Cold-chain logistics temperature real-time control system and method

Abstract

The invention discloses a cold-chain logistics temperature real-time control system and method, and relates to the technical field of cold-chain logistics management.The cold-chain logistics temperature real-time control system comprises a data acquisition module, and the data acquisition module comprises an acquisition unit and a monitoring unit; the acquisition unit is used for acquiring parameter information in a cold-chain logistics process, and the monitoring unit is used for monitoring the parameter information acquired by the acquisition unit and preprocessing the parameter information to remove abnormal parameter information; the analysis control module comprises a control unit, and the control unit is used for adjusting control parameters of the cold-chain logistics equipment; according to the invention, effective afterward tracking and analysis can be carried out at the first time when a fault occurs, the safety and effectiveness of drugs are ensured, subsequent reduction of afterward tracking and analysis problems caused by monitoring equipment fault or data recording interruption is facilitated, the transparency and tracking capability of a supply chain are improved, and the understanding and management of each link of a service are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of cold chain logistics management, and more specifically, to a real-time temperature control system and method for cold chain logistics. Background Art

[0002] The real-time temperature control system for cold chain logistics is widely used in the storage and transportation of products that require temperature and humidity sensitivity, such as fresh food and pharmaceutical cold chain. Its main functions include automated data collection, configurable monitoring indicators, automated early warning notifications, and visualized statistical analysis, thereby improving the safety control level of fresh food and pharmaceutical storage and transportation.

[0003] However, in actual use, pharmaceutical cold chain logistics requires real-time monitoring of the temperature of drugs during the entire storage and transportation process to ensure the quality and safety of drugs. However, the existing control system may be insufficient in terms of data continuity and integrity, especially in the monitoring of loading and unloading links, which may not be comprehensive enough, resulting in the inability to achieve seamless monitoring and traceability throughout the process. In addition, once the monitoring equipment fails or the data record is interrupted, it will be difficult to conduct effective post-event tracking and analysis;

[0004] Moreover, the pharmaceutical cold chain logistics industry still has problems such as inconsistent information standards and lack of a unified full-process traceability system, which makes it difficult to connect information systems between different companies and leads to serious information island phenomena. In addition, the lack of product stability verification standards after a short "chain break" increases the difficulty of coordination between upstream and downstream companies in the supply chain. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a real-time temperature control system and method for cold chain logistics.

[0006] On the one hand, a cold chain logistics temperature real-time control system is provided, comprising a data acquisition module, wherein the data acquisition module comprises a collection unit and a monitoring unit;

[0007] The acquisition unit is used to acquire parameter information in the cold chain logistics process, and the monitoring unit is used to monitor the acquisition unit to acquire parameter information for preprocessing and remove abnormal parameter information;

[0008] An analysis and control module, the analysis and control module comprising a control unit, the control unit being used to adjust control parameters of the cold chain logistics equipment;

[0009] The analysis and control module further includes a judgment unit, which is used to judge whether there is an abnormality in the cold chain logistics according to the preprocessed parameter information of the data acquisition module;

[0010] The analysis and control module further comprises a response unit, which is used to respond according to the judgment result of the judgment unit and adjust the control parameters of the control unit;

[0011] The analysis and control module further includes a positioning unit, which is used to quickly locate abnormal objects and isolate them when the judgment unit judges that an abnormality occurs.

[0012] Preferably, the specific working mode of the monitoring unit is as follows:

[0013] Data parameters are obtained by collecting data at a fixed frequency;

[0014] Obtain the integrity value Q of each data parameter;

[0015] Obtain the continuity value W of each data parameter;

[0016] According to the formula , calculate and obtain the evaluation value E of each data parameter, where and is the preset weight coefficient;

[0017] A threshold value of the parameter evaluation value is set in advance. If the evaluation value E of the data parameter is less than the threshold value of the parameter evaluation value, the data parameter is marked as abnormal and removed.

