Data acquisition transmission method and device of a sensing device

Abstract

The present application relates to the field of data transmission, and more particularly to a data acquisition and transmission method and device for a sensing device. The method comprises the following steps: deploying a plurality of sensing devices in a cold chain transport container; continuously sampling the temperature of medical goods based on the sensing devices to generate a first parameter sequence; suppressing environmental interference on the first parameter sequence to generate a second parameter sequence; performing outlier rejection processing on the second parameter sequence to output a third parameter sequence; modeling the spatial temperature distribution of the third parameter sequence to construct a spatial temperature distribution field; losslessly encoding and compressing the spatial temperature distribution field, setting a short frequency transmission period, and transmitting the compressed package to a cloud platform to complete the data acquisition and transmission task. The present application improves the accuracy of temperature data acquisition during cold chain transportation, as well as real-time and efficient data transmission.

Description

Technical Field

[0001] This invention relates to the field of data transmission, and more particularly to a data acquisition and transmission method and apparatus for a sensing device. Background Technology

[0002] With the rapid development of IoT technology, temperature IoT devices (sensors) are increasingly widely used in medical cold chain transportation, providing real-time temperature monitoring capabilities for the transport of vaccines, pharmaceuticals, and biological samples. However, existing sensing devices still face some unresolved issues in practical applications. The temperature data collected by these devices is easily affected by environmental factors, differences in sensor accuracy, or external interference, resulting in abnormal noise. For example, during transportation, fluctuations in cold air inside the container, door opening and closing operations, or momentary anomalies in the sensor itself can all cause some temperature data to deviate from the true value, resulting in isolated outliers or drastic fluctuations in the acquisition sequence. This type of noise not only affects the accuracy of temperature data but may also mislead the cold chain management system into making incorrect temperature control decisions.

[0003] The real-time performance of temperature data uploaded by sensing devices is insufficient. In long-distance transportation or complex network environments, data upload delays or packet loss are common, preventing monitoring platforms from obtaining the latest temperature information inside containers in a timely manner. Especially in multi-node deployments or high-frequency sampling scenarios, the large volume of data and frequent transmissions, limited by communication link bandwidth and device processing capabilities, prevent true real-time temperature updates. This delay can hinder responses to temperature anomalies and reduce the safety level of medical cold chain transportation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a data acquisition and transmission method and apparatus for sensing devices, thereby resolving at least one of the aforementioned technical problems.

[0005] To achieve the above objectives, the present invention provides a data acquisition and transmission method for a sensing device, comprising the following steps: S1: Deploy multiple sensing devices on a cold chain transport container; continuously sample the temperature of the medical goods based on the sensing devices to generate a first parameter sequence; S2: Suppress environmental interference on the first parameter sequence to generate the second parameter sequence; S3: Perform outlier removal on the second parameter sequence and output the third parameter sequence; S4: Model the spatial temperature distribution of the third parameter sequence and construct the spatial temperature distribution field; S5: Perform lossless encoding and compression on the spatial temperature distribution field, set a short-frequency transmission period, and transmit the compressed package to the cloud platform to complete the data acquisition and transmission operation.

[0006] This specification provides a data acquisition and transmission apparatus for a sensing device, used to execute the data acquisition and transmission method for a sensing device as described above, comprising: A sampling unit is used to deploy multiple sensing devices on a cold chain transport container; based on the sensing devices, the temperature of the medical goods is continuously sampled to generate a first parameter sequence; The suppression unit is used to suppress environmental interference in the first parameter sequence and generate the second parameter sequence. The elimination unit is used to perform outlier elimination on the second parameter sequence and output the third parameter sequence. The distribution modeling unit is used to model the spatial temperature distribution of the third parameter sequence and construct the spatial temperature distribution field. The transmission unit is used to perform lossless encoding and compression of the spatial temperature distribution field, set a short-frequency transmission period, and transmit the compressed package to the cloud platform to complete the data acquisition and transmission operation.

[0007] The specific benefits of this invention are as follows: Deploying multiple sensing devices within a cold chain transport container and continuously sampling the temperature enables comprehensive coverage and high-precision monitoring of all areas inside the container. Multi-node collaborative sampling reflects local temperature differences, reduces the impact of single-point deviations, and generates a continuous first parameter sequence, providing a complete and reliable data foundation for temperature trend analysis, anomaly detection, and subsequent data processing. It also improves monitoring redundancy; even if some nodes fail temporarily, it does not affect the integrity of the overall temperature data or the safety of the medical cold chain. By suppressing environmental interference in the first parameter sequence, interference from external temperature fluctuations, transport vehicle vibrations, or instantaneous thermal disturbances on the internal temperature data can be effectively eliminated, making the second parameter sequence more stable and reliable. The processed data more accurately reflects the actual storage and transportation environment temperature of the goods, reducing the risk of misjudgment. Outlier removal and abnormal noise repair of the second parameter sequence can remove extreme values ​​caused by sensor failures, communication anomalies, or instantaneous interference, making the third parameter sequence more stable and reliable.

[0008] Identifying potential cold or hot spots improves temperature uniformity assessment capabilities and optimizes cargo placement and refrigeration strategies. Simultaneously, the spatial temperature distribution field provides a foundation for visual monitoring, dynamic analysis, and cloud management, enabling more precise temperature control decisions and improved cold chain transportation safety. Lossless encoding and compression of the spatial temperature distribution field, coupled with short-frequency transmission cycles, uploads it to the cloud, achieving efficient, real-time data acquisition and remote monitoring. Lossless compression ensures no loss of temperature data accuracy, reduces network bandwidth pressure, and minimizes transmission latency and data loss risks. The cloud platform can uniformly manage, analyze, and visualize temperature information from multiple containers and nodes, supporting rapid anomaly warnings and temperature control decisions, thereby improving the safety and management efficiency of medical cold chain transportation. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the steps of a data acquisition and transmission method for a sensing device according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a detailed flowchart illustrating the implementation steps of step S2; Figure 4 This is a schematic diagram of the results of a data acquisition and transmission device for a sensing equipment. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0011] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0012] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0013] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0014] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0015] This application provides a data acquisition and transmission method and apparatus for a sensing device. The execution entities of the data acquisition and transmission method and apparatus for the sensing device include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered as general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud-based data management system.

