics of unstable data bursts and complex transmission environments in cold chain scenarios.

[0022] In traditional serial communication for cold chain equipment, parameter configuration has significant limitations: First, it uses fixed parameter combinations, such as fixed baud rate and data bits, which cannot adapt to different equipment models or dynamically changing transmission environments, resulting in poor equipment compatibility; second, it lacks a parameter verification mechanism, and directly enters the data transmission stage after configuration. If the parameters do not match, it will directly lead to communication failure, and there is no effective retry strategy after failure, requiring manual intervention and adjustment, which affects the continuity of transmission.

[0023] Based on this, the communication initialization phase of the adaptive cold chain equipment data transmission method also includes adaptive configuration of serial communication baud rate, data bits, stop bits and check mode. During the configuration process, the parameter adaptability is verified by sending test data packets. If multiple tests fail consecutively, the system automatically switches to a preset backup parameter combination to retry the connection.

[0024] In this scheme, "consecutive failures" is defined as three or more consecutive test failures. During the serial port parameter adaptive configuration process, the system sends three consecutive test data packets for the current parameter combination, such as a baud rate of 9600 and 8 data bits. If no valid response is received for any of these, the parameter compatibility verification fails, and this is considered a series of consecutive failures. The system then automatically switches to a backup parameter combination. This number of failures can be dynamically adjusted based on the electromagnetic interference intensity of the device's location. In environments with strong interference, such as densely populated areas with cold storage motors, the number of failures can be set to five to avoid misjudging parameter mismatches due to momentary interference. In environments with weak interference, the default value of three failures can be maintained to improve configuration efficiency.

[0025] This technical solution improves upon the configuration logic and verification mechanism: adaptive configuration breaks the limitation of fixed parameters, incorporating core communication parameters such as baud rate and data bits into a dynamic adjustment range, enabling the system to flexibly adapt to device characteristics and environmental requirements; the test data packet verification stage serves as an intermediate verification layer between configuration and transmission, verifying the validity of parameter combinations through actual data interaction to prevent invalid configurations from entering the transmission stage; backup parameter combinations and multiple retry mechanisms form fault-tolerant logic, automatically activating preset backup schemes when the current parameter combination fails consecutively, reducing the need for manual intervention.

[0026] The solution achieves remarkable technical results: adaptive configuration significantly improves device compatibility, enabling the same transmission method to be adapted to various cold chain devices without the need to develop separate communication modules for different devices; test data packet verification exposes parameter mismatch issues in advance, avoiding the tedious process of troubleshooting from scratch after communication failure and shortening fault location time; backup parameters and retry mechanisms greatly reduce the probability of communication interruption. Even if the initial parameter adaptation fails, the system can still maintain transmission continuity through automatic switching, which is especially suitable for unattended cold chain scenarios and reduces manual maintenance costs.

[0027] Traditional metadata data transmission has several reliability risks: First, metadata, such as the total number of data packets and the total number of records, serves as the basis for subsequent data transmission, but its own verification mechanism is weak and is prone to errors due to transmission interference. Second, the handling method after metadata verification failure is simplistic, usually directly terminating the transmission without a targeted retry strategy. This can lead to temporary errors under minor interference being misjudged as fatal failures, reducing transmission robustness.

[0028] Based on this, the metadata acquisition stage of the adaptive cold chain equipment data transmission method also includes verifying the total number of returned data packets and the total number of records. The verification bit is calculated by a hash algorithm. If the verification fails, multiple retransmission operations of the metadata request packet are performed first. Only if the retransmission still fails is the global termination and error reporting process executed.

[0029] This solution defines retransmission operations as three or more times. When the checksum verification of metadata (total data packets, total number of records) fails, the system automatically retransmits the metadata request packet three times by default. If verification still fails after three retransmissions, a global termination is executed and an error is reported. For critical cold chain equipment, such as the data transmission of vaccine refrigerated boxes, the number of retransmissions can be adjusted to five, as the accuracy of the metadata directly affects the integrity of subsequent full data transmission, requiring increased fault tolerance and redundancy. For non-critical equipment, three retransmissions can be maintained to avoid unnecessary waiting.

[0030] This technical solution constructs a three-level processing logic of verification, retry, and termination: the check bit generated by the hash algorithm provides strong verification capability for metadata. By comparing hash values, it can accurately identify tampering or loss during data transmission, ensuring the originality of metadata; the multiple retransmission mechanism is designed for temporary interference, such as sudden electromagnetic interference, giving metadata data transmission multiple opportunities for correction, avoiding the extreme handling of termination upon a single failure; global termination, as a last resort, is triggered only when multiple retransmissions still fail, ensuring timely error reporting in case of fundamental failure, such as equipment failure, and avoiding invalid retries.

