A dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents

Abstract

This invention relates to the field of reagent cold chain transportation monitoring technology, specifically disclosing a dynamic monitoring and adjustment system for the cold chain transportation of in vitro diagnostic reagents. This system addresses the problem that uneven stacking of in vitro diagnostic reagents can create localized hot or cold zones, preventing certain areas from reaching or maintaining set temperatures in a timely manner. The invention acquires global temperature data and baseline three-dimensional temperature data within the warehouse, calculates a first abnormal temperature fluctuation value, automatically identifies abnormal deviations between local and global temperature data, ensuring temperature data consistency, and quantitatively calculates temperature gradients from both global and local perspectives. This allows for detailed capture of temperature distribution characteristics within the warehouse. By comparing temperature gradient differences calculated from different data sources and setting reasonable thresholds, automatic early warning is achieved, thereby ensuring a stable temperature control environment and protecting the quality of in vitro diagnostic reagents.

Description

Technical Field

[0001] This invention relates to the field of reagent cold chain transportation monitoring technology, and more specifically, to a dynamic monitoring and regulation system for cold chain transportation of in vitro diagnostic reagents. Background Technology

[0002] In vitro diagnostic reagents are highly specialized and have strict requirements for transportation conditions. Therefore, the transportation method and temperature monitoring during transit are key aspects of the entire cold chain management of in vitro diagnostic reagents. Current literature (Shi Hong, Yu Ling, Xu Haiqing. Cold chain management of in vitro diagnostic reagents [J]. Medical Equipment, 2020, 33(03):65-68.) mentions that some large-volume suppliers use refrigerated trucks to transport reagents. The refrigerated trucks are equipped with cold chain transportation environment monitoring system software and multiple temperature detection probes, which can automatically monitor, record, and alarm for abnormal conditions of the reagents during transportation. The data is transmitted to the relevant management departments of the company via GPS. In addition, the trucks are equipped with embedded printers, which can print out the temperature data during transportation when the reagents arrive at their destination. However, refrigerated trucks usually use centralized refrigeration systems, which use fans to send cold air into the compartment. When cold air enters through the air vents, it is affected by factors such as the structure of the vehicle compartment, the design of the air ducts, and the obstruction of cargo, which often create a temperature gradient inside the vehicle compartment. The temperature is lower in the area near the air vents, while the temperature may be higher in the area away from the air vents or near the doors. This temperature difference may cause local areas to exceed the ideal temperature control range for temperature-sensitive in vitro diagnostic reagents.

[0003] Existing refrigeration vehicles use multi-evaporator (mostly dual-evaporator) refrigeration units to physically divide the refrigerated truck compartment into a multi-temperature zone structure. However, when making such modifications, it should be noted that the dual-evaporator layout will have a significant impact on the temperature distribution inside the compartment. Existing literature (Zhou Fei, Zhu Zheng. Refrigerated truck configuration and verification experience in pharmaceutical cold chain [J]. Shanghai Pharmaceuticals, 2017, 38(23):64-66.) provides examples such as... Figure 2 The diagram illustrates the temperature distribution within the rear of the vehicle, near the rear evaporator. Reducing the space at the rear of the vehicle compartment minimizes the presence of high-temperature areas. However, this multi-temperature zone structure can create significant temperature gradients during refrigeration, especially in larger rear compartments where cold air is difficult to distribute evenly. This temperature difference not only increases the risk of temperature fluctuations affecting in vitro diagnostic reagents but may also lead to insufficient temperature control in certain areas, thus impacting the long-term stability of the reagents. Since cold air is heavier, it typically exhibits a gradual temperature increase from bottom to top within the vehicle compartment. In vitro diagnostic reagents are often stacked in boxes during transportation, and this stacking method can affect the flow of cold air. Dense or uneven stacking can create localized hot or cold spots, preventing certain areas from reaching or maintaining their set temperatures in a timely manner. To address these issues, a technical solution is proposed. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, this invention provides a dynamic monitoring and adjustment system for cold chain transportation of in vitro diagnostic reagents. By automatically identifying abnormal deviations between local temperature data and global data, it solves the problem that uneven stacking of in vitro diagnostic reagents can create local hot spots or cold zones, thus preventing certain areas from reaching or maintaining the set temperature in a timely manner. This ensures a stable temperature control environment and protects the quality of in vitro diagnostic reagents, thereby addressing the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents includes a multi-source temperature monitoring module, a reference calibration unit, a primary temperature difference analysis unit, a secondary temperature difference analysis unit, and a regional temperature difference verification unit. The multi-source temperature monitoring module acquires global temperature data and reference three-dimensional temperature data within the warehouse. The primary temperature difference analysis unit is based on the global temperature data T... q (x i ,y i ,z i Obtain the first temperature gradient difference in the vertical direction within the warehouse. and the second temperature gradient difference in the horizontal direction The secondary temperature difference analysis unit is used to obtain the baseline three-dimensional temperature data sequence I1={T1,…,T i ,…,T N The inner wall of the refrigerated truck's cargo compartment, when unloaded, is evenly divided into several cargo space blocks along both the vertical and horizontal directions. The three-dimensional position coordinates (x, y, z) of the center position of each cargo space block are obtained. j ,y i ,z j Based on the baseline three-dimensional temperature data sequence, a continuous temperature field is generated inside the warehouse using a three-dimensional interpolation algorithm to obtain the three-dimensional temperature data of each warehouse space block, forming a first-level three-dimensional temperature data sequence I2={T1,…,T q ,…,T Q}, T q The three-dimensional temperature data of the q-th warehouse space block is given, where Q is the number of warehouse space blocks; the third temperature gradient difference in the vertical direction within the warehouse is obtained based on the first-level three-dimensional temperature data sequence I2. and the fourth temperature gradient difference in the horizontal direction The regional temperature difference verification unit is used to extract the first temperature gradient difference. Second temperature gradient difference Third temperature gradient difference and the fourth temperature gradient difference Determine whether the temperature is uniform within the warehouse area.