[0016] Please see Figures 1 to 4 This invention provides a data acquisition and transmission method for a sensing device, comprising the following steps: S1: Deploy multiple sensing devices on a cold chain transport container; continuously sample the temperature of the medical goods based on the sensing devices to generate a first parameter sequence; S2: Suppress environmental interference on the first parameter sequence to generate the second parameter sequence; S3: Perform outlier removal on the second parameter sequence and output the third parameter sequence; S4: Model the spatial temperature distribution of the third parameter sequence and construct the spatial temperature distribution field; S5: Perform lossless encoding and compression on the spatial temperature distribution field, set a short-frequency transmission period, and transmit the compressed package to the cloud platform to complete the data acquisition and transmission operation.

[0017] In the embodiments of the present invention, see Figure 1 This is a flowchart illustrating the steps of a data acquisition and transmission method for a sensing device according to the present invention. In this example, the steps of the data acquisition and transmission method for the sensing device include: S1: Deploy multiple sensing devices on a cold chain transport container; continuously sample the temperature of the medical goods based on the sensing devices to generate a first parameter sequence; In this embodiment, the internal space of the container is measured to establish a three-dimensional coordinate system. The length, width, and height of the container are set as the X, Y, and Z axes, respectively, with the origin typically set at the bottom front corner. For example, for a container measuring 2.0m × 1.5m × 1.6m, a sensing node can be placed at every 0.5m position, for a total of 8 nodes, covering the top, bottom, and center of the container. Each sensing device continuously samples through a built-in temperature sensor, with a sampling period of 5 or 10 seconds, selecting the appropriate sampling frequency based on the rate of temperature change. During sampling, the device records the temperature value and the sampling timestamp. For example, Node-01 collects 6 temperature values ​​within 30 consecutive seconds: 3.02℃, 3.04℃, 3.01℃, 3.05℃, 3.03℃, and 3.04℃. All node data is integrated according to the timestamp order to form a first parameter sequence, which includes the device ID, sampling time, and temperature value. For example, the sequence entry can be represented as (10:00:05, Node-01, 3.02℃).

[0018] Step S2: Perform environmental interference suppression on the first parameter sequence to generate the second parameter sequence; In this embodiment, ambient temperature sensor data is collected to obtain the background ambient temperature inside the container. For example, the ambient temperature value is collected every 5 seconds and recorded in a separate sequence. Then, correlation analysis is performed between the first parameter sequence and the ambient temperature sequence. The Pearson correlation coefficient is calculated to determine the dependence of each node's temperature data on the ambient temperature. For example, the correlation coefficient between the temperature sequence of Node-01 and the ambient temperature sequence is 0.65, indicating that environmental fluctuations have a certain impact on this node. Based on the correlation coefficient, linear regression or adaptive filtering methods are used to correct the node temperature sequence, removing the parts related to ambient temperature. For example, weighted filtering is used to subtract the part affected by ambient temperature from the node temperature, resulting in a temperature sequence after environmental interference suppression. After suppression, the fluctuation amplitude of the temperature sequence is significantly reduced; for example, the standard deviation of the original sequence is 0.12℃, which decreases to 0.07℃ after suppression. After processing, a second parameter sequence is obtained, which can more realistically reflect the temperature changes of the cargo itself.

[0019] S3: Perform outlier removal on the second parameter sequence and output the third parameter sequence; In this embodiment, statistical distribution analysis is performed on the second parameter sequence to calculate the mean and standard deviation. For example, the mean of Node-01 within a 10-minute time window is 3.05℃, and the standard deviation is 0.07℃. Based on the normal distribution, temperature values ​​outside the mean ± 2 times the standard deviation are marked as outliers. For example, if the temperature at a certain moment is 3.24℃, significantly higher than the upper limit of 3.19℃, it is determined to be an outlier. For single instantaneous abnormal noise points, linear interpolation is used for repair, i.e., the interpolated value is calculated based on two preceding normal temperature values ​​to replace the outlier. For example, if the preceding and following normal values ​​are 3.03℃ and 3.05℃, the interpolated outlier is 3.04℃. For continuous outliers or obviously abnormal sequences, they are directly removed, and the sequence is rearranged chronologically. After processing, a third parameter sequence is formed, with continuous data and outliers corrected or removed, for example, the standard deviation is reduced to 0.05℃, the temperature change trend is smooth, and it truly reflects the temperature status of the goods.

[0020] S4: Model the spatial temperature distribution of the third parameter sequence and construct the spatial temperature distribution field; In this embodiment, the container is evenly divided into grids along the X, Y, and Z directions according to its dimensions, for example, one spatial unit every 0.2m, forming 1200 grid nodes. Using the temperature data of each node in the third parameter sequence, the temperature value of each grid node is calculated using inverse distance weighted interpolation. For example, if the target node is 0.3m, 0.5m, and 0.8m away from the three sensors, corresponding to temperatures of 3.02℃, 3.05℃, and 3.08℃ respectively, then the target node temperature is calculated to be approximately 3.05℃ based on the weights. On this basis, the temperature gradient, rate of change, and fluctuation amplitude of each node are calculated, forming a multidimensional temperature feature matrix. By integrating all node data, a complete spatial temperature distribution field can be generated, intuitively reflecting the temperature differences inside the container. For example, the temperature of the top node is approximately 3.12℃, the bottom node is approximately 2.98℃, and the fluctuation amplitude ranges from 0.03℃ to 0.10℃. This spatial distribution field provides the foundation for temperature monitoring visualization and subsequent data transmission.

[0021] S5: Perform lossless encoding and compression on the spatial temperature distribution field, set a short-frequency transmission period, and transmit the compressed package to the cloud platform to complete the data acquisition and transmission operation.