[0031] The solution achieves significant technical benefits: the hash check bit fundamentally solves the problem of weak metadata verification, enabling accurate identification of metadata errors and laying the foundation for the integrity of subsequent data transmission; the multiple retransmission mechanism greatly improves the anti-interference capability of metadata data transmission, transforming transmission failures caused by temporary interference into a recoverable normal process and reducing unnecessary transmission interruptions; the logic of prioritizing retry and terminating as a fallback balances reliability and efficiency, avoiding transmission termination caused by minor errors and stopping losses in time during serious failures, ensuring predictable system behavior.

[0032] Traditional data packet sequence number management suffers from overflow and confusion issues: First, it uses single-byte counting, which has a limited sequence number range. When transmitting large amounts of data, overflow can easily occur, leading to duplicate or disordered sequence numbers and making it impossible to distinguish between data packets from different batches. Second, the sequence number loop mechanism is simple, and after overflow, it is directly reset to the initial value. It lacks a record of the number of loops, making it difficult for the receiving end to determine the order of data packets and affecting the accuracy of data splicing.

[0033] Based on this, the packet sequence management of the adaptive cold chain equipment data transmission method adopts a two-byte circular counting mechanism. When the low-order byte overflows, the high-order byte is triggered to carry over. The high-order byte is set with a limited range. When the high-order byte reaches the upper limit and the low-order byte overflows again, the high-order byte is reset to the initial value and the number of loops is recorded. The number of loops is used as an auxiliary parameter for data integrity verification.

[0034] This technical solution optimizes sequence number management through hierarchical counting and cyclic recording: the two-byte structure expands the sequence number space, with the low-order byte responsible for basic counting and the high-order byte responsible for carry management, significantly improving the range of sequence number representation and reducing overflow frequency; the high-order byte's range limitation and reset mechanism avoid resource occupation caused by unlimited carry, while the cycle count record provides a batch identifier for sequence number cycling; the cycle count, as an auxiliary parameter, together with the sequence number, constitutes a complete data packet identifier, providing a basis for the receiving end to determine the data order.

[0035] The solution achieves significant technical benefits: the double-byte counting significantly expands the sequence number range, meeting the needs of large-volume data transmission in cold chain equipment and reducing transmission chaos caused by sequence number overflow; the high-byte carry and reset mechanism makes sequence number management more efficient, enabling cyclic reuse of sequence numbers under limited resources; the combination of cycle count and sequence number identification solves the problem of sequence number duplication, and the receiving end can clearly determine the order of data packets through the cycle count, ensuring the accuracy of data splicing, which is especially suitable for the long-term, continuous data transmission needs in cold chain scenarios.

[0036] In traditional cold chain data transmission, the impact of environmental factors on transmission performance is neglected: First, the coupling effect of low temperature environment and electromagnetic interference is not considered. Evaluating the impact of temperature or electromagnetic interference alone cannot reflect the synergistic effect of the two in actual scenarios, resulting in a lack of accurate basis for adjusting transmission parameters; Second, there is a lack of quantitative indicators of environmental impact, and the understanding of how low temperature and electromagnetic interference affect transmission stability is vague, making it difficult to formulate targeted optimization strategies, resulting in increased transmission error rate or decreased efficiency.

[0037] Based on this, the adaptive cold chain equipment data transmission method introduces an interference-temperature coupling coefficient during data transmission to quantify the impact of the low-temperature environment on transmission performance. The coupling coefficient is calculated using the following formula: ; in, Interference-temperature coupling coefficient, Indicates the intensity of the ambient electromagnetic field. Indicates the chip junction temperature. The electromagnetic susceptibility coefficient, This is the temperature compensation reference value. Temperature is a factor that affects the environment. This is the chip's reference operating temperature.

[0038] This formula is used to quantify the combined effects of environmental electromagnetic interference and chip temperature on transmission performance. Its design logic is based on the physical characteristics of cold chain equipment in extreme environments and the working mechanism of the chip.

[0039] From the perspective of the numerator, The cubic form is not arbitrarily set, but rather stems from strong electromagnetic interference, such as the nonlinear effect of the electromagnetic field generated by the cold storage motor group on data transmission.

[0040] Experiments show that when the electromagnetic field intensity exceeds a certain threshold, its damage to signal integrity increases cubically with increasing intensity. This is because a strong electromagnetic field induces a nonlinear decay in the carrier mobility within the chip, leading to a sharp increase in signal distortion. As a parameter for electromagnetic susceptibility calibration, it needs to be determined experimentally based on the chip model, such as the anti-interference level of a cryogenic MCU. For example, for chips with strong anti-interference capabilities... A smaller value is used to weaken the weight of electromagnetic interference; conversely, a larger value is used to ensure accurate quantification of highly sensitive chips.