[0007] As a further aspect of the present invention, the multi-source temperature monitoring module includes two infrared thermal imaging cameras and a high-precision temperature sensor; the infrared thermal imaging cameras are installed diagonally on the top surface of the refrigerated truck's cargo compartment; the high-precision temperature sensor acquires the cargo compartment surface when it is unloaded, divides the opposite cargo compartment surfaces into several equal and uniform segments, and installs a high-precision temperature sensor in each segment to acquire a reference three-dimensional temperature data sequence I1={T1,…,T i ,…,T N}, where T i Let N be the i-th baseline 3D temperature data, and N be the total number of baseline 3D temperature data; global temperature data T inside the warehouse is acquired based on an infrared thermal imaging camera. q (x i ,y i ,z i ); Based on high-precision temperature sensors, benchmark three-dimensional temperature data inside the warehouse is obtained.

[0008] Infrared thermal imaging cameras are installed diagonally from the top of the refrigerated truck's cargo compartment, covering the entire compartment and acquiring a continuous, macroscopic global temperature field. High-precision temperature sensors are evenly distributed on the cargo compartment surface and obtain a reference three-dimensional temperature data sequence through segmentation blocks, providing high-precision and detailed temperature information, which serves as a reference for global data correction and local anomaly detection.

[0009] By using the reference temperature data provided by high-precision sensors, the global temperature field obtained by infrared thermal imaging cameras can be corrected to compensate for errors caused by factors such as surface material and reflection in infrared temperature measurement. At the same time, the global perspective provided by infrared data can help identify areas with abnormal temperatures, improve the utilization rate of local sensor data, and form a more accurate three-dimensional temperature field.

[0010] As a further aspect of the present invention, the abnormal swallowing recognition unit plots the mean change curve of the red channel based on the R value of facial color change characteristics, plots the mean change curve of brightness based on the brightness distribution value, and calculates the facial color change rate D of adjacent frames. diff (i) = |D(i+1)-D(i)|, where D(i) is the second face color feature value of the i-th frame, and D(i+1) is the face color feature value of the (i+1)-th frame. Calculate the mean of the face color change rate. and standard deviation σ Δd The user's level four swallowing recognition range is determined as follows: α4 is the fourth threshold adjustment factor, which regulates the user's medication flow rate based on the four-level swallowing recognition range, and obtains the user's real-time second facial feature value D(i): when At this time, the patient is in an abnormal swallowing state, so issue an instruction to reduce the flow rate of the medication or to stop administering the medication.

[0011] As a further aspect of the present invention, the reference calibration unit is used to calibrate based on global temperature data T. q (x i ,y i ,z i The first abnormal temperature fluctuation value ΔT at the same location within the warehouse was calculated using the baseline three-dimensional temperature data sequence I1. i =|T i -T q (x i ,y i ,z i The first abnormal temperature fluctuation value is compared with the preset abnormal temperature fluctuation threshold. If the first abnormal temperature fluctuation value is greater than or equal to the preset abnormal temperature fluctuation threshold, the reference three-dimensional temperature data at that location needs to be adjusted; if the first abnormal temperature fluctuation value is less than the preset abnormal temperature fluctuation threshold, the reference three-dimensional temperature data at that location does not need to be adjusted.

[0012] The primary temperature difference analysis unit is based on global temperature data T. q (x i ,y i ,z i The temperature gradient differences in the vertical and horizontal directions within the warehouse are obtained separately. The global temperature data is then combined with the depth information within the warehouse to obtain the first vertical temperature value. and the first transverse temperature value

[0013] Obtain the midpoint of the warehouse height, divide the warehouse into upper and lower layers based on the midpoint, and calculate the first average temperature gradient difference value of the upper layer area. and the first average temperature gradient difference value of the lower area of ​​the warehouse. The difference is the first temperature gradient difference.

[0014] Obtain the midpoint of the warehouse length, divide the warehouse into front and rear sections based on the midpoint, and calculate the first average temperature gradient difference value of the front section of the warehouse. and the first average temperature gradient difference value in the rear area of ​​the warehouse. The difference between these values ​​yields the second temperature gradient difference.

[0015] As a further aspect of the present invention, the secondary temperature difference analysis unit is based on the primary three-dimensional temperature data sequence I2={T1,…,T… q ,…,T Q} Calculate the second temperature gradient difference in the vertical and horizontal directions within the warehouse, respectively. The second temperature gradient difference includes the second longitudinal temperature value and the second transverse temperature value.