[0022] In this embodiment, after constructing the spatial temperature distribution field, the three-dimensional node temperature data and multi-dimensional features are losslessly compressed. First, the temperature sequence is differentially encoded, storing the temperature changes of adjacent nodes as differences to reduce redundant information. Then, a compressed package is generated using a lossless compression algorithm based on dictionary or Huffman coding; for example, the original data is approximately 80KB, which is compressed to 25KB. A short-frequency transmission period is set, for example, transmitting a compressed package every 30 seconds. The data is sent to the gateway via the sensor's wireless communication module along the optimal path, and then uploaded to the cloud platform by the gateway. Each data packet includes a timestamp and checksum to ensure integrity; if a packet is lost, it is automatically retransmitted. This method enables real-time acquisition and remote storage of temperature data throughout the cold chain transportation process. For example, approximately 360 compressed packages can be uploaded during a 3-hour transportation period, ensuring the integrity, continuity, and reliability of temperature monitoring data for medical goods, providing solid data support for temperature monitoring and anomaly early warning.

[0023] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: S11: Deploy multiple sensing devices on cold chain transport containers; S12: Continuously sample the temperature of the medical goods based on the sensing device to obtain temperature parameters; S13: Synchronously acquire parameter sampling timestamps; based on the parameter sampling timestamps, integrate the temperature parameters in a time sequence to generate the first parameter sequence.

[0024] In this embodiment, monitoring nodes are planned based on the structural dimensions of the cold chain transport container and the cargo loading layout. For example, for a standard medical cold chain transport box with a volume of approximately 5–8 m³, the internal space can be divided into three monitoring areas: upper, middle, and lower. Two to three sensor nodes are arranged in each area, keeping the total number of monitoring nodes to approximately 6–8 to ensure spatial representativeness of temperature sampling. Each sensor integrates a high-precision digital temperature sensor with a measurement range set to -40℃ to +60℃, a measurement accuracy controlled within ±0.2℃, and a temperature resolution of approximately 0.01℃. During installation, the sensor probe is suspended in an air gap of approximately 3–5 cm between cargo packaging via a fixed bracket to avoid direct contact with the cold source or cargo packaging surface, which could lead to localized temperature deviations. Each node is numbered with a unique device ID, such as Node-01 to Node-08, and a node location mapping relationship is established in the system. Subsequently, all sensor nodes are initialized and configured through the gateway device in the cold chain container, including wireless communication frequency band settings (such as LoRa or ZigBee communication), sampling period settings, and data upload channel configuration. For example, the communication channel can be set to 2.4GHz, and the data upload cycle can be set to once every 10 seconds.

[0025] Each sensor's internal microcontroller unit automatically performs temperature sampling according to a preset sampling period. For example, the system can be set to a 5-second sampling period, meaning each node reads temperature sensor data every 5 seconds. To improve the stability and noise immunity of the sampled data, multiple rapid measurements are typically performed and averaged within each sampling period. For instance, five temperature values ​​are continuously collected within a 5-second sampling period, with each sampling interval approximately 200 milliseconds, and then a moving average is used to calculate the final temperature value. If a node acquires temperature data of 2.83℃, 2.85℃, 2.86℃, 2.84℃, and 2.87℃ in one sampling period, the averaged temperature value is approximately 2.85℃, which is recorded as the temperature parameter for the current sampling period. Simultaneously, the system sets a safe temperature monitoring range based on common temperature control requirements for medical cold chain transportation (e.g., vaccine transportation environments must be maintained between 2℃ and 8℃). When a temperature is detected below 1.8℃ or above 8.2℃, the sensor automatically triggers a secondary confirmation sampling, such as performing three consecutive rapid samplings to verify the results and eliminate transient noise interference. After sampling, each temperature data point, along with node number, device status, battery voltage, and other information, forms a complete sampling record, such as "Node-03 / Temperature=3.12℃ / Battery=3.65V". The data is then transmitted in real-time to the gateway device inside the container via a wireless communication module, and the most recent 20-30 records are cached locally on the node as backup data to prevent data loss due to short-term communication interruptions. Through continuous periodic sampling, a stable temperature data stream can be formed.

[0026] A unified time reference is established through a gateway device. The gateway device obtains the standard time via the Network Time Protocol (NTP) or GPS timing module and periodically broadcasts time synchronization signals to all sensor nodes, such as performing time calibration every 10 minutes to keep the clock error of each node within ±50 milliseconds. Whenever a sensor completes a temperature sampling, the system immediately reads the device's internal clock and generates a corresponding sampling timestamp. For example, when Node-02 completes temperature sampling at system time "10:15:20" and obtains a temperature value of 3.04℃, it is recorded as the data structure: "Node-02 / Time=10:15:20 / Temperature=3.04℃". Subsequently, all sampled data is transmitted to the gateway device via the wireless network, where the gateway's data processing module performs unified data processing. During the data integration phase, the system first sorts the data uploaded by different nodes according to the timestamp and then aggregates it within a fixed time window, such as a 1-minute time window, centrally processing the temperature data of all nodes within that time range. Subsequently, a temperature change sequence is constructed using time-series data integration methods. For example, for a given node, the following sequence is formed within 10 minutes: (10:00:00, 2.96℃), (10:00:05, 2.98℃), (10:00:10, 2.97℃)...(10:09:55, 3.05℃). When communication delays or data loss occur, the system can perform linear interpolation compensation using temperature data from adjacent time points. For example, the missing temperature data at 10:02:10 can be estimated using two sample values, 10:02:05 and 10:02:15. Through timestamp binding, sorting, and data compensation operations, a complete and continuous temperature monitoring time series, i.e., the first parameter sequence, can finally be generated.

[0027] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: S21: Identify ambient temperature monitoring values; S22: Perform temperature correlation analysis on the first parameter sequence based on the ambient temperature monitoring values ​​to obtain the correlation coefficient; S23: Perform environmental interference analysis based on correlation coefficients to generate environmental interference characteristics; S24: Based on environmental interference characteristics, suppress environmental interference in the temperature parameter sequence to generate a second parameter sequence.