[0041] In the denominator, The exponential function is used to characterize the effect of temperature on the chip's anti-interference capability. When the chip junction temperature... Below the reference operating temperature This is usually the room temperature operating point marked in the chip datasheet, such as 25°C. When the value is negative, the exponent term is transformed into... Its value increases as temperature decreases, leading to an increase in the denominator and the coupling coefficient. This decreases—which aligns with the physical characteristic that at low temperatures, the resistivity of the semiconductor material inside the chip increases, signal transmission delay increases, but electromagnetic interference sensitivity decreases; when Higher than When the exponent term approaches 0, the denominator is approximately equal to 0. At this point, the coupling coefficient is mainly determined by electromagnetic interference.

[0042] As a temperature-dependent factor, it needs to be calibrated through low-temperature environment experiments. For example, within the range of -40℃ to 25℃, the bit error rate variation curve of the chip at different temperatures is measured and fitted to obtain the result. The optimal value is determined to ensure the accuracy of the quantification of the coupling effect of temperature.

[0043] constant term Its purpose is to prevent the denominator from approaching 0 at extremely low temperatures, such as At that time, the exponential term may be extremely large, but if A properly configured value ensures the denominator remains positive and also serves as a reference value for temperature compensation. Its magnitude needs to be determined based on the chip's anti-interference baseline at room temperature, for example, by measuring the chip's performance in the absence of electromagnetic interference. , normal temperature Transmission performance at that time The coupling coefficient is set to be within a reasonable range, such as around 1.0, to facilitate the calculation of subsequent fragmentation and retransmission parameters.

[0044] Those skilled in the art can implement this formula through the following steps: Select the target chip and simulate different electromagnetic shielding chambers. (0 to the maximum electromagnetic intensity of the equipment's operating environment) and (Combined conditions of -40℃ to 50℃); Measure the packet error rate for each combination and establish an error rate versus... , The mapping relationship; Fitting by least squares method , , The specific values ​​ensure the coupling coefficient. Consistent with the trend of error rate changes; The calibrated parameters are written into the device firmware for real-time calculation. The formula is designed to reflect the physical mechanisms of electromagnetic interference and temperature, and its calibrable parameters ensure universality across different hardware environments.

[0045] This technical solution constructs a quantitative model of environmental impact through coupling coefficients: In the formula... The term reflects the nonlinear effects of electromagnetic interference, demonstrating the sharp increase in interference under strong electromagnetic environments; the exponential term... Characterizing the mechanism of temperature influence, when Below At low temperatures, the value of the exponent changes significantly, reflecting the amplifying effect of low temperature on transmission performance. , , The coefficients are calibrated according to chip characteristics and cold chain scenarios to ensure the model's relevance.

[0046] The solution achieves significant technical results: For the first time, the coupling coefficient quantifies the synergistic effect of electromagnetic interference and low temperature, overcoming the limitations of traditional methods that assess environmental factors individually, and transforming the impact of the environment on transmission performance from a qualitative description to a quantitative indicator; the quantitative model provides a precise basis for subsequent adjustments to transmission parameters, allowing for adjustments based on… The value is dynamically optimized to improve the transmission strategy, such as adjusting the fragment size and the number of retransmissions; coefficient calibration for cold chain scenarios ensures that the model fits the actual application, avoids the error of the general model in low temperature environment, improves the accuracy of transmission parameter adjustment, and ultimately reduces transmission failures caused by environmental factors.

[0047] In traditional cold chain data transmission, the setting of data packet fragment size has obvious drawbacks: First, using a fixed fragment size makes it impossible to dynamically adjust according to the actual electromagnetic interference intensity and temperature environment. In environments with strong interference and ultra-low temperatures, large fragments are prone to overall failure due to transmission errors, while in low-interference environments, small fragments reduce transmission efficiency due to the excessive proportion of header information. Second, the correlation between fragment size and environmental impact lacks quantitative basis, and adjustment strategies rely heavily on empirical values, resulting in insufficient precision in fragment optimization and difficulty in balancing transmission reliability and efficiency.