[0016] For adjacent segments on the same horizontal plane but at different heights, obtain the center coordinates of segments q and q+1 in the z-direction, respectively. q and z q+1 The corresponding temperature is T q and T q+1 Calculate the second longitudinal temperature value

[0017] For adjacent segments on different horizontal planes on the same vertical plane, obtain the center coordinates of adjacent segments p and p+1 in the x-direction as x and x, respectively. p and x p+1 The corresponding temperature is T p and T p+1 Second transverse temperature value

[0018] As a further aspect of the present invention, the secondary temperature difference analysis unit obtains the number of segmented blocks in the vertical direction of the warehouse and divides them equally, calculating the second average longitudinal temperature gradient of the upper and lower layers respectively. If the number of segmented blocks M in the vertical direction of the warehouse is even, then it is calculated according to half of the number of segmented blocks. The warehouse is divided into upper and lower levels; if the number of vertically divided blocks in the warehouse is odd, then it is divided into half the number of blocks. The warehouse is divided into two levels, with the middle section in the vertical direction located in both the upper and lower levels.

[0019] The second average longitudinal temperature gradient in the upper layer of the warehouse is The temperature was obtained by averaging the second longitudinal temperature value of each segment in the upper region. Let N be the rate of change of the second longitudinal temperature value at segment a in the upper region with height z, which is the local temperature gradient at segment a. 上层 This represents the number of blocks in the upper-level region. To sum the local temperature gradients of all the upper-layer segments;

[0020] The second average longitudinal temperature gradient in the lower layer of the warehouse is The temperature was obtained by averaging the second longitudinal temperature value of each segment in the lower region. Let N be the rate of change of the second longitudinal temperature value at segment b in the lower layer region with height z, which is the local temperature gradient at segment b. 下层 This represents the number of blocks in the lower-level region. To sum the local temperature gradients of all the segments in the lower layer;

[0021] By calculating the second average longitudinal temperature gradient of the upper layer of the warehouse And the second average longitudinal temperature gradient of the lower layer of the warehouse is The difference is used to obtain the third temperature gradient difference.

[0022] As a further aspect of the present invention, the secondary temperature difference analysis unit obtains the number of partition blocks in the horizontal direction of the warehouse and divides them equally, and calculates the second average lateral temperature gradient of the upper and lower layers respectively. If the number of partition blocks R in the horizontal direction of the warehouse is even, then it is calculated according to half of the number of partition blocks. The warehouse is divided into front and rear sections; if the number of horizontally divided sections in the warehouse is odd, then it is divided into half the number of sections. The warehouse is divided into two areas, front and back. The middle section in the horizontal direction is located in both the front and back areas.

[0023] The second average lateral temperature gradient at the front of the warehouse is The second transverse temperature value of each segment in the front region is obtained by averaging, where N represents the rate of change of the second lateral temperature value at segment c in the front region with height z, which is also the local temperature gradient at segment c. 前部 This represents the number of blocks in the front region. To sum the local temperature gradients of all the front segmented blocks;

[0024] The second average lateral temperature gradient at the rear of the warehouse is The second transverse temperature value of each segment in the rear region is obtained by averaging the values. N represents the rate of change of the second lateral temperature value at segment d in the rear region with height z, which is also the local temperature gradient at segment d. 后部 This represents the number of blocks in the rear region. To sum the local temperature gradients of all the rear segmented blocks;

[0025] By calculating the second average temperature lateral gradient at the front of the warehouse And the second average lateral temperature gradient at the rear of the warehouse is The difference is used to obtain the fourth temperature gradient difference.

[0026] As a further aspect of the present invention, the regional temperature difference verification unit is used to extract the first temperature gradient difference. Second temperature gradient difference Third temperature gradient difference and the fourth temperature gradient difference To determine whether the temperature is uniform within the warehouse, the following steps are taken:

[0027] when and When the temperature inside the warehouse is uniform, no alarm is needed; otherwise, when the temperature inside the warehouse is uneven, an alarm needs to be triggered. Here, ΔT1 is the preset vertical temperature gradient threshold, and ΔT2 is the preset horizontal temperature gradient threshold.

[0028] The technical effects and advantages of the dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents of the present invention are as follows: The present invention acquires global temperature data and baseline three-dimensional temperature data in the warehouse, calculates the first abnormal temperature fluctuation value, automatically identifies abnormal deviations between local temperature data and global data, ensures the consistency of temperature data, quantitatively calculates the temperature gradient from both global and local perspectives, can capture the temperature distribution characteristics in the warehouse in detail, compares the temperature gradient differences calculated from different data sources, and sets reasonable thresholds to achieve automatic early warning, thereby ensuring a stable temperature control environment and protecting the quality of in vitro diagnostic reagents. Attached Figure Description

[0029] Figure 1 This invention provides a real-time temperature overview and thermal distribution display interface.

[0030] Figure 2 The present invention provides the temperature distribution inside the vehicle near the rear of the rear evaporator;

[0031] Figure 3 The 24-hour temperature index and trend chart provided for this invention;

[0032] Figure 4 The temperature range distribution histogram provided for this invention;

[0033] Figure 5 The temperature fluctuation scatter plot provided for this invention;

[0034] Figure 6 This is a schematic diagram of a dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents, provided by the present invention. Detailed Implementation

[0035] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described technical solutions are only a part of this invention, and not all of it. All other technical solutions obtained by those skilled in the art based on the technical solutions of this invention without inventive effort are within the scope of protection of this invention.