[0028] In this embodiment, an independent ambient temperature monitoring device is installed outside the cold chain transport container to collect data on changes in outside air temperature during transportation. The ambient temperature sensor is installed approximately 10-20 cm above the outer wall of the transport container, allowing the sensor to directly sense the outside air temperature without being affected by heat conduction from the vehicle's metal structure. An industrial-grade digital temperature sensor can be used, with a measurement range of -40℃ to 85℃, a measurement accuracy controlled within ±0.3℃, and a resolution of approximately 0.01℃. The temperature sampling period is consistent with the sampling period of the sensing device inside the cold chain container, for example, set to once every 5 seconds, thus ensuring consistency of internal and external temperature data over time. During each sampling, the temperature sensor first performs a rapid temperature reading and then continuously collects three temperature values ​​within a 500-millisecond interval, for example, -4.2℃, -4.1℃, and -4.3℃. The three sampling results are then averaged to obtain a stable ambient temperature value of approximately -4.2℃. After collection, the temperature value is bound to the corresponding sampling time and recorded, for example, forming a data record of "10:20:05, -4.2℃". After continuous sampling, a complete environmental temperature time series can be formed, such as (10:20:00, -4.3℃), (10:20:05, -4.2℃), (10:20:10, -4.1℃), etc.

[0029] After acquiring the ambient temperature time series, the ambient temperature data is matched with the first parameter sequence formed by the sensing devices inside the cold chain container at uniform time intervals. During time alignment, based on a 5-second sampling period, data at the same or closest time points are matched accordingly. For example, at time 10:25:00, the ambient temperature is -3.8℃, while the corresponding temperature values ​​in the first parameter sequence are 3.05℃ for Node-02 and 3.12℃ for Node-03. After data matching, a certain time window is selected for correlation calculation. For example, setting a 10-minute time window will yield approximately 120 sets of corresponding temperature data within that time period. The ambient temperature series is denoted as E(t), and the internal temperature series is denoted as T(t). The relationship between the two sets of data is calculated using statistical correlation analysis. Specifically, the mean and variance of the two series are calculated, and then the covariance is used to calculate the consistency of the changes in the two sets of data, thus obtaining the correlation coefficient R. The correlation coefficient ranges from -1 to 1. When the R value is close to 1, it indicates a significant positive correlation between the two temperature changes; when the R value is close to 0, it indicates a weak correlation. For example, in a certain time window, the ambient temperature gradually rises from -4.0℃ to -2.5℃, while the internal temperature slowly rises from 2.90℃ to 3.10℃, resulting in a correlation coefficient R of approximately 0.57. By repeatedly performing this calculation using a sliding time window method, for example, moving the window every 5 minutes and recalculating the correlation coefficient, a continuous correlation coefficient sequence can be formed.

[0030] Further analysis was conducted to determine the impact of ambient temperature changes on internal temperature, aiming to identify environmental interference characteristics. The analysis first established interference thresholds: a correlation coefficient R greater than 0.5 indicated significant environmental interference, R between 0.3 and 0.5 indicated moderate interference, and R below 0.3 indicated weak interference. After identifying a clear correlation, the amplitude of ambient temperature changes and the amplitude of internal temperature response were further calculated. Specifically, the difference between the maximum and minimum ambient temperatures within a time window was calculated. For example, if the ambient temperature rose from -5.2℃ to -2.8℃ within a 10-minute period, the ambient temperature fluctuation was 2.4℃. Simultaneously, the amplitude of internal temperature changes within the corresponding time period was calculated; for example, a change from 2.85℃ to 3.10℃ resulted in an internal temperature fluctuation of 0.25℃. The ratio of these two changes—the ratio of the internal temperature response amplitude to the ambient temperature fluctuation amplitude—was then calculated; for example, 0.25 / 2.4 ≈ 0.10. Further time difference analysis was used to determine the lag time between environmental changes and internal responses. For example, if the ambient temperature showed a significant upward trend at 10:10:00, while the internal temperature began to change synchronously at 10:11:30, the response delay was approximately 90 seconds. The correlation coefficient, ambient temperature fluctuation amplitude, internal response amplitude, and response delay time together constituted the environmental interference characteristics. For example, an interference feature vector (R=0.57, ambient temperature fluctuation=2.4℃, response amplitude=0.25℃, response delay=90s) was formed to describe the interference pattern of external ambient temperature on the internal temperature of the cold chain.

[0031] Environmental impact correction is applied to the temperature data in the first parameter sequence to reduce the interference of external temperature changes on the internal temperature monitoring data. The process begins by establishing a temperature compensation coefficient based on the response ratio in the environmental interference characteristics. For example, analysis shows the internal temperature response ratio is approximately 0.10. When the ambient temperature changes by 1.5℃ over a certain time period, it can be estimated that this change may cause a fluctuation of approximately 0.15℃ in the internal temperature. Subsequently, the temperature data in the first parameter sequence for the corresponding time period are compensated and corrected. For example, if the internal temperature sampling value at a certain time point is 3.18℃, and the ambient temperature is detected to rise by 1.6℃ during that time period, the environmental impact value is calculated to be approximately 0.16℃ based on the response ratio of 0.10. Subtracting this impact value from the original temperature yields a corrected temperature of approximately 3.02℃. To ensure the continuous stability of the temperature sequence, the corrected data is further smoothed using an exponential smoothing method to keep temperature changes stable across consecutive time points. For example, a smoothing coefficient of 0.2 is set, and a weighted average is applied to adjacent temperature data. After continuous compensation and smoothing, new temperature time series can be obtained, such as (10:30:00, 2.98℃), (10:30:05, 3.00℃), (10:30:10, 3.01℃), etc.

[0032] In this embodiment, step S3 includes the following steps: The second parameter sequence is statistically processed to obtain the normal distribution interval; Based on the normal distribution interval, abnormal distribution deviations are identified, and abnormal noise points and outlier parameters are marked; Abnormal noise points are repaired by interpolation, and outlier parameters are removed, outputting the third parameter sequence.

[0033] In this embodiment, the processing first selects a data window of a certain time length, such as 30 minutes or 60 minutes, as the statistical interval. If the temperature sampling period is set to 5 seconds, approximately 360 temperature data points can be obtained within a 30-minute time period. Basic statistical calculations are performed on this dataset, including statistical indicators such as temperature mean, variance, and standard deviation. The mean is calculated by summing all temperature values ​​and dividing by the number of data points; for example, the average of 360 temperature data points within a certain time window is 3.05℃. Subsequently, the deviation between each temperature value and the mean is squared and averaged to obtain the variance; for example, the variance is approximately 0.018. The standard deviation is obtained by taking the square root of the variance; for example, the standard deviation is approximately 0.134℃. After obtaining the mean and standard deviation, temperature distribution intervals can be constructed based on the normal distribution law. Typically, the mean ± 1 standard deviation is used as the primary data concentration interval, for example, 2.92℃~3.18℃; the mean ± 2 standard deviations is used as the extended interval, for example, 2.78℃~3.32℃. Temperature data within this range typically accounts for about 95% of the total data and can represent the normal temperature variation range.