[0048] Based on this, the adaptive cold chain equipment data transmission method adjusts the data packet fragment size based on the coupling coefficient, and the fragment size calculation formula is as follows; ; in, For packet fragment size, Maximum slice size, The preset baseline coupling coefficient is used when At that time, the fragment size remains at 1. , The interference-temperature coupling coefficient is currently calculated. This technical solution constructs a dynamic relationship between the piece size and environmental impact through a mathematical model: the introduction of a sine function causes the piece size to vary with the coupling coefficient. This reflects the nonlinear variation of the combined effects of disturbance and temperature. As the environment deteriorates, the value of the sine function decreases, and the fragment size shrinks accordingly, reducing the risk of single-packet transmission. As an upper limit, to ensure low-interference environment, Maximum fragmentation is used to reduce header information redundancy and improve transmission efficiency; reference coupling coefficient The setting provides a critical point for environment partitioning, enabling the fragmentation strategy to automatically switch between efficient and reliable transmission modes.

[0049] This formula is used based on the coupling coefficient. The function form for dynamically adjusting the data packet fragment size is based on the principle of balancing anti-interference requirements and transmission efficiency, and can be implemented by those skilled in the art through specific parameter configuration.

[0050] sine function It is the core of slice size adjustment, when This means that environmental interference and temperature effects are within the normal range that the chip can withstand. The sine function value is 1 at this time. The basis for this setting is that, under good conditions, large fragments can reduce the redundancy of data packet header information and improve transmission efficiency. For example, a 1000-byte large fragment has a 90% lower header ratio than a 100-byte small fragment.

[0051] As a baseline coupling coefficient, it needs to be determined based on the stable operating threshold of the chip within its design life, for example, the maximum value when the chip's bit error rate is below 0.1%, as measured experimentally. Value, set it to This ensures that the division of the normal range is feasible.

[0052] when At that time, the environment deteriorated. The value of the sine function varies with Increasing while monotonically decreasing, leading to Reduce synchronously.

[0053] The physical significance of this change lies in the fact that, under harsh conditions, smaller fragments can reduce the probability of single-packet transmission failure. For example, the retransmission cost of a 200-byte fragment is far lower than that of a 2000-byte fragment. Furthermore, the non-linear characteristics of the sine function, with a rapid initial decrease followed by a slower decrease, can prevent excessive fragment shrinkage. Much greater than hour, The value approaches 0, but in practical applications it can be limited by hardware. The minimum value, such as not less than 64 bytes, ensures the integrity of the data frame structure.

[0054] The settings need to be combined with the communication interface characteristics of the cold chain equipment, such as the maximum frame length of the serial port and the MTU value of the wireless module. For example, for an RS485 serial port, the settings can be adjusted accordingly. Set to 1024 bytes, the typical maximum payload of this interface, to match the hardware's transmission capacity.

[0055] Those skilled in the art can determine the parameters through the following steps: The maximum slice size supported by the measuring device is set. ; Determined through experiments ; In different The relationship between fragment size and transmission success rate was tested under certain conditions to verify the effectiveness of the sine function adjustment. If necessary, the function form was fine-tuned, such as by replacing it with... To change the rate of descent.

[0056] The design of this formula not only clarifies the quantitative relationship between fragment size and environmental coupling effect, but also uses measurable parameters ( , This ensured feasibility.

[0057] The solution achieves significant technical benefits: the nonlinear adjustment mechanism overcomes the rigidity of fixed fragmentation, enabling fragment size to precisely match the degree of environmental degradation. In environments with strong interference and ultra-low temperatures, smaller fragments reduce the probability of errors, while larger fragments improve efficiency in favorable environments. The quantitative characteristics of the mathematical model avoid the subjectivity of empirical adjustments, ensuring consistent and predictable logic for fragment size adjustment under different environments. The combination of the maximum fragment size and the baseline coefficient forms a dual constraint, guaranteeing transmission efficiency in low-interference environments while providing a safety net for reliability in high-interference environments. This is particularly suitable for the characteristics of large environmental fluctuations in cold chain scenarios, enabling the transmission system to have environmental self-adaptive capabilities.

[0058] Traditional data retransmission mechanisms suffer from both resource waste and insufficient reliability: First, the number of retransmissions is fixed. In harsh environments such as strong electromagnetic interference and ultra-low temperatures, the fixed number of retransmissions may lead to data loss due to insufficient retransmissions. On the other hand, in good environments, excessive retransmissions will consume bandwidth and time resources. Second, the correlation between the number of retransmissions and environmental impact lacks scientific basis. Adjustments rely heavily on static thresholds and cannot be dynamically adapted based on the real-time synergistic effect of interference and temperature, resulting in insufficient accuracy of the retransmission strategy.

[0059] Based on this, the number of retransmissions in the adaptive cold chain equipment data transmission method is dynamically determined according to the coupling coefficient, and the calculation formula is as follows:

[0060] in, For the number of retransmissions, The interference-temperature coupling coefficient is currently calculated. This is the retransmission coefficient. This indicates a rounding up operation. If the result is less than 1, the number of retransmissions will be forced to be 1.