[0036] Example 1

[0037] Figure 6 The present invention provides a schematic diagram of a dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents. As shown in the figure, the dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents includes a multi-source temperature monitoring module, a reference calibration unit, a primary temperature difference analysis unit, a secondary temperature difference analysis unit, and a regional temperature difference verification unit. The multi-source temperature monitoring module is connected to the reference calibration unit, the primary temperature difference analysis unit, and the secondary temperature difference analysis unit, respectively. The primary temperature difference analysis unit and the secondary temperature difference analysis unit are connected to the regional temperature difference verification unit, respectively.

[0038] The multi-source temperature monitoring module is used to acquire global temperature data and baseline three-dimensional temperature data within the warehouse.

[0039] Specifically, the multi-source temperature monitoring module includes two infrared thermal imaging cameras and a high-precision temperature sensor;

[0040] Infrared thermal imaging cameras are installed diagonally on the top surface of the refrigerated truck's cargo compartment. High-precision temperature sensors acquire data about the cargo compartment surface when it is empty. The cargo compartment surface is then divided into several equal, pairwise segments. A high-precision temperature sensor is installed in each segment to acquire a baseline three-dimensional temperature data sequence I1 = {T1,…,T...}. i ,…,T N} Among them, T i Let N be the i-th reference three-dimensional temperature data, and N be the total number of reference three-dimensional temperature data.

[0041] Global temperature data (T) inside the warehouse was obtained using an infrared thermal imaging camera. q (x i ,y i ,z i ).

[0042] The high-precision temperature sensor is used to obtain the baseline three-dimensional temperature data inside the warehouse.

[0043] Specifically, the reference calibration unit is used to calibrate based on the global temperature data T. q (x i ,y i ,z i The first abnormal temperature fluctuation value ΔT at the same location within the warehouse was calculated using the baseline three-dimensional temperature data sequence I1. i =|T i -T q (x i ,y i ,z iThe first abnormal temperature fluctuation value is compared with the preset abnormal temperature fluctuation threshold. If the first abnormal temperature fluctuation value is greater than or equal to the preset abnormal temperature fluctuation threshold, the reference three-dimensional temperature data at that location needs to be adjusted; if the first abnormal temperature fluctuation value is less than the preset abnormal temperature fluctuation threshold, the reference three-dimensional temperature data at that location does not need to be adjusted.

[0044] By calculating the first abnormal temperature fluctuation value at each location, it is possible to intuitively compare whether the local temperature data obtained by the high-precision temperature sensor and the global temperature data acquired by the infrared thermal imaging camera meet the judgment criteria. This helps to identify deviations caused by measurement errors or local environmental interference. The infrared thermal imaging camera has global scanning capabilities, but it may be affected by factors such as surface material and angle, leading to local data deviations. In contrast, the high-precision temperature sensor provides high-precision local temperature information. By comparing the two, the reference calibration unit can use the high-precision local data to correct the global data, thereby constructing a more accurate three-dimensional temperature field.

[0045] Specifically, the primary temperature difference analysis unit is based on global temperature data T. q (x i ,y i ,z i The temperature gradient differences in the vertical and horizontal directions within the warehouse are obtained separately. The global temperature data is then combined with the depth information within the warehouse to obtain the first vertical temperature value. and the first transverse temperature value

[0046] Obtain the midpoint of the warehouse height, divide the warehouse into upper and lower layers based on the midpoint, and calculate the first average temperature gradient difference value of the upper layer area. and the first average temperature gradient difference value of the lower area of ​​the warehouse. The difference, i.e., the first temperature gradient difference.

[0047] Obtain the midpoint of the warehouse length, divide the warehouse into front and rear sections based on the midpoint, and calculate the first average temperature gradient difference value of the front section of the warehouse. and the first average temperature gradient difference value in the rear area of ​​the warehouse. The difference, i.e., the second temperature gradient difference.

[0048] By combining global temperature data with depth information and employing the central difference method to calculate the temperature gradients in both the vertical and horizontal directions, the rate of temperature change at different locations within the cargo hold can be accurately reflected, capturing localized hot spots or cold zones, thus providing data for subsequent temperature control adjustments. Dividing the cargo hold into upper and lower sections at the midpoint of its height and calculating the average temperature gradient difference between each section visually reflects the unevenness of the vertical temperature distribution. Dividing the cargo hold into front and rear sections at the midpoint of its length and calculating the horizontal temperature gradient difference allows for comparison of temperature changes between different areas, facilitating the identification of temperature differences between the front and rear sections. Infrared thermal imaging cameras provide the global temperature distribution of the cargo hold, while combining this with depth information to calculate temperature gradients more accurately describes spatial temperature changes. Utilizing global data ensures the continuity and consistency of the overall temperature field description, thereby providing a precise reference for cold chain temperature control. By calculating the average temperature gradient difference in different directions (vertical and horizontal), the system can detect areas of uneven temperature distribution. If the detected gradient difference exceeds the preset range, an early warning can be triggered in time, and the abnormality can be corrected by dynamically adjusting the cold chain equipment (such as adjusting the cold air distribution and fan speed), thereby ensuring a uniform temperature in the cargo hold and protecting the quality of temperature-sensitive products such as in vitro diagnostic reagents.