[0034] Deviation analysis was performed on all temperature data in the second parameter sequence to identify anomalous noise points and outliers that significantly deviated from the normal range. The analysis first calculated the difference between each temperature value and the mean, and then determined the degree of deviation based on the standard deviation. Temperature values ​​falling within ±1 standard deviation of the mean were considered within the normal fluctuation range; values ​​between 1 and 2 standard deviations were considered slight fluctuations; and values ​​exceeding 2 standard deviations required further anomaly identification. For example, if the mean was 3.05℃ and the standard deviation was 0.13℃ within a certain statistical interval, then 2 standard deviations would be between 2.79℃ and 3.31℃. A temperature of 3.48℃ at a given sampling time point was significantly higher than the upper limit of 3.31℃ and could be identified as an outlier. Temperature points with small amplitude but abrupt changes were identified as anomalous noise points. For example, in a continuous temperature sequence, the following data appears: 3.02℃, 3.03℃, 3.65℃, 3.04℃, 3.05℃. Among these, 3.65℃ shows a significant difference from the preceding and following temperatures and lasts for a very short period, so it can be identified as an instantaneous noise point. During the identification process, temporal continuity is also considered. If a temperature value shows an anomaly only once and quickly returns to the normal range at the next sampling point, it is marked as an abnormal noise point; if a temperature value persists for multiple sampling periods and is far from the normal distribution range, it is marked as an outlier parameter.

[0035] Linear interpolation is performed using two adjacent normal temperature values ​​before and after an outlier. For example, in a time series, temperature data appear: 3.01℃, 3.03℃, 3.62℃, 3.05℃, and 3.06℃, where 3.62℃ is marked as a noise point. During interpolation, the preceding and following normal data (3.03℃ and 3.05℃) are used as references, and linear calculation is performed based on the time interval to obtain a corrected temperature value of approximately 3.04℃, which is then used to replace the original outlier data. For cases with multiple consecutive noise points, multi-point interpolation methods, such as quadratic interpolation or moving average interpolation, can be used to ensure that the corrected temperature trend remains smooth and continuous. For temperature values ​​marked as outliers, data removal is used, i.e., the temperature point is directly deleted from the series. For example, if the temperature data for a certain time period are 3.02℃, 3.04℃, 3.35℃, 3.36℃, and 3.03℃, then the consecutive points of 3.35℃ and 3.36℃ significantly exceed the normal distribution range and persist for a relatively long period. These can be considered outliers and removed entirely. After removal, the remaining data are rearranged in chronological order and then subjected to simple smoothing, such as using a three-point moving average to smooth adjacent temperature data. Through noise point interpolation repair and outlier parameter removal, a stable and continuous temperature data sequence can be obtained, for example, forming a new sequence (3.01℃, 3.03℃, 3.04℃, 3.05℃, 3.06℃…).

[0036] In this embodiment, step S4 includes the following steps: Calculate the spatial deployment coordinates of the sensing devices; Based on the spatial deployment coordinates, the spatial temperature distribution of the third parameter sequence is calculated to obtain temperature values ​​at multiple locations; The temperature gradient, temperature change rate, and temperature fluctuation amplitude are calculated based on temperature values ​​from multiple locations to generate multidimensional temperature distribution characteristics. Based on the aforementioned multidimensional temperature distribution characteristics, spatial temperature distribution modeling is performed to construct a spatial temperature distribution field.

[0037] In this embodiment, a three-dimensional coordinate system is established based on the internal geometry of the cold chain transport container. The origin (0, 0, 0) is set at the front left corner of the container's bottom. The length of the container is set as the X-axis, the width as the Y-axis, and the height as the Z-axis. For example, if the internal dimensions of a medical cold chain transport box are 2.0m long, 1.5m wide, and 1.6m high, then the X-axis range is 0–2.0m, the Y-axis range is 0–1.5m, and the Z-axis range is 0–1.6m. Subsequently, the installation position of each sensing device is measured and recorded. The distance between the device and the container boundary can be measured using a laser rangefinder or a measuring tape to obtain the three-dimensional spatial coordinates of each node. For example, a node installed in the central area of ​​the top of the container can be represented as (1.0m, 0.75m, 1.45m); another node is located in the rear area of ​​the bottom, and its coordinates can be represented as (1.8m, 1.2m, 0.25m). All node coordinates are bound by device IDs. For example, Node-01 corresponds to (0.5m, 0.4m, 1.3m), Node-02 corresponds to (1.2m, 0.6m, 0.9m), and Node-03 corresponds to (1.6m, 1.0m, 0.4m), etc. After completing the measurement of all device positions, a complete set of spatial node coordinates can be formed.

[0038] Temperature data for each device node is extracted according to a unified time point. For example, at time 10:30:00, the temperature of Node-01 is 3.02℃, Node-02 is 3.06℃, Node-03 is 3.11℃, and Node-04 is 2.98℃. These temperature values ​​are combined with their corresponding spatial coordinates to form spatial temperature data points, such as (0.5m, 0.4m, 1.3m, 3.02℃), (1.2m, 0.6m, 0.9m, 3.06℃), and (1.6m, 1.0m, 0.4m, 3.11℃). Since the number of sensors is limited and cannot cover all spatial locations inside the container, spatial interpolation methods are needed to estimate the temperature in areas without sensors. Interpolation calculations can be performed using the inverse distance weighting method or spatial interpolation methods. The specific method involves selecting 3-4 monitoring nodes closest to a target location as references, and using the reciprocal of the distance between the node and the target point as a weight for temperature estimation. For example, if the target location is 0.3m, 0.5m, and 0.8m away from three nodes, the corresponding weights are approximately 3.33, 2.00, and 1.25, respectively. After weighting the node temperature values ​​according to these weights, the temperature value at the target location can be estimated to be approximately 3.05℃.