[0018] Preferably, the integrity value Q and the continuity value W are obtained as follows:

[0019] According to the formula 00, obtain the integrity value of the data parameter , where V is the actual value of the data parameter, is the expected value of the data parameter, is the half width of the expected range;

[0020] According to the formula 00, calculate and obtain the continuity value W of the data parameter, is the actual time interval between two consecutive data parameter collections, and T is the maximum time interval allowed by the system.

[0021] Preferably, the specific working mode of the judgment unit is as follows:

[0022] Get the data set consisting of the data parameters collected from the cold chain logistics temperature within one minute before the current time period ;

[0023] Get the upper threshold of the cold chain logistics temperature and lower threshold ;

[0024] The dataset The data parameters and upper threshold in and lower threshold For comparison, record the data set The data parameter in is greater than the upper threshold Or less than the lower threshold The number of times is marked as the warning number B;

[0025] The threshold of the number of warnings is obtained in advance. If the number of warnings B is greater than the threshold of the number of warnings, an abnormal alarm is generated and transmitted to the response unit and the tracing unit. If not, the cold logistics is marked as working normally.

[0026] Preferably, the upper threshold of the cold chain logistics temperature is and lower threshold The specific way to obtain is as follows:

[0027] According to the formula , get the upper temperature threshold ;

[0028] According to the formula , get the lower limit threshold of the temperature ;

[0029] in is the historical average temperature, is the historical standard deviation of temperature, and k is the preset proportional coefficient.

[0030] Preferably, the specific working method of the judgment unit also includes:

[0031] According to the formula , and obtain the environmental impact factors ,in is the benchmark impact factor, is the adjustment factor;

[0032] According to the formula , calculate and obtain the adjusted proportional coefficient , the adjusted proportional coefficient Substitute into the calculation to get the new upper temperature threshold and lower threshold .

[0033] Preferably, the specific working mode of the response unit is as follows:

[0034] Obtain the difference H between the number of warnings B and the threshold of the number of warnings, divide the difference H by the threshold of the number of warnings, and then multiply by 100% to obtain the excess rate ;

[0035] According to the formula , obtain the temperature adjustment amplitude A, and transmit the adjustment amplitude A to the control unit for adjustment, where L is a preset proportional coefficient.

[0036] Preferably, the specific working mode of the positioning unit is as follows:

[0037] First, build the blockchain network: Then upload the data to the chain:

[0038] Each product unit is then assigned a unique identifier;

[0039] The temperature anomaly is associated with a specific geographic location, and when the early warning module detects an anomaly, the affected product is isolated and its flow path in the supply chain is tracked.

[0040] Preferably, the specific working method of the positioning unit also includes:

[0041] The cold chain logistics route is divided into different areas with an area of ​​ten square kilometers;

[0042] The temperature anomaly frequency and temperature anomaly duration in each area are obtained, thresholds of the temperature anomaly frequency and temperature anomaly duration are set in advance, areas where the temperature anomaly frequency and temperature anomaly duration are greater than the corresponding thresholds are marked as abnormal areas, and information of the abnormal areas is transmitted to the control unit for optimization.

[0043] On the other hand, the present invention also proposes a power load control method based on big data, comprising the following steps:

[0044] Step 1: Acquire parameter information in the cold chain logistics process, the monitoring unit is used to monitor the acquisition unit to acquire parameter information for preprocessing, and remove abnormal parameter information;

[0045] Step 2: judging whether there is any abnormality in the cold chain logistics according to the preprocessed parameter information of the data acquisition module, and responding according to the judgment result of the judgment unit to adjust the control parameters of the control unit;

[0046] Step 3: When the judgment unit judges that an abnormality occurs, the abnormal object is quickly located and isolated.