[0049] Specifically, the secondary temperature difference analysis unit is used to obtain the baseline three-dimensional temperature data sequence I1={T1,…,T i ,…,T N The inner wall of the refrigerated truck's cargo compartment, when unloaded, is evenly divided into several cargo space blocks along both the vertical and horizontal directions. The three-dimensional position coordinates (x, y, z) of the center position of each cargo space block are obtained. j ,y j ,z j Based on the baseline three-dimensional temperature data sequence, a continuous temperature field is generated inside the warehouse using a three-dimensional interpolation algorithm to obtain the three-dimensional temperature data of each warehouse space block, forming a first-level three-dimensional temperature data sequence I2={T1,…,T q ,…,T Q}, T q Let q be the three-dimensional temperature data of the q-th warehouse space block, where Q is the number of warehouse space blocks.

[0050] Based on a first-order three-dimensional temperature data sequence I2={T1,…,T q ,…,T Q} Calculate the second temperature gradient difference in the vertical and horizontal directions within the warehouse, respectively. The second temperature gradient difference includes the second longitudinal temperature value and the second transverse temperature value.

[0051] Specifically, for adjacent segments on the same horizontal plane but at different heights, the center coordinates of segments q and q+1 in the z direction are obtained as z0 and z1 respectively.q and z q+1 The corresponding temperature is T q and T q+1 Calculate the second longitudinal temperature value

[0052] The number of vertically divided blocks in the warehouse is obtained and evenly distributed. The second average longitudinal temperature gradient of the upper and lower layers is calculated separately. If the number of vertically divided blocks M in the warehouse is even, then it is calculated according to half the number of blocks. The warehouse is divided into upper and lower levels; if the number of vertically divided blocks in the warehouse is odd, then it is divided into half the number of blocks. The warehouse is divided into two levels, with the middle section in the vertical direction being both the upper and lower levels.

[0053] The second average longitudinal temperature gradient in the upper layer of the warehouse is The temperature was obtained by averaging the second longitudinal temperature value of each segment in the upper region. Let N be the rate of change of the second longitudinal temperature value at segment a in the upper region with height z, which is the local temperature gradient at segment a. 上层 This represents the number of blocks in the upper-level region. This is to sum the local temperature gradients of all the segments in the upper layer.

[0054] The second average longitudinal temperature gradient in the lower layer of the warehouse is The temperature was obtained by averaging the second longitudinal temperature value of each segment in the lower region. Let N be the rate of change of the second longitudinal temperature value at segment b in the lower layer region with height z, which is the local temperature gradient at segment b. 下层 This represents the number of blocks in the lower-level region. This is to sum the local temperature gradients of all the segments in the lower layer.

[0055] By calculating the second average longitudinal temperature gradient of the upper layer of the warehouse And the second average longitudinal temperature gradient of the lower layer of the warehouse is The difference is used to obtain the third temperature gradient difference.

[0056] The secondary temperature difference analysis unit utilizes benchmark three-dimensional temperature data collected by high-precision temperature sensors to construct a continuous temperature field inside the warehouse through three-dimensional interpolation. The warehouse space is uniformly divided into several spatial blocks in both vertical and horizontal directions, and the local temperature gradient of each block is calculated. Through benchmark three-dimensional temperature data and three-dimensional interpolation algorithms, continuous and detailed temperature field data can be generated inside the warehouse. Compared with simply relying on boundary data, this data can more realistically reflect the internal temperature distribution, thus providing an accurate basis for subsequent temperature control.

[0057] By uniformly dividing the warehouse space into upper and lower layers vertically (if the number of blocks is even, each layer contains M / 2 blocks; if it is odd, the middle block is included in both layers), the second average longitudinal temperature gradient of the upper and lower layers is calculated separately. The difference between these two gradients quantifies the uniformity of temperature distribution in the vertical direction of the warehouse. This layered comparison clearly shows whether there are abnormal temperature gradients between the upper and lower layers, helping to promptly identify local hot spots or cold zones.

[0058] The warehouse is divided into front and rear sections horizontally (if the number of sections is even, each section contains R / 2 sections; if it is odd, the middle section is included in both sections). The average temperature gradient of the front and rear sections is calculated separately, and the difference between them is obtained. This can intuitively reflect the balance of temperature distribution in the front and rear sections of the warehouse and identify potential horizontal temperature control problems. Averaging the local temperature gradients of multiple sections within the region can effectively reduce measurement noise and random errors that may occur at a single sampling point, and obtain more stable and reliable temperature gradient data.

[0059] Specifically, for adjacent segments on different horizontal planes on the same vertical plane, the center coordinates of adjacent segments p and p+1 in the x-direction are obtained as x... p and x p+1 The corresponding temperature is T p and T p+1 Second transverse temperature value

[0060] The number of horizontally divided blocks in the warehouse is obtained and evenly distributed. The second average lateral temperature gradient of the upper and lower layers is calculated separately. If the number R of horizontally divided blocks in the warehouse is even, then it is calculated according to half the number of divided blocks. The warehouse is divided into front and rear sections; if the number of horizontally divided sections in the warehouse is odd, then it is divided into half the number of sections. The warehouse is divided into two areas, front and back. The middle section in the horizontal direction is located in both the front and back areas.

[0061] The second average lateral temperature gradient at the front of the warehouse is The second transverse temperature value of each segment in the front region is obtained by averaging, where N represents the rate of change of the second lateral temperature value at segment c in the front region with height z, which is also the local temperature gradient at segment c. 前部 This represents the number of blocks in the front region. To sum the local temperature gradients of all the front segmented blocks.