[0039] Temperature gradient calculation is performed to analyze the degree of temperature change between adjacent spatial locations. For example, if the distance between two spatial points is 0.5m, and the temperature at one point is 3.05℃ while the temperature at the other point is 3.20℃, the temperature difference is 0.15℃, and the temperature gradient is approximately 0.30℃ / m. By calculating for multiple adjacent spatial grid points, the temperature gradient distribution in various directions inside the container can be obtained. Subsequently, the rate of temperature change is calculated to reflect how quickly the temperature changes over time. For example, if the temperature at a certain spatial node is 3.01℃, 3.05℃, and 3.09℃ at three consecutive time points, with a sampling interval of 5 seconds, the rate of temperature change is approximately (3.09℃ / m). 3.01) / 10 seconds ≈ 0.008℃ / second. A temperature change rate sequence can be formed through continuous calculation. Simultaneously, the temperature fluctuation amplitude is statistically analyzed, that is, the difference between the maximum and minimum temperature values ​​within a certain time window is calculated. For example, if the highest temperature at a certain spatial location within a 10-minute time window is 3.18℃ and the lowest is 2.96℃, then the temperature fluctuation amplitude is 0.22℃.

[0040] The modeling process begins by establishing a regular spatial grid based on the container's three-dimensional coordinates. For example, the grid is divided along the X, Y, and Z directions at 0.2m intervals, resulting in a large number of three-dimensional grid units. For instance, approximately 1200 spatial grid nodes can be formed inside a container with dimensions of 2.0m × 1.5m × 1.6m. Subsequently, the spatial temperature data obtained in step two, along with the characteristic parameters such as temperature gradient, rate of change, and fluctuation amplitude calculated in step three, are used as inputs to estimate the temperature of each grid node. During the estimation process, the temperature values ​​of neighboring nodes are weighted according to the gradient direction to ensure that the calculation results both match actual monitoring data and maintain spatial continuity. For example, if a high temperature gradient (approximately 0.35℃ / m) is detected in a certain area, the weight of temperature change in that direction is appropriately increased in the model calculation, enabling the model to reflect local temperature change trends. After calculation, each spatial grid node corresponds to a temperature estimate, such as (1.0m, 0.8m, 0.9m, 3.08℃) or (1.2m, 0.6m, 1.1m, 3.12℃). The combination of all node temperature values ​​forms a complete three-dimensional temperature data set, which is the temperature distribution field inside the cold chain transport container.

[0041] In this embodiment, step S5 includes the following steps: By dynamically tracking the changes in the spatial temperature distribution field, the temperature fluctuation distribution field can be obtained. The temperature fluctuation distribution field is losslessly encoded and compressed to construct a compressed package; Set a short-frequency transmission cycle to transmit the compressed package to the cloud platform and complete the data acquisition and transmission operation.

[0042] In this embodiment, the spatial temperature distribution data is continuously updated according to a predetermined sampling period. For example, if the temperature sampling period is set to 5 seconds, a new spatial temperature distribution field can be generated every 5 seconds. By comparing and calculating the temperature distribution fields at consecutive time nodes, the fluctuation value of the temperature of each spatial grid node over time can be obtained. For example, at a spatial grid node location (1.2m, 0.8m, 0.9m), the temperature values ​​at three consecutive sampling times are 3.02℃, 3.05℃, and 3.08℃, respectively. The temperature change within a 10-second time span is approximately 0.06℃. By performing similar calculations on all spatial nodes, the temperature change corresponding to each node can be obtained. Furthermore, by statistically analyzing the maximum and average fluctuation values ​​of each node within a certain time window, for example, calculating 12 sets of temperature change data within a 1-minute time window, the maximum fluctuation value of a certain node is approximately 0.12℃, and the average fluctuation value is approximately 0.05℃. By reorganizing all node temperature fluctuation values ​​according to spatial coordinates, a new spatial distribution data structure can be formed, where each spatial node corresponds to a temperature fluctuation parameter, such as (0.8m, 0.6m, 0.7m, 0.04℃) or (1.4m, 0.9m, 1.1m, 0.09℃). Through this continuous time tracking method, the spatial distribution of temperature fluctuation intensity in different regions inside the container can be obtained, and this dataset is the temperature fluctuation distribution field.

[0043] The temperature fluctuation data is structured and organized by arranging the spatial node coordinates and corresponding fluctuation values ​​in a unified format, such as storing them using a data structure of "node number + spatial coordinates + temperature fluctuation value". For example, a node record can be represented as Node-05: (1.2m, 0.8m, 0.9m, 0.06℃). After sorting all node data in spatial order, a complete dataset is formed. The dataset is then processed by differential encoding, recording the difference between the temperature fluctuation values ​​of adjacent nodes. For example, if the temperature fluctuation values ​​in a sequence are 0.04℃, 0.05℃, 0.06℃, and 0.07℃, the difference values ​​are 0.01℃, 0.01℃, and 0.01℃, respectively. This method reduces repetitive numerical information, thereby reducing data redundancy. Based on differential encoding, lossless compression algorithms are further used for encoding, such as dictionary-based compression, to uniformly encode recurring data patterns. For example, patterns like "0.01℃ change" or "0.02℃ change" may frequently appear in large amounts of node data. By establishing a compression dictionary, these patterns can be converted into shorter coded identifiers. After encoding, the compressed data is combined with time identifier information, such as adding the time identifier "10:45:00" as the data block header. Then, all coded data is encapsulated to generate a data compressed package with a unified format. After compression, the data size can be significantly reduced. For example, the original temperature fluctuation data size is approximately 80KB, which can be reduced to approximately 25KB after differential encoding and lossless compression, thereby improving the efficiency of subsequent data transmission.

[0044] During transmission, a data transmission period is first set, such as once every 30 or 60 seconds. Temperature fluctuation data compressed packets generated within this period are then sent uniformly. Each compressed packet contains a timestamp, node data encoding, and data integrity verification information. For example, a compressed data packet is generated at time node 10:45:00, recording the temperature fluctuation information of all spatial nodes within that time period. Data transmission is completed through a wireless communication module, using 4G, 5G, or low-power wide-area communication methods. During transmission, a communication connection is first established, and the compressed packets are sent in segments. For example, a 25KB compressed data packet can be divided into 5 data blocks, each approximately 5KB, and uploaded continuously. Each data block is accompanied by a checksum to verify data integrity. When the receiving end detects that the checksum matches, the data transmission is considered successful. If a data block is lost or erroneous, it is retransmitted to ensure data integrity. Through continuous periodic transmission, temperature fluctuation distribution data can be continuously uploaded to the remote platform throughout the transportation process. For example, during a 2-hour transportation process, if the transmission period is 30 seconds, approximately 240 compressed data packets can be uploaded. All uploaded data is stored in chronological order to form a complete temperature monitoring data record.