[0047] Beneficial effects: When a failure occurs, effective post-facto tracking and analysis can be carried out immediately to ensure the safety and effectiveness of the drugs, which is conducive to reducing the subsequent post-facto tracking and analysis problems caused by monitoring equipment failure or data record interruption, improving the transparency and tracking capabilities of the supply chain, and enhancing the understanding and management of all aspects of the business. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a control system flow chart of the present invention;

[0049] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0050] like Figure 1 As shown, a cold chain logistics temperature real-time control system includes a data acquisition module, and the data acquisition module includes a collection unit and a monitoring unit;

[0051] The acquisition unit is used to acquire parameter information in the cold chain logistics process, and the monitoring unit is used to monitor the acquisition unit to acquire parameter information for preprocessing and remove abnormal parameter information; it should be noted that in this embodiment, the key parameter may be temperature, and it should be noted that it has an automated data acquisition function to reduce manual intervention and improve the accuracy and real-time nature of the data; it should also be noted that the preprocessing method is to acquire the continuity and integrity of the information, and then make a judgment and remove abnormal parameters, which can evaluate whether the parameter information is accurate and complete, which is conducive to screening out accurate information and facilitating the accuracy of subsequent early warning analysis;

[0052] The real-time temperature control system for cold chain logistics is widely used in the storage and transportation of products that require temperature and humidity sensitivity, such as fresh food and pharmaceutical cold chain. Its main functions include automated data collection, configurable monitoring indicators, automated early warning notifications, and visualized statistical analysis, thereby improving the safety control level of fresh food and pharmaceutical storage and transportation.

[0053] However, in actual use, pharmaceutical cold chain logistics requires real-time monitoring of the temperature of drugs during the entire storage and transportation process to ensure the quality and safety of drugs. However, the existing control system may be insufficient in terms of data continuity and integrity, especially in the monitoring of loading and unloading links, which may not be comprehensive enough, resulting in the inability to achieve seamless monitoring and traceability throughout the process. In addition, once the monitoring equipment fails or the data record is interrupted, it will be difficult to conduct effective post-event tracking and analysis;

[0054] Moreover, the pharmaceutical cold chain logistics industry still has problems such as inconsistent information standards and lack of a unified full-process traceability system, which makes it difficult to connect information systems between different companies and leads to serious information island phenomena. In addition, the lack of product stability verification standards after a short "chain break" increases the difficulty of coordination between upstream and downstream companies in the supply chain.

[0055] An analysis control module, wherein the analysis control module includes a control unit, and the control unit is used to adjust control parameters of cold chain logistics equipment; it should be noted that cold chain logistics equipment includes refrigerated trucks, cold storages, refrigerated boxes, refrigeration equipment, heating equipment, etc.;

[0056] The analysis and control module further includes a judgment unit, which is used to judge whether there is an abnormality in the cold chain logistics according to the preprocessed parameter information of the data acquisition module;

[0057] The analysis and control module further includes a response unit, which is used to respond according to the judgment result of the judgment unit and adjust the control parameters of the control unit; it should be noted that when a fault occurs, the temperature in the cold chain logistics can be adjusted by the control unit according to the actual situation of the fault to reduce subsequent losses and impacts;

[0058] The analysis and control module also includes a positioning unit, which is used to quickly locate and isolate abnormal items when the judgment unit determines that an abnormality occurs. It should be noted that when a failure occurs, effective post-facto tracking and analysis can be carried out in the first place to ensure the safety and effectiveness of the drugs, which is conducive to reducing the subsequent post-facto tracking and analysis problems caused by monitoring equipment failure or data record interruption, improving the transparency and tracking capabilities of the supply chain, and enhancing the understanding and management of all aspects of the business.

[0059] As an optional embodiment: the specific working mode of the monitoring unit is as follows:

[0060] The data parameters are obtained by collecting data at a fixed frequency; it should be noted that in this embodiment, the collection frequency may be 1 second, and it should also be noted that the data parameter may be the temperature in the logistics equipment, and the temperature data collected every second is marked as the data parameter;

[0061] The integrity value Q of each data parameter is obtained; it should be noted that integrity is used to evaluate data parameters and can quantify the integrity of data parameters, which is conducive to more accurate and objective subsequent evaluation;

[0062] The continuity value W of each data parameter is obtained; it should be noted that the continuity of the data parameter can be quantified, which is conducive to the subsequent evaluation to be more accurate and objective;