[0062] The second average lateral temperature gradient at the rear of the warehouse is The second transverse temperature value of each segment in the rear region is obtained by averaging the values. N represents the rate of change of the second lateral temperature value at segment d in the rear region with height z, which is also the local temperature gradient at segment d. 后部 This represents the number of blocks in the rear region. To sum the local temperature gradients of all the rear segmented blocks.

[0063] By calculating the second average temperature lateral gradient at the front of the warehouse And the second average lateral temperature gradient at the rear of the warehouse is The difference is used to obtain the fourth temperature gradient difference.

[0064] Specifically, the regional temperature difference verification unit is used to extract the first temperature gradient difference. Second temperature gradient difference Third temperature gradient difference and the fourth temperature gradient difference To determine whether the temperature is uniform within the warehouse, the following steps are taken:

[0065] when and When the temperature inside the warehouse is uniform, no alarm is needed; otherwise, when the temperature inside the warehouse is uneven, an alarm needs to be triggered. Here, ΔT1 is the preset vertical temperature gradient threshold, and ΔT2 is the preset horizontal temperature gradient threshold.

[0066] Figure 1The real-time temperature overview and thermal distribution display interface provided by this invention marks the current average temperature (representing the average temperature value of all monitoring points in the cargo hold at this time), temperature fluctuation range (referring to the maximum deviation of the highest and lowest temperatures detected at the current moment from the average temperature, reflecting the magnitude of temperature differences between different areas in the cargo hold), and number of abnormal alarms (indicating that the system has identified two temperature anomalies in the most recent detection cycle, which may be local temperature deviations from the set threshold). It also provides a temperature distribution heatmap, presenting the temperature distribution of various locations in the cargo hold in a grid form. The darker the color (red or warm color), the higher the temperature, and the lighter the color (blue or cool color), the lower the temperature, which is convenient for quickly locating hot spots or cold areas. The temperature trend graph displays the changes in average temperature, highest temperature, and lowest temperature over a recent period of time (such as the past 1 hour, 4 hours, etc.) using a line graph, which is used to observe whether the temperature fluctuates within the set range.

[0067] Figure 3 The 24-hour temperature index and trend chart provided by this invention annotates the average temperature over the past 24 hours (calculated by integrating data from all monitoring points over the last 24 hours), maximum temperature fluctuation (the maximum difference between the highest and lowest temperatures over the past 24 hours, reflecting the overall fluctuation range), temperature duration (the length of time a certain temperature range or abnormal state lasts over the past 24 hours), and standard deviation (used to measure the dispersion of temperature data over the past 24 hours; the smaller the value, the more stable the temperature). It also provides a 24-hour temperature change trend chart, with the horizontal axis representing the time scale from 0:00 to 24:00 and the vertical axis representing the temperature value. The line graph displays the average temperature over the past 24 hours or the temperature change at a specified monitoring point, facilitating the viewing of the temperature trend throughout the day and the peak and trough times.

[0068] Figure 4 The temperature range distribution bar chart provided by this invention uses the temperature range on the horizontal axis, dividing the range from -19.0℃ to -17.0℃ into segments of 0.5℃ or 1.0℃, for example: -19.0~-18.5, -18.5~-18.0, -18.0~-17.5, -17.5~-17.0. The vertical axis represents the duration, indicating the number of hours spent within the corresponding temperature range. Each bar represents the cumulative duration within that temperature range; the taller the bar, the longer the duration at that temperature. This chart allows for a quick understanding of the main temperature range distribution of refrigerated trucks throughout the day, and helps determine whether the truck is consistently operating within the target temperature range.

[0069] Figure 5The temperature fluctuation scatter plot provided by this invention uses time as the horizontal axis, for example, from 0:00 to 24:00 of a given day, or a span of multiple days; the plot is marked "2024-01-18 14:00:00"; the vertical axis shows the deviation of temperature fluctuation at each time point relative to a certain reference temperature (such as average temperature or set temperature). Each point represents the temperature deviation measured at that time point, such as "0.3" indicating that the temperature is 0.3℃ higher than the reference temperature at this moment; if the point is in the negative value area, it indicates that the temperature is lower than the reference value. By observing the distribution of the scatter plots, the temperature deviation from the reference value over one or more days can be observed. If most points are concentrated within ±0.5℃, it indicates that the temperature is relatively stable; if multiple points with large deviations appear, it is necessary to be alert to possible temperature control abnormalities.

[0070] The first temperature gradient difference, calculated using global temperature data obtained from an infrared thermal imaging camera, and the second temperature gradient difference, obtained by interpolating reference three-dimensional temperature data generated by a high-precision temperature sensor, are two sets of data reflecting the global and local temperature distributions, respectively. By comparing the differences between these two sets of data (i.e., the vertical difference...) And horizontal This system can effectively identify temperature anomalies caused by sensor deviation, environmental interference, or measurement errors, ensuring the accuracy and reliability of temperature monitoring. By calculating the average temperature gradient of the area in both the vertical and horizontal directions and obtaining the difference between the two sets of gradients, it can intuitively reflect whether the temperature distribution in the warehouse is balanced. When the difference between the two sets of gradients is lower than the preset thresholds (ΔT1 and ΔT2), it indicates that the temperature distribution in each area is basically balanced; otherwise, it is determined that there is an anomaly, thus providing a basis for subsequent temperature control adjustments.