[0045] In this embodiment, the specific steps for setting a short-frequency transmission period, transmitting the compressed package to the cloud platform, and completing the data acquisition and transmission operation are as follows: Based on the collection of communication link parameters by sensing devices; Based on the communication link parameters, the link signal strength, transmission delay and packet loss rate are calculated, and the link quality is evaluated to generate a link quality index. Dynamic routing is performed based on the link quality index to obtain the optimal transmission path; Set a short-frequency transmission period; based on the short-frequency transmission period and the optimal transmission path, transmit the compressed package to the cloud platform to complete the data acquisition and transmission operation.

[0046] In this embodiment, each sensing device synchronously records the link status information of the wireless communication module when sending or receiving data, including parameters such as signal strength, channel noise level, number of data transmissions, and number of data acknowledgments. For example, when a node sends a data packet, it records a received signal strength of -72 dBm and a channel noise of approximately -95 dBm. To obtain stable link parameters, multiple measurements are performed during each data transmission process. For example, five signal strength values ​​are continuously collected within a 10-second time window, such as -71 dBm, -73 dBm, -72 dBm, -74 dBm, and -72 dBm. These values ​​are then averaged to obtain an average link signal strength of approximately -72.4 dBm for that time period. Simultaneously, the communication module also records the number of data packet transmissions and the number of received acknowledgments. For example, if 100 data packets are sent within a certain time window and 95 are successfully received and acknowledged, the number of successful transmissions is recorded as 95. In addition, the data transmission start time and the reception acknowledgment time are recorded for subsequent calculation of transmission delay. For example, if a data packet is sent at 10:52:05.100 and received at 10:52:05.145, the single transmission delay is approximately 45 milliseconds. By continuously collecting signal strength, channel noise, number of transmissions, number of acknowledgments, and transmission time information, a complete set of communication link parameters can be formed, thus providing basic data for subsequent link quality analysis.

[0047] The analysis process begins with statistical calculations on the collected signal strength data. For example, 10 sets of signal strength data are collected within a 20-second timeframe, and the average signal strength of the link is calculated. For instance, an average signal strength of -72 dBm for a certain link indicates a relatively stable wireless signal. Next, data transmission delay is calculated by subtracting the transmission time from the reception confirmation time. For example, if delay values ​​of 45 milliseconds, 48 ​​milliseconds, 50 milliseconds, 47 milliseconds, and 46 milliseconds are recorded in 10 data transmissions, the average transmission delay is approximately 47 milliseconds. Then, packet loss rate is calculated by subtracting the number of successful transmissions from the number of successful receptions. For example, if 95 out of 100 data transmissions are successfully received, there are 5 packet losses, resulting in a packet loss rate of approximately 5%. After obtaining signal strength, transmission delay, and packet loss rate, these three indicators are comprehensively evaluated. First, each indicator is normalized; for example, signal strength is converted into a score range of 0 to 1, and delay time and packet loss rate are converted into corresponding score values. The link quality index is then calculated using a weighted approach. For example, the signal strength weight is set to 0.4, the transmission delay weight to 0.3, and the packet loss rate weight to 0.3. The weighted sum is then used to obtain the link quality index. For instance, if a link has a signal strength score of 0.82, a delay score of 0.75, and a packet loss rate score of 0.70, the overall link quality index is approximately 0.77.

[0048] Each sensor node is considered a communication node in the network, and multiple communication paths may exist between different nodes. For example, Node-01 can send data directly to the gateway via Node-02, or it can relay the data via Node-03. Link quality indices are calculated for all possible paths. For instance, if the link quality index from Node-01 to Node-02 is 0.78 and the link quality index from Node-02 to the gateway is 0.81, the overall quality of this path is approximately 0.79. If the link quality index from Node-01 to Node-03 is 0.74 and the link quality index from Node-03 to the gateway is 0.76, the overall quality of this path is approximately 0.75. By comparing the overall quality indices of different paths, the path with the highest quality index can be selected as the data transmission path. For example, Node-01→Node-02→Gateway can be selected as the optimal transmission path. To adapt to changes in the communication environment, the link quality index is continuously updated during data transmission. For example, the link quality index is recalculated every 30 seconds. When a decrease in signal strength or an increase in packet loss rate is detected on a link, the path quality is reassessed, and the system automatically switches to a path with a higher quality index. For example, when the link signal of Node-02 drops to -85 dBm, the overall link quality index drops to 0.62, so Node-03 is selected as the new relay node to maintain data transmission stability.

[0049] Within each transmission cycle, the already generated temperature monitoring data compressed package is used as the data to be sent. The data packet is first segmented and sent according to the optimal transmission path. For example, a 20KB data compressed package is divided into 4 data blocks, each approximately 5KB. Data is forwarded node by node in the path sequence. For example, Node-01 first sends the data to Node-02, and then Node-02 forwards it to the gateway device. After each data block is sent, the sending time is recorded, and the system waits for an acknowledgment signal from the next node. For example, if a data block is sent at 11:05:20.200 and the acknowledgment time is 11:05:20.245, the transmission delay for that data block is approximately 45 milliseconds. If no acknowledgment signal is received within the specified time, the data is automatically retransmitted. For example, the maximum number of retransmissions is set to 3 to ensure complete data transmission. After all data blocks have been sent, the gateway device sends the received data compressed package to a remote cloud platform via a cellular network or internet connection. By using a continuous short-cycle sending method, temperature monitoring data can be continuously uploaded throughout the entire transportation process. For example, during a 3-hour transportation process, if the transmission cycle is set to 30 seconds, approximately 360 data transmissions can be completed, thereby forming a complete temperature monitoring data record and realizing the stable acquisition and remote transmission of medical cold chain temperature monitoring data.