[0061] This formula is used based on the coupling coefficient. The function structure for dynamically determining the number of retransmissions for data packets is designed to balance transmission reliability and resource consumption, and the physical meaning and determination method of each parameter are clear and operable. (Logarithmic terms) It is the core of retransmission count adjustment, and its function is to adjust the retransmission count according to... It is increasing and growing, but the growth rate is gradually slowing down.

[0062] when Slightly larger At that time, it was in a state of mild interference. The logarithmic terms are approximately The number of retransmissions increased slightly; when Much greater than At that time, it was in a state of severe interference. The growth rate has decreased significantly.

[0063] The technical logic behind this design is as follows: Under severe interference, excessive retransmissions can lead to bandwidth saturation and a surge in latency. The diminishing marginal returns of logarithmic terms can prevent the number of retransmissions from increasing indefinitely. For example, when… for When the value is 10 times that of the linear term, the logarithmic term is approximately 2.3, which is only 23% of the linear growth, effectively balancing reliability and efficiency.

[0064] coefficient The retransmission intensity adjustment factor needs to be determined based on the device's real-time requirements and battery capacity. For scenarios with high real-time requirements, such as cold chain alarm data transmission, It can be set to a smaller value, such as 1.2, to reduce retransmission latency; for scenarios where reliability is paramount, such as historical temperature and humidity records, Setting it to a larger value, such as 2.5, can increase the success rate.

[0065] The specific values ​​can be calibrated experimentally, for example, under the same conditions. The following tests are different The optimal balance between the corresponding transmission success rate and latency is selected.

[0066] Round up symbol Ensuring that the retransmission count is a positive integer, and forcing it to be 1 when the calculated result is less than 1, guarantees that even under slight interference, Slightly smaller It can also correct occasional errors, such as single packet loss caused by transient electromagnetic pulses, through at least one retransmission.

[0067] Those skilled in the art can implement this through the following steps: Based on the equipment application scenario The initial value; In different The relationship between retransmission count, success rate, and latency was tested and optimized. ; Verification in extreme cases, such as Check if the number of retransmissions is within a reasonable range, such as no more than 10, to avoid exhausting resources.

[0068] This technical solution achieves intelligent adaptation of the retransmission count through a composite function model: coupling coefficient By directly introducing the formula, the number of retransmissions increases with the enhancement of the combined effects of interference and temperature, ensuring sufficient retransmission redundancy under harsh environments; for several terms The addition of this feature makes the increase in retransmission count marginally decreasing, avoiding resource depletion caused by an unlimited increase in retransmission count under extreme conditions; retransmission coefficient It can be adjusted according to device characteristics, such as battery capacity and transmission priority, to provide customization space for different scenarios; rounding up and minimum value constraints ensure that the number of retransmissions is a valid integer and that at least one retransmission is performed to avoid errors caused by zero retransmissions that cannot be corrected.

[0069] The solution achieves significant technical benefits: the dynamic retransmission mechanism addresses the limitations of fixed retransmissions, automatically increasing retransmissions to improve reliability in deteriorating environments and reducing retransmissions to conserve resources in favorable environments; the diminishing marginal growth characteristic balances reliability and efficiency, ensuring necessary redundancy in harsh environments while avoiding transmission delays caused by excessive retransmissions; the adjustability of the retransmission coefficient enhances the solution's versatility, allowing for flexible configuration based on the actual needs of cold chain equipment, such as prioritizing real-time performance or reliability; and the minimum constraint ensures that even under slight interference, occasional errors can be corrected through at least one retransmission, reducing the probability of data loss and maintaining the stability of the transmission system in complex environments.

[0070] Traditional data integrity verification suffers from insufficient fault tolerance: First, it uses a single verification method, such as relying solely on CRC check, which is insufficient to handle various types of transmission errors. For example, CRC check can detect data bit flipping but is not sensitive to abnormal data length, leading to some errors being missed. Second, the handling strategy after verification failure is simple, usually determining the data invalid based on a single verification failure. This is prone to misjudging data due to occasional errors caused by momentary interference, affecting the continuity of transmission. Third, it lacks specific protection for critical data, such as temperature and humidity, and equipment status codes. Using the same verification standard as ordinary data may result in critical information errors not being detected in a timely manner.

[0071] Based on this, the integrity verification of the data reception and processing stage of the adaptive cold chain equipment data transmission method adopts a multi-layer verification mechanism. The first layer is CRC16 cyclic redundancy check, the second layer is data length matching check, and the third layer is key field range check. The key fields include temperature value, humidity value and equipment operating status code. Each layer of verification is set with an independent failure threshold. When any layer of verification fails multiple times in a row, the retransmission process of the corresponding data packet is triggered.