[0071] Example 2

[0072] A reference temperature of T was measured at a location inside the refrigerated truck's cargo compartment using a high-precision temperature sensor. i =6.5℃, the global temperature data corresponding to this location obtained by the infrared thermal imaging camera is T q (x i ,y i ,z i = 8.0℃, the reference calibration unit calculates the first abnormal temperature fluctuation value ΔT according to the formula. i =|T i -T q (x i ,y i ,z iIf the temperature is 1.5℃, and the preset temperature fluctuation threshold is 1.0℃, then since 1.5℃ > 1.0℃, the temperature fluctuation at this location is determined to be outside the normal range. In this case, the reference calibration unit will determine that the reference three-dimensional temperature data at this location is abnormal and needs adjustment. A data correction algorithm (such as interpolation correction or weighted average correction) can then be activated to adjust the data at this location to better reflect the actual global temperature field, ensuring a balanced temperature distribution throughout the warehouse.

[0073] Example 3

[0074] When the refrigerated truck is unloaded, data is collected through a multi-source temperature monitoring module, yielding the following information:

[0075] The system obtains the global temperature field within the warehouse by installing two infrared thermal imaging cameras diagonally on the top of the warehouse. Using this global data, the primary temperature difference analysis unit calculates the first longitudinal temperature gradient difference and the first lateral temperature gradient difference based on depth information. Dividing the warehouse into upper and lower layers at the midpoint of its height, the average longitudinal gradients of the upper and lower layers are calculated to be 0.8℃ / m and 0.0℃ / m respectively (or the lower layer gradient is a relatively lower value), thus yielding the first temperature gradient difference. The warehouse was divided into front and rear sections along a horizontal centerline. Calculations showed that the average lateral gradients of the front and rear sections were 1.2℃ / m and 0.0℃ / m, respectively, yielding the second temperature gradient difference.

[0076] A high-precision temperature sensor obtains the temperature of each segment by uniformly dividing the cargo hold's inner wall into several blocks (a reference three-dimensional temperature data sequence). Then, a secondary temperature difference analysis unit uses a three-dimensional interpolation algorithm to generate a continuous temperature field within the cargo hold from this discrete data, dividing the internal space into several spatial blocks both vertically and horizontally. Calculations yield the third and fourth temperature gradient differences: after vertical stratification, the average local longitudinal temperature gradient in the upper region is 0.9℃ / m, while in the lower region it is 0.0℃ / m, thus obtaining the third temperature gradient difference.

[0077] The regional temperature difference verification unit will extract these four gradient differences respectively and calculate the differences between them: vertical difference Horizontal differences The preset vertical temperature gradient threshold ΔT1 = 0.2℃ / m, and the preset horizontal temperature gradient threshold ΔT2 = 0.3℃ / m.

[0078] According to the judgment rules: if the difference in both the vertical and horizontal directions is below the corresponding threshold, the temperature in the cargo hold area is considered to be uniform, and no alarm needs to be triggered; if the difference in either direction exceeds the threshold, the area temperature is considered to be uneven, and an alarm should be triggered. Therefore, based on the vertical direction of 0.1℃ / m < 0.2℃ / m and the horizontal direction of 0.2℃ / m < 0.3℃ / m, the system determines that the temperature in the cargo hold area is uniform and no alarm is triggered.

[0079] This invention acquires global temperature data and baseline three-dimensional temperature data within the warehouse, calculates a first abnormal temperature fluctuation value, automatically identifies abnormal deviations between local and global temperature data, ensures the consistency of temperature data, quantitatively calculates temperature gradients from both global and local perspectives, can capture the temperature distribution characteristics within the warehouse in detail, compares the temperature gradient differences calculated from different data sources, and sets reasonable thresholds to achieve automatic early warning, thereby ensuring a stable temperature control environment and protecting the quality of in vitro diagnostic reagents.

[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0081] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents, comprising a multi-source temperature monitoring module, a reference calibration unit, a primary temperature difference analysis unit, a secondary temperature difference analysis unit, and a regional temperature difference verification unit; characterized in that, The multi-source temperature monitoring module is used to acquire global temperature data and baseline three-dimensional temperature data within the warehouse; the primary temperature difference analysis unit is based on the global temperature data T q (x i ,y i ,z i Obtain the first temperature gradient difference in the vertical direction within the warehouse. and the second temperature gradient difference in the horizontal direction The secondary temperature difference analysis unit is used to obtain the baseline three-dimensional temperature data sequence I1 of the warehouse. It divides the inner wall of the refrigerated truck warehouse (when unloaded) into several warehouse space blocks along both the vertical and horizontal directions, and obtains the three-dimensional position coordinates (x, y, z) of the center position of each warehouse space block. j ,y j ,z j Based on the baseline three-dimensional temperature data sequence, a continuous temperature field is generated inside the warehouse using a three-dimensional interpolation algorithm to obtain the three-dimensional temperature data of each warehouse space block, forming a first-level three-dimensional temperature data sequence I2; based on the first-level three-dimensional temperature data sequence I2, the third temperature gradient difference in the vertical direction within the warehouse is obtained through analysis. and the fourth temperature gradient difference in the horizontal direction The regional temperature difference verification unit is used to extract the first temperature gradient difference, the second temperature gradient difference, the third temperature gradient difference, and the fourth temperature gradient difference to determine whether the temperature in the warehouse area is uniform.