[0050] In this embodiment, as Figure 4 A data acquisition and transmission apparatus for a sensing device is provided, used to execute the data acquisition and transmission method for the sensing device as described above, comprising: A sampling unit is used to deploy multiple sensing devices on a cold chain transport container; based on the sensing devices, the temperature of the medical goods is continuously sampled to generate a first parameter sequence; The suppression unit is used to suppress environmental interference in the first parameter sequence and generate the second parameter sequence. The elimination unit is used to perform outlier elimination on the second parameter sequence and output the third parameter sequence. The distribution modeling unit is used to model the spatial temperature distribution of the third parameter sequence and construct the spatial temperature distribution field. The transmission unit is used to perform lossless encoding and compression of the spatial temperature distribution field, set a short-frequency transmission period, and transmit the compressed package to the cloud platform to complete the data acquisition and transmission operation.

[0051] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0052] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A data acquisition and transmission method for a sensing device, characterized in that, Includes the following steps: Deploy multiple sensing devices on cold chain transport containers; Based on the sensing device, the temperature of the medical goods is continuously sampled to generate a first parameter sequence; Environmental interference suppression is applied to the first parameter sequence to generate the second parameter sequence; Perform outlier removal on the second parameter sequence and output the third parameter sequence; Model the spatial temperature distribution of the third parameter sequence to construct a spatial temperature distribution field; The spatial temperature distribution field is losslessly encoded and compressed, a short-frequency transmission period is set, and the compressed package is transmitted to the cloud platform to complete the data acquisition and transmission operation.

2. The data acquisition and transmission method of the sensing device according to claim 1, characterized in that, The specific steps for deploying multiple sensing devices on the cold chain transport container and continuously sampling the temperature of the medical goods based on the sensing devices to generate the first parameter sequence are as follows: Deploy multiple sensing devices on cold chain transport containers; The temperature parameters are obtained by continuously sampling the temperature of the medical goods based on the aforementioned sensing device. Synchronous acquisition of parameter sampling timestamps; The temperature parameters are integrated in time sequence based on the parameter sampling timestamp to generate the first parameter sequence.

3. The data acquisition and transmission method of the sensing device according to claim 1, characterized in that, The specific steps for suppressing environmental interference in the first parameter sequence and generating the second parameter sequence are as follows: Identify ambient temperature monitoring values; Temperature correlation analysis was performed on the first parameter sequence based on the ambient temperature monitoring values ​​to obtain the correlation coefficient; Environmental interference characteristics are generated by performing environmental interference analysis based on correlation coefficients. Environmental interference is suppressed based on environmental interference characteristics to generate a second parameter sequence.

4. The data acquisition and transmission method of the sensing device according to claim 1, characterized in that, The specific steps for performing outlier removal on the second parameter sequence to output the third parameter sequence are as follows: The second parameter sequence is statistically processed to obtain the normal distribution interval; Based on the normal distribution interval, abnormal distribution deviations are identified, and abnormal noise points and outlier parameters are marked; Abnormal noise points are repaired by interpolation, and outlier parameters are removed, outputting the third parameter sequence.

5. The data acquisition and transmission method of the sensing device according to claim 4, characterized in that, The abnormal noise points specifically include sensor noise, equipment vibration, and local airflow disturbance.

6. The data acquisition and transmission method of the sensing device according to claim 1, characterized in that, The specific steps for modeling the spatial temperature distribution of the third parameter sequence and constructing the spatial temperature distribution field are as follows: Calculate the spatial deployment coordinates of the sensing devices; Based on the spatial deployment coordinates, the spatial temperature distribution of the third parameter sequence is calculated to obtain temperature values ​​at multiple locations; The temperature gradient, temperature change rate, and temperature fluctuation amplitude are calculated based on temperature values ​​from multiple locations to generate multidimensional temperature distribution characteristics. Based on the aforementioned multidimensional temperature distribution characteristics, spatial temperature distribution modeling is performed to construct a spatial temperature distribution field.

7. The data acquisition and transmission method of the sensing device according to claim 1, characterized in that, The specific steps for performing lossless encoding and compression of the spatial temperature distribution field, setting a short-frequency transmission period, and transmitting the compressed package to the cloud platform to complete the data acquisition and transmission operation are as follows: By dynamically tracking the changes in the spatial temperature distribution field, the temperature fluctuation distribution field can be obtained. The temperature fluctuation distribution field is losslessly encoded and compressed to construct a compressed package; Set a short-frequency transmission cycle to transmit the compressed package to the cloud platform and complete the data acquisition and transmission operation.

8. The data acquisition and transmission method of the sensing device according to claim 1, characterized in that, The specific steps for setting a short-frequency transmission period, transmitting the compressed package to the cloud platform, and completing the data acquisition and transmission operation are as follows: Based on the collection of communication link parameters by sensing devices; Based on the communication link parameters, the link signal strength, transmission delay and packet loss rate are calculated, and the link quality is evaluated to generate a link quality index. Dynamic routing is performed based on the link quality index to obtain the optimal transmission path; Set a short-frequency transmission period; based on the short-frequency transmission period and the optimal transmission path, transmit the compressed package to the cloud platform to complete the data acquisition and transmission operation.

9. The data acquisition and transmission method of the sensing device according to claim 8, characterized in that, The medical supplies are affixed with RFID tags; by scanning the RFID tags, data analysis is performed on the compressed package retrieved via a cloud platform.

10. A data acquisition and transmission device for a sensing device, characterized in that, The data acquisition and transmission method for performing the sensing device as described in claim 1 includes: A sampling unit is used to deploy multiple sensing devices on a cold chain transport container; based on the sensing devices, the temperature of the medical goods is continuously sampled to generate a first parameter sequence; The suppression unit is used to suppress environmental interference in the first parameter sequence and generate the second parameter sequence. The elimination unit is used to perform outlier elimination on the second parameter sequence and output the third parameter sequence. The distribution modeling unit is used to model the spatial temperature distribution of the third parameter sequence and construct the spatial temperature distribution field. The transmission unit is used to perform lossless encoding and compression of the spatial temperature distribution field, set a short-frequency transmission period, and transmit the compressed package to the cloud platform to complete the data acquisition and transmission operation.

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