[0072] In this solution, "consecutive multiples" is defined as three or more consecutive failures, and the number of failures can be configured independently for different verification levels. The first level is CRC16 verification: three consecutive failures trigger retransmission. Because CRC verification has strong anti-interference capabilities, three failures can be considered a substantial error in the data packet. The second level is data length matching verification: two consecutive failures trigger retransmission. Length errors are mostly due to transmission truncation, and the fault tolerance requirement is low. The third level is critical field verification: temperature, humidity, and status code verification: three consecutive failures trigger retransmission. Errors in critical fields directly affect the effectiveness of monitoring and require strict judgment. These multiple configuration differences need to be preset through firmware parameters during device initialization. Those skilled in the art can flexibly adjust them according to the importance of the fields.

[0073] This technical solution enhances data reliability through layered verification and fault tolerance mechanisms: CRC16 verification serves as the base layer, utilizing polynomial operations to detect errors such as bit flips and shifts during data transmission, covering most problems caused by random interference; data length matching verification serves as the second layer, comparing the received data length with the expected length to identify problems that CRC cannot detect, such as data truncation and splicing errors; critical field range verification is designed for core cold chain data, using preset reasonable ranges, such as temperature values ​​within the operating range of cold chain equipment, to identify erroneous data exceeding physical norms, ensuring the validity of critical information; multi-layered independent failure thresholds and a mechanism for triggering retransmission after consecutive failures avoid misjudgments caused by single interferences, improving the fault tolerance of the verification.

[0074] The solution achieves significant technical benefits: the multi-layered verification mechanism covers various error types, overcoming the limitations of single verification methods and enabling accurate identification of bit errors, length errors, and logical errors in data transmission; dedicated verification of key data ensures the accuracy of core cold chain information, avoiding monitoring inaccuracies or decision-making errors caused by temperature, humidity, or equipment status code errors; the strategy of triggering retransmission after consecutive failures reduces the impact of occasional errors on transmission, confirming the authenticity of errors through multiple verifications and reducing the probability of misjudgment; the setting of independent thresholds for each layer enhances the flexibility of verification, allowing the strictness to be adjusted according to the probability of different error types, such as setting stricter thresholds for range verification of key data, so that the verification system can effectively intercept errors while maintaining the continuity of transmission, thus improving the reliability of cold chain data.

[0075] Traditional buffer monitoring suffers from several issues regarding accuracy: First, it directly uses real-time data volume to determine transmission status, making it susceptible to instantaneous fluctuations caused by sudden data transmissions. For example, a sudden increase or decrease in buffer data volume within a short period could be misjudged as the termination of data transmission. Second, the fixed detection cycle and lack of filtering fail to smooth out high-frequency noise interference, leading to inconsistencies in the assessment of data growth status, such as frequent switching between data presence and absence. Third, the threshold for determining the absence of new data is set uniformly and does not consider the characteristics of different transmission stages. For instance, the buffer change patterns differ between the initial and final stages of data transmission, and a uniform threshold can easily lead to misjudgments.

[0076] Based on this, the buffer monitoring of the adaptive cold chain equipment data transmission method adopts sliding window mean filtering. The size of the filtering window is a preset number of continuous detection cycles, and each detection cycle is a preset duration. The data growth status is judged by the change rate of the filtered buffer data volume. The change rate is calculated by the ratio of the difference between the current data volume and the data volume of the previous cycle to the data volume of the previous cycle. When the change rate is less than the preset value multiple times in a row, it is determined that there is no new data.

[0077] In this scheme, "consecutive multiple times" is defined as three or more consecutive times. Buffer monitoring uses a sliding window mean filter. For example, if the window size is 5 detection cycles, each cycle is 100ms, the rate of change after filtering is calculated as the difference between the current data volume and the previous cycle's data volume divided by the previous cycle's data volume. When this rate of change is less than a preset threshold for three consecutive times, it is determined that no new data has been added. For high-frequency transmission equipment, such as transmitting 100 frames per second, "multiple times" can be set to 5 times to avoid misjudging transmission termination due to brief data gaps; for low-frequency transmission equipment, such as transmitting 1 frame per minute, it can be set to 3 times to reduce waiting time.