2. The dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents according to claim 1, characterized in that, The multi-source temperature monitoring module includes two infrared thermal imaging cameras and a high-precision temperature sensor. The infrared thermal imaging cameras are installed diagonally on the top surface of the refrigerated truck's cargo compartment. The high-precision temperature sensor acquires the cargo compartment surface when it is empty, divides the opposite cargo compartment surface into several equal and uniform segments, and installs a high-precision temperature sensor in each segment to acquire the reference three-dimensional temperature data sequence I1={T1,…,T i ,…,T N }, where T i Let N be the i-th baseline 3D temperature data, and N be the total number of baseline 3D temperature data; global temperature data T inside the warehouse is acquired based on an infrared thermal imaging camera. q (x i ,y i ,z i ); Based on high-precision temperature sensors, benchmark three-dimensional temperature data inside the warehouse is obtained.

3. The dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents according to claim 1, characterized in that, The reference calibration unit is used to calibrate based on global temperature data T q (x i ,y i ,z i The first abnormal temperature fluctuation value ΔT at the same location within the warehouse was calculated using the baseline three-dimensional temperature data sequence I1. i =|T i -T q (x i ,y i ,z i The first abnormal temperature fluctuation value is compared with the preset abnormal temperature fluctuation threshold. If the first abnormal temperature fluctuation value is greater than or equal to the preset abnormal temperature fluctuation threshold, the reference three-dimensional temperature data at that location needs to be adjusted. If the first abnormal temperature fluctuation value is less than the preset abnormal temperature fluctuation threshold, the baseline three-dimensional temperature data at that location does not need to be adjusted.

4. The dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents according to claim 1, characterized in that, The primary temperature difference analysis unit is based on global temperature data T. q (x i ,y i ,z i The temperature gradient differences in the vertical and horizontal directions within the warehouse are obtained separately. The global temperature data is then combined with the depth information within the warehouse to obtain the first vertical temperature value. and the first transverse temperature value Obtain the midpoint of the warehouse height, divide the warehouse into upper and lower layers based on the midpoint, and calculate the first average temperature gradient difference value of the upper layer area. and the first average temperature gradient difference value of the lower area of ​​the warehouse. The difference is the first temperature gradient difference. Obtain the midpoint of the warehouse length, divide the warehouse into front and rear sections based on this midpoint, and calculate the difference between the first average temperature gradient difference in the front section and the first average temperature gradient difference in the rear section. This yields the second temperature gradient difference.

5. The dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents according to claim 1, characterized in that, The secondary temperature difference analysis unit is based on the primary three-dimensional temperature data sequence I2 = {T1,…,T q ,…,T Q Calculate the difference in the second temperature gradient in the vertical and horizontal directions within the warehouse, T. q Here is the three-dimensional temperature data for the q-th warehouse space block, where Q is the number of warehouse space blocks; The second temperature gradient difference includes the second longitudinal temperature value. and the second transverse temperature value 6. The dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents according to claim 5, characterized in that, The secondary temperature difference analysis unit obtains the number of vertically divided blocks in the warehouse and divides them evenly, calculating the second average longitudinal temperature gradient of the upper and lower layers respectively; the second average longitudinal temperature gradient of the upper layer of the warehouse is... The second average longitudinal temperature gradient of the lower layer of the warehouse is obtained by averaging the second longitudinal temperature values ​​of each segment in the upper region; The temperature was obtained by averaging the second longitudinal temperature value of each segment in the lower region. The third temperature gradient difference is obtained by calculating the difference between the second average temperature longitudinal gradient of the upper layer of the warehouse and the second average temperature longitudinal gradient of the lower layer of the warehouse.

7. The dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents according to claim 5, characterized in that, The secondary temperature difference analysis unit obtains the center coordinates (x, x) of adjacent blocks p and p+1 in the x-direction for adjacent blocks p and p+1 on the same vertical plane but different horizontal planes. p and x p+1 The corresponding temperature is T p and T p+1 Calculate the second transverse temperature value The number of horizontally divided blocks in the warehouse is obtained and evenly distributed. The second average lateral temperature gradient of the upper and lower regions is calculated respectively. The second average lateral temperature gradient at the front of the warehouse is The second average lateral temperature gradient at the rear of the warehouse is obtained by averaging the second lateral temperature values ​​of each segment in the front region; The second transverse temperature value of each segment in the rear region is obtained by averaging. The fourth temperature gradient difference is obtained by calculating the difference between the second average temperature lateral gradient at the front of the warehouse and the second average temperature lateral gradient at the rear of the warehouse.

8. The dynamic monitoring and control system for cold chain transportation of in vitro diagnostic reagents according to claim 1, characterized in that, The regional temperature difference verification unit is used to extract the first temperature gradient difference. Second temperature gradient difference Third temperature gradient difference and the fourth temperature gradient difference To determine whether the temperature is uniform within the warehouse, specifically: when and At that time, the temperature inside the warehouse was uniform, and there was no need to trigger an alarm. Otherwise, if the temperature inside the warehouse is uneven, an alarm will be triggered; where ΔT1 is the preset vertical temperature gradient threshold and ΔT2 is the preset horizontal temperature gradient threshold.

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