[0063] According to the formula , calculate and obtain the evaluation value E of each data parameter, where and is a preset weight coefficient; it should be noted that, in this embodiment, and It is used to measure the importance of the data parameter integrity value Q and the continuity value W to the evaluation value E. In this embodiment, and The values ​​of can be 0.893 and 0.107;

[0064] A threshold of parameter evaluation value is set in advance. If the evaluation value E of the data parameter is less than the threshold of the parameter evaluation value, the data parameter is marked as abnormal and removed. It should be noted that the removed parameter can use the historical average value to fill the data parameter at this time point; it should also be noted that the data parameters can be monitored in real time, deviations and interruptions can be discovered in time, so as to respond quickly and take measures; clear numerical indicators are provided to facilitate relevant personnel to make decisions based on these indicators, which helps to unify the evaluation standards and improve the consistency of the evaluation.

[0065] As an optional embodiment: the integrity value Q and the continuity value W are obtained as follows:

[0066] According to the formula 00, obtain the integrity value of the data parameter , where V is the actual value of the data parameter, is the expected value of the data parameter, is the half width of the expected range; it should be noted that the half width of the expected range is the expected maximum value minus the expected minimum value divided by 2. This calculation can reflect the closeness of the data point to the expected value, and the integrity value The higher the value, the better the data integrity. It should also be noted that a relative deviation value is calculated by dividing the deviation by the half-width of the expected range, which makes the score comparable between different data sets or parameters of different dimensions. Converting the relative deviation to a percentage system provides an intuitive scoring system, where 100 points represent a perfect match with the expected value and 0 points represent that the deviation reaches the limit of the expected range. Using the half-width of the expected range as the divisor of the deviation ensures that the scoring system can perceive the fluctuation range of the data. For data sets with large fluctuations, the scoring system will be more relaxed.

[0067] According to the formula 00, calculate and obtain the continuity value W of the data parameter, is the actual time interval between two consecutive data parameter collections, and T is the maximum time interval allowed by the system. It should be noted that in this embodiment, the maximum time interval is 1 second. This calculation can reflect the comparison between the data collection interval and the maximum allowed interval. The higher the value of the continuity value W, the better the continuity of data collection. It should also be noted that the calculation of the time interval metric is based on the time interval between consecutive data points, which reflects the frequency and regularity of data collection. By identifying the maximum time interval, potential interruptions or delays in data collection can be found. The maximum interval is compared with the maximum time interval allowed by the system to evaluate the continuity of data collection. The percentage system is used to make the continuity score intuitive and easy to understand. 100 points means that the data collection is completely continuous and does not exceed the maximum allowed interval. By comparing the actual interval with the allowed interval, the continuity of data collection can be dynamically evaluated to adapt to different collection frequencies and system requirements.

[0068] As an optional embodiment: the specific working mode of the judgment unit is as follows:

[0069] Get the data set consisting of the data parameters collected from the cold chain logistics temperature within one minute before the current time period It should be noted that, in this embodiment, the interval for data parameter collection is 1 second, and the data set The number of data in the dataset is 60. It should also be noted that the dataset The data parameters in the data set also need to be standardized to reduce subsequent deviations. The specific standardized calculation method can be ,in is the historical average temperature, is the historical standard deviation of temperature, is the cold chain logistics temperature collected at time point i, where i is an integer from 1 to 60;

[0070] Get the upper threshold of the cold chain logistics temperature and lower threshold ;

[0071] The dataset The data parameters and upper threshold in and lower threshold For comparison, record the data set The data parameter in is greater than the upper threshold Or less than the lower threshold The number of times is marked as the warning number B;

[0072] The threshold of the number of warnings is obtained in advance. If the number of warnings B is greater than the threshold of the number of warnings, an abnormal alarm is generated and transmitted to the response unit and the traceability unit. If not, the cold logistics is marked as working normally. It should be noted that the combination of real-time standardization of data, abnormality detection, warning triggering and warning level evaluation can realize real-time monitoring and warning of environmental parameters in the cold chain logistics process.