[0078] This technical solution improves monitoring accuracy through filtering and dynamic thresholds: sliding window mean filtering smooths the buffer data volume over multiple consecutive detection cycles, effectively suppressing instantaneous fluctuations and high-frequency noise, making the data volume change trend clearer and avoiding misjudgments caused by sudden data; the introduction of a rate of change index, rather than absolute data volume, judges the data growth status through relative change, solving the adaptation problem for different transmission stages, such as different data volumes. For example, when the absolute change is small but the relative change is large, it can still be identified as new data; the judgment condition of multiple consecutive times below the threshold further reduces the probability of misjudgment caused by a single fluctuation, ensuring that the judgment of no new data is based on a stable trend rather than an instantaneous state.

[0079] The solution achieves significant technical benefits: sliding window filtering significantly improves the stability of data volume monitoring, smooths out jitter caused by instantaneous interference, and makes buffer state judgment more reliable; the rate of change index enhances adaptability to different transmission stages, and can accurately identify the growth status through relative changes regardless of the data volume; the continuous judgment mechanism reduces the probability of false judgment, ensuring that the waiting is terminated only when data growth has indeed stopped, avoiding data loss due to premature termination or resource waste due to premature termination; the design of preset detection cycle and window size can be flexibly configured according to the data transmission rate of cold chain equipment, making the monitoring mechanism adaptable to the characteristics of different equipment models, and improving the versatility and practicality of the solution.

[0080] Traditional cold chain data transmission lacks performance feedback and dynamic optimization mechanisms: First, key performance indicators are not evaluated in real time during transmission, making it impossible to detect trends of transmission quality degradation in a timely manner. For example, problems such as decreased data packet reception success rate and increased latency are only noticed after transmission failure, missing the opportunity for early intervention. Second, performance evaluation indicators are singular, focusing only on success rate and ignoring dimensions related to user experience such as latency and data integrity, resulting in an incomplete evaluation of transmission quality. Third, there is a lack of automatic optimization mechanisms after performance degradation. When transmission indicators fail to meet standards, manual intervention to adjust parameters is required, which is slow and inefficient, especially unsuitable for unattended cold chain scenarios.

[0081] Based on this, the adaptive cold chain equipment data transmission method also includes a dynamic evaluation step for transmission performance. The evaluation indicators include data packet reception success rate, average transmission latency, and data integrity score. The success rate is the ratio of the number of successfully received data packets to the total number of requested data packets. The average transmission latency is the average time from sending a request to receiving a data packet. The integrity score is the weighted sum of the number of fields that pass verification and the total number of fields. The weights are pre-configured according to the importance of the fields. When any indicator fails to meet the preset threshold for multiple consecutive monitoring periods, the transmission parameter optimization process is automatically initiated.

[0082] In this solution, multiple definitions are defined for three or more monitoring periods. The default monitoring period is 1 minute. If any of the following indicators—data packet reception success rate, average transmission latency, or data integrity score—fail to meet the preset threshold for three consecutive periods (e.g., success rate <95%), transmission parameter optimization is initiated, such as adjusting fragment size and retransmission coefficient. For scenarios with extremely high real-time requirements, such as cold chain alarm transmission, multiple definitions can be set to two periods for rapid response to performance degradation. For scenarios prioritizing stability, five periods are used to avoid instantaneous fluctuations triggering ineffective optimizations.

[0083] This technical solution constructs an adaptive transmission system through multi-dimensional evaluation and closed-loop optimization: the multi-index evaluation system covers transmission reliability, real-time performance, and data quality, comprehensively reflecting transmission performance and avoiding the limitations of a single index; the weighted design of integrity scoring highlights the importance of key fields, making the evaluation results more aligned with the needs of cold chain operations; the threshold judgment mechanism for multiple consecutive periods avoids erroneous optimization caused by instantaneous fluctuations, ensuring the necessity of initiating optimization; the automatic parameter optimization process, such as adjusting fragment size and retransmission count, forms a closed-loop control, which can repair performance degradation without manual intervention.

[0084] The solution achieves significant technical benefits: dynamic evaluation enables real-time monitoring of transmission performance, allowing for the detection of degradation trends before transmission failures and providing a basis for early intervention; multi-dimensional indicators ensure comprehensive performance evaluation, avoiding the overlooking of other potential problems due to the achievement of a single indicator, such as high success rate but excessive latency; weighted integrity scoring focuses the evaluation on core data, ensuring the transmission quality of critical information; and the automatic optimization mechanism enhances the system's autonomy and response speed, enabling rapid parameter adjustments to restore transmission quality when performance deteriorates, reducing manual maintenance costs. It is particularly suitable for large-scale, distributed cold chain equipment networks, improving the overall stability and adaptability of the transmission system.

[0085] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An adaptive cold chain equipment data transmission method, comprising: The communication initialization phase involves opening the serial port corresponding to the cold chain equipment and setting connection parameters; the metadata acquisition phase involves sending metadata request packets, receiving and parsing the total number of data packets and total number of records returned by the equipment; the data packet request phase involves cyclically sending data requests according to packet sequence numbers; and the data reception and processing phase involves reading data and performing integrity verification. The feature is that it further includes: Dynamic buffer monitoring detects changes in buffer data volume in real time to replace fixed-delay waiting; Intelligent waiting adjustment: terminates the waiting and sets the maximum monitoring period when no new data is detected after multiple consecutive checks. Precise retransmission locates the sequence number of the problematic packet when a single packet error occurs and only retransmits the failed data packet while retaining the successfully received data packets.

2. The adaptive cold chain equipment data transmission method according to claim 1, characterized in that, The communication initialization phase also includes adaptive configuration of serial communication baud rate, data bits, stop bits and parity method. During the configuration process, the parameter compatibility is verified by sending test data packets. If multiple tests fail in a row, the system will automatically switch to the preset backup parameter combination and try to connect again.

3. The adaptive cold chain equipment data transmission method according to claim 1, characterized in that, The metadata acquisition phase also includes verifying the total number of returned data packets and total number of records using a checksum. The checksum is calculated using a hash algorithm. If the verification fails, multiple retransmissions of the metadata request packets are performed. Only if the retransmissions still fail is the global termination and error reporting process executed.

4. The adaptive cold chain equipment data transmission method according to claim 1, characterized in that, The packet sequence number management adopts a two-byte circular counting mechanism. When the low byte overflows, it triggers the carry of the high byte. The high byte has a limited range. When the high byte reaches the upper limit and the low byte overflows again, the high byte is reset to the initial value and the loop count is recorded. The loop count is used as an auxiliary parameter for data integrity verification.

5. The adaptive cold chain equipment data transmission method according to claim 1, characterized in that, An interference-temperature coupling coefficient is introduced during data transmission to quantify the impact of low-temperature environments on transmission performance. The formula for calculating the coupling coefficient is as follows: ； in, The interference-temperature coupling coefficient, Indicates the intensity of the ambient electromagnetic field. Indicates the chip junction temperature. The electromagnetic susceptibility coefficient, This is the temperature compensation reference value. Temperature is a factor that affects the environment. This is the chip's reference operating temperature.

6. The adaptive cold chain equipment data transmission method according to claim 5, characterized in that, The packet fragment size is adjusted based on the coupling coefficient. The formula for calculating the fragment size is as follows: ； in, For packet fragment size, Maximum slice size, The preset baseline coupling coefficient is used when At that time, the fragment size remains at 1. , The interference-temperature coupling coefficient is currently calculated.

7. The adaptive cold chain equipment data transmission method according to claim 5, characterized in that, The number of retransmissions is dynamically determined based on the coupling coefficient, and the calculation formula is as follows: ； in, For the number of retransmissions, The interference-temperature coupling coefficient is currently calculated. This is the retransmission coefficient. This indicates a rounding up operation. If the result is less than 1, the number of retransmissions will be forced to be 1.

8. The adaptive cold chain equipment data transmission method according to claim 1, characterized in that, The integrity verification during the data reception and processing stage adopts a multi-layer verification mechanism. The first layer is CRC16 cyclic redundancy check, the second layer is data length matching check, and the third layer is key field range check. Key fields include temperature value, humidity value and equipment operating status code. Each layer of verification is set with an independent failure threshold. When any layer of verification fails multiple times in a row, the corresponding data packet retransmission process is triggered.

9. The adaptive cold chain equipment data transmission method according to claim 1, characterized in that, The buffer monitoring uses a sliding window mean filtering process. The size of the filtering window is the number of consecutive detection cycles, and each detection cycle is the preset duration. The data growth status is judged by the rate of change of the filtered buffer data. The rate of change is calculated by the ratio of the difference between the current data volume and the data volume of the previous cycle to the data volume of the previous cycle. When the rate of change is less than the preset value for several consecutive times, it is determined that there is no new data.

10. The adaptive cold chain equipment data transmission method according to claim 1, characterized in that, It also includes a dynamic evaluation step for transmission performance. Evaluation indicators include data packet reception success rate, average transmission latency, and data integrity score. The reception success rate is the ratio of the number of successfully received data packets to the total number of requested data packets. The average transmission latency is the average time from sending a request to receiving a data packet. The integrity score is the weighted sum of the number of fields that pass verification and the total number of fields. The weights are pre-configured according to the importance of the fields. When any indicator fails to meet the preset threshold for multiple consecutive monitoring periods, the transmission parameter optimization process is automatically started.

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