[0073] As an optional embodiment: the upper threshold of the cold chain logistics temperature and lower threshold The specific way to obtain is as follows:

[0074] According to the formula , get the upper temperature threshold ;

[0075] According to the formula , get the lower limit threshold of the temperature ;

[0076] in is the historical average temperature, is the historical standard deviation of temperature, and k is a preset proportionality coefficient. It should be noted that the value of k depends on the required confidence level, and the value of k is between 2 and 3. In this embodiment, k can be 2.485;

[0077] It should also be noted that the historical mean and standard deviation can be regularly updated based on the latest data to maintain the accuracy of the threshold.

[0078] As an optional embodiment: the specific working mode of the judgment unit also includes: It should be noted that the threshold is based on a fixed proportional coefficient k, which may not adapt to the dynamic characteristics of environmental changes in the cold chain logistics process;

[0079] According to the formula , and obtain the environmental impact factors ,in is the benchmark impact factor, is the adjustment coefficient; it should be noted that It reflects the relative degree of current environmental change and baseline environmental change. It can be set based on historical data or industry standards to reflect the adaptability of cold chain logistics under normal environmental changes. The way to obtain it can be , where V is the benchmark coefficient of variation set according to industry standards or historical data under similar conditions, reflecting the adaptability of the cold chain logistics system under normal environmental changes. In this embodiment, The value of is 1.023, and the value of V is 0.947;

[0080] According to the formula , calculate and obtain the adjusted proportional coefficient , the adjusted proportional coefficient Substitute into the calculation to get the new upper temperature threshold and lower threshold It should be noted that in this way, the threshold is no longer fixed, but is dynamically adjusted according to the characteristics of the real-time data stream to better adapt to environmental changes. In this embodiment, the real-time data stream may be ambient temperature and device parameters, etc. Adjustment can make the threshold setting more in line with the changes caused by actual use.

[0081] As an optional embodiment: the specific working mode of the response unit is as follows:

[0082] Obtain the difference H between the number of warnings B and the threshold of the number of warnings, divide the difference H by the threshold of the number of warnings, and then multiply by 100% to obtain the excess rate ;

[0083] According to the formula , obtain the temperature adjustment amplitude A, and transmit the adjustment amplitude A to the control unit for adjustment, where L is a preset proportional coefficient. It should be noted that if the current temperature is too high, the temperature is lowered by the control unit, otherwise, the temperature is raised, and the lowered and raised values ​​are both A. In this embodiment, L is an adjustment coefficient set based on historical data and industry standards. The specific value can be obtained by subtracting the lowest safe temperature threshold for drug storage from the highest safe temperature threshold and then dividing it by the number of response level divisions. In this embodiment, there can be three response levels, specifically 10%, 50% and 100%, so the number of response level divisions is 3;

[0084] It is also important to note that adjusting the temperature based on the degree to which the threshold is actually exceeded provides an adaptive response that ensures that the magnitude of the adjustment matches the severity of the problem;

[0085] Allows different response levels to be set according to different excess ratios (such as 10%, 50%, 100%), increasing the flexibility of the system;

[0086] By adjusting the temperature setting in time, potential risks can be prevented and medicines can be prevented from deteriorating or being damaged;

[0087] Appropriate adjustment range can reduce energy waste caused by over-adjustment while ensuring drug safety;

[0088] Setting the adjustment factor L based on historical data and industry standards makes the adjustment process more scientific and data-driven;

[0089] It should also be noted that it can ensure that the temperature of cold chain logistics can be adjusted in time when logistics are abnormal, reducing the drug safety risks caused by abnormal temperature. These measures help to improve the stability and reliability of cold chain logistics and ensure the quality and safety of drugs throughout the entire supply chain process.

[0090] As an optional embodiment: the specific working mode of the positioning unit is as follows:

[0091] First, the blockchain network is built: It should be noted that a consortium chain is established to invite companies in various links of the supply chain, such as drug production, circulation, and sales, to join and jointly maintain the blockchain network for drug traceability;

[0092] Then, the data is uploaded to the chain: It should be noted that the key data of drug production, storage, transportation, sales and other links are stored on the chain, and the authenticity and integrity of the data are ensured by using the tamper-proof characteristics of the blockchain;

[0093] Each product unit is then assigned a unique identifier; it should be noted that (such as an RFID tag) and the affected product is quickly identified when an anomaly occurs;

[0094] The temperature anomaly is associated with a specific geographic location. When the early warning module detects an anomaly, the affected product is isolated and its flow path in the supply chain is tracked. It should be noted that through blockchain technology, all participants can access shared, tamper-proof drug circulation records, which enhances the transparency and trust of the supply chain. The automatic execution mechanism of smart contracts improves the response speed to emergencies, helps to quickly take effective measures and reduce drug safety risks.

[0095] As an optional embodiment: the specific working mode of the positioning unit also includes:

[0096] The cold chain logistics route is divided into different areas with an area of ​​ten square kilometers;

[0097] The frequency and duration of temperature anomalies in each area are obtained, and thresholds of temperature anomaly frequency and temperature anomaly duration are set in advance. Areas where the temperature anomaly frequency and temperature anomaly duration are greater than the corresponding thresholds are marked as abnormal areas, and the information of the abnormal areas is transmitted to the control unit for optimization. It should be noted that by analyzing historical data, common patterns and risk areas of temperature anomalies are determined, and then the transportation routes and storage conditions are optimized to reduce the probability of abnormalities. When the control unit formulates the route, the abnormal areas are avoided.

[0098] On the other hand, the present invention also proposes a power load control method based on big data, comprising the following steps:

[0099] Step 1: Acquire parameter information in the cold chain logistics process, the monitoring unit is used to monitor the acquisition unit to acquire parameter information for preprocessing, and remove abnormal parameter information;

[0100] Step 2: judging whether there is any abnormality in the cold chain logistics according to the preprocessed parameter information of the data acquisition module, and responding according to the judgment result of the judgment unit to adjust the control parameters of the control unit;

[0101] Step 3: When the judgment unit judges that an abnormality occurs, the abnormal object is quickly located and isolated.

[0102] How it works

[0103] When a failure occurs, effective post-facto tracking and analysis can be carried out immediately to ensure the safety and effectiveness of the drugs, which is conducive to reducing the subsequent post-facto tracking and analysis problems caused by monitoring equipment failure or data record interruption, improving the transparency and tracking capabilities of the supply chain, and enhancing the understanding and management of all aspects of the business.

[0104] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. A real-time temperature control system for cold chain logistics, characterized in that: It includes a data acquisition module, which includes a collection unit and a monitoring unit; The acquisition unit is used to acquire parameter information in the cold chain logistics process, and the monitoring unit is used to monitor the acquisition unit to acquire parameter information for preprocessing and remove abnormal parameter information; An analysis and control module, the analysis and control module comprising a control unit, the control unit being used to adjust control parameters of the cold chain logistics equipment; The analysis and control module further includes a judgment unit, which is used to judge whether there is an abnormality in the cold chain logistics according to the preprocessed parameter information of the data acquisition module; The analysis and control module further comprises a response unit, which is used to respond according to the judgment result of the judgment unit and adjust the control parameters of the control unit; The analysis and control module further includes a positioning unit, which is used to quickly locate abnormal objects and isolate them when the judgment unit judges that an abnormality occurs.

2. A cold chain logistics temperature real-time control system according to claim 1, characterized in that: The specific working mode of the monitoring unit is as follows: Data parameters are obtained by collecting data at a fixed frequency; Obtain the integrity value Q of each data parameter; Obtain the continuity value W of each data parameter; According to the formula , calculate and obtain the evaluation value E of each data parameter, where and is the preset weight coefficient; A threshold value of the parameter evaluation value is set in advance. If the evaluation value E of the data parameter is less than the threshold value of the parameter evaluation value, the data parameter is marked as abnormal and removed.

3. A cold chain logistics temperature real-time control system according to claim 2, characterized in that: The integrity value Q and the continuity value W are obtained as follows: According to the formula 00, obtain the integrity value of the data parameter , where V is the actual value of the data parameter, is the expected value of the data parameter, is the half width of the expected range; According to the formula 00, calculate and obtain the continuity value W of the data parameter, is the actual time interval between two consecutive data parameter collections, and T is the maximum time interval allowed by the system.

4. A cold chain logistics temperature real-time control system according to claim 3, characterized in that: The specific working mode of the judgment unit is as follows: Get the data set consisting of the data parameters collected from the cold chain logistics temperature within one minute before the current time period ; Get the upper threshold of the cold chain logistics temperature and lower threshold ; The dataset The data parameters and upper threshold in and lower threshold For comparison, record the data set The data parameter in is greater than the upper threshold Or less than the lower threshold The number of times is marked as the warning number B; The threshold of the number of warnings is obtained in advance. If the number of warnings B is greater than the threshold of the number of warnings, an abnormal alarm is generated and transmitted to the response unit and the tracing unit. If not, the cold logistics is marked as working normally.

5. A cold chain logistics temperature real-time control system according to claim 4, characterized in that: The upper limit threshold of the cold chain logistics temperature and lower threshold The specific way to obtain is as follows: According to the formula , get the upper temperature threshold ; According to the formula , get the lower limit threshold of the temperature ; in is the historical average temperature, is the historical standard deviation of temperature, and k is the preset proportional coefficient.

6. A cold chain logistics temperature real-time control system according to claim 5, characterized in that: The specific working method of the judgment unit also includes: According to the formula , and obtain the environmental impact factors ,in is the benchmark impact factor, is the adjustment factor; According to the formula , calculate and obtain the adjusted proportional coefficient , the adjusted proportional coefficient Substitute into the calculation to get the new upper temperature threshold and lower threshold .

7. A cold chain logistics temperature real-time control system according to claim 4, characterized in that: The specific working mode of the response unit is as follows: Obtain the difference H between the number of warnings B and the threshold of the number of warnings, divide the difference H by the threshold of the number of warnings, and then multiply by 100% to obtain the excess rate ; According to the formula , obtain the temperature adjustment amplitude A, and transmit the adjustment amplitude A to the control unit for adjustment, where L is a preset proportional coefficient.

8. A cold chain logistics temperature real-time control system according to claim 4, characterized in that: The specific working mode of the positioning unit is as follows: First, build the blockchain network: Then upload the data to the chain: Each product unit is then assigned a unique identifier; The temperature anomaly is associated with a specific geographic location, and when the early warning module detects an anomaly, the affected product is isolated and its flow path in the supply chain is tracked.

9. A cold chain logistics temperature real-time control system according to claim 8, characterized in that: The specific working method of the positioning unit also includes: The cold chain logistics route is divided into different areas with an area of ​​ten square kilometers; The temperature anomaly frequency and temperature anomaly duration in each area are obtained, thresholds of the temperature anomaly frequency and temperature anomaly duration are set in advance, areas where the temperature anomaly frequency and temperature anomaly duration are greater than the corresponding thresholds are marked as abnormal areas, and information of the abnormal areas is transmitted to the control unit for optimization.

10. A method for real-time temperature control of cold chain logistics, characterized in that: The following steps are involved: Step 1: Acquire parameter information in the cold chain logistics process, the monitoring unit is used to monitor the acquisition unit to acquire parameter information for preprocessing, and remove abnormal parameter information; Step 2: judging whether there is any abnormality in the cold chain logistics according to the preprocessed parameter information of the data acquisition module, and responding according to the judgment result of the judgment unit to adjust the control parameters of the control unit; Step 3: When the judgment unit judges that an abnormality occurs, the abnormal object is quickly located and isolated.

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