A method and system for instrument measurement detection and calibration

Abstract

The present invention discloses an instrument measurement detection and calibration method and system, which specifically relates to the technical field of instrument calibration, and is used to solve the problem that the potential error hidden danger of the increase of instrument measurement data deviation cannot be timely identified in the existing cold chain transportation. Before the start of the cold chain transportation, it is judged whether the altitude change affects the measurement accuracy, and by analyzing the historical error events and environmental parameters, the probability of intermittent error caused by condensation phenomenon is evaluated; based on the altitude change and the condensation error probability, it is judged whether the instrument calibration is needed; by analyzing the influence of the thermal hysteresis effect on the measurement quantified by the heat conduction path disturbance, and by analyzing the time-varying entropy transfer between the environmental parameters and the instrument signal, the influence of the environmental fluctuation on the instrument measurement accuracy is evaluated; the thermal hysteresis effect and the environmental influence are comprehensively evaluated to judge whether calibration is needed, thereby improving the measurement accuracy and stability of the instrument during the cold chain transportation, and ensuring the efficiency of the cold chain transportation and the quality of the goods.

Description

Technical Field

[0001] The present invention relates to the technical field of instrument calibration, and more specifically, to an instrument measurement detection calibration method and system. Background Art

[0002] Cold chain transportation refers to the process of maintaining the low temperature environment required by the goods during the logistics transportation process to ensure the quality and safety of temperature-sensitive goods such as perishable foods and pharmaceutical products. In cold chain transportation, accurate monitoring of temperature and humidity is crucial, because slight changes in temperature and humidity may cause the quality of the goods to deteriorate or even be scrapped. Therefore, the measurement accuracy and reliability of temperature and humidity instruments are of great significance for the full monitoring of cold chain transportation.

[0003] There are deficiencies in the calibration method of temperature and humidity instruments in cold chain transportation. Usually, only one-time calibration is performed before leaving the factory, ignoring the influence of complex environmental factors during transportation, and the measurement accuracy of the instrument may be significantly affected. However, the existing technology lacks a comprehensive evaluation and dynamic calibration mechanism for these influencing factors, resulting in increased deviations in instrument measurement data and failure to identify potential error hazards in a timely manner, which in turn affects temperature control and cargo quality assurance in cold chain transportation.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an instrument measurement detection calibration method and system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An instrument measurement detection and calibration method comprises the following steps:

[0008] S1: Before the cold chain transportation begins, analyze the altitude of the cold chain transportation route to determine whether the rapid altitude change of the cold chain transportation route is serious; by analyzing the historical error event sequence and environmental parameter sequence of the instrument electrical contact point, determine whether the probability of intermittent measurement error caused by condensation is too large;

[0009] S2: Determine whether to perform instrument calibration before cold chain transportation based on whether the cold chain transportation route has a serious rapid change in altitude and whether the probability of intermittent measurement errors caused by condensation is too high;

[0010] S3: By analyzing the disturbance of the instrument heat conduction path caused by the frequent opening and closing of the compartment door during cold chain transportation, the error degree of measurement hidden dangers caused by the thermal hysteresis effect of the instrument is evaluated;

[0011] S4: Analyze the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation, and evaluate the potential risk of instrument calibration due to environmental influences;

[0012] S5: During cold chain transportation, a comprehensive analysis is conducted on the degree of potential measurement errors caused by the thermal hysteresis effect of the instrument and the degree of potential calibration required by the instrument due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too great.

[0013] In a preferred embodiment, the altitude of the cold chain transportation route is analyzed to determine whether the rapid altitude change of the cold chain transportation route is serious, specifically including:

[0014] Use geographic information systems to collect latitude, longitude and elevation data of cold chain transportation routes;

[0015] Calculate the altitude change rate between adjacent sampling points: the altitude change rate is the ratio of the altitude difference between adjacent sampling points to the horizontal distance between adjacent sampling points;

[0016] Marking the rapid altitude change section where the altitude change rate exceeds a predetermined threshold;

[0017] Count the total length of the fast-changing section and its proportion in the entire cold chain transportation route;

[0018] Based on the proportion of rapidly changing sections, it is possible to determine whether the rapid altitude changes in the cold chain transportation route are serious.

[0019] In a preferred embodiment, by analyzing the historical error event sequence and the environmental parameter sequence of the electrical contact points of the instrument, it is determined whether the probability of intermittent measurement errors caused by condensation is too large, specifically including:

[0020] Obtain the historical error event sequence of the instrument electrical contact points, and simultaneously collect the environmental parameter sequence of the corresponding time period;

[0021] Define the normal measurement state and the error state, and calculate the transition probability from the normal state to the error state;

[0022] The Markov chain Monte Carlo method is applied to construct a probability model of intermittent error occurrence;

[0023] By simulating the probability model of intermittent errors, the probability distribution of errors under future environmental conditions is predicted, and it is determined whether the probability of intermittent measurement errors caused by condensation is too large.

[0024] In a preferred embodiment, based on whether the rapid altitude change in the cold chain transportation route is serious and whether the probability of intermittent measurement errors caused by condensation is too large, it is determined whether to perform instrument calibration before cold chain transportation, specifically:

[0025] When the rapid change in altitude on the cold chain transportation route is not serious and the probability of intermittent measurement errors caused by condensation is not too high, it is determined that cold chain transportation can be carried out directly; otherwise, it is determined that instrument calibration is required before cold chain transportation can be carried out.

[0026] In a preferred embodiment, by analyzing the disturbance of the instrument heat conduction path caused by the frequent opening and closing of the compartment door during cold chain transportation, the degree of measurement error caused by the thermal hysteresis effect of the instrument is evaluated, specifically:

[0027] Build a model of the heat conduction path inside the instrument and identify the key paths and nodes of heat transfer;

[0028] Simulate the temperature fluctuation caused by frequent opening and closing of carriage doors during cold chain transportation, and impose temperature change conditions on the heat conduction path;

[0029] Combined with random vibration analysis, the micro-perturbation behavior of the heat conduction path caused by temperature changes is evaluated;

[0030] The heat conduction path disturbance index is calculated based on the offset and disturbance frequency of the heat conduction path. The degree of measurement error caused by the thermal hysteresis effect of the instrument is quantified based on the heat conduction path disturbance index:

[0031] The heat conduction path offset is defined as the time Physical offset of the heat transfer path: ;in, Indicates at time The thermal path offset under is the initial offset, Indicates at time The intensity of vibration on

[0032] Calculate the perturbation frequency of the heat conduction path: ;in, represents the perturbation frequency of the heat conduction path, represents the average disturbance frequency, represents the variance of the disturbance frequency;

[0033] Calculate the heat conduction path perturbation index: ;in, represents the heat conduction path disturbance index, is the total observation time.

[0034] In a preferred embodiment, the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation is analyzed to assess the potential risk of instrument calibration due to environmental influences, specifically:

[0035] Correlate the environmental parameters and instrument signal data during cold chain transportation and define the entropy transfer path:

[0036] The formula of entropy transfer path is as follows: ;in, Indicates environmental parameters To the instrument signal The entropy transfer path between represents the entropy value of the environmental parameters, Indicates the entropy value of the instrument signal;

[0037] By calculating the entropy of the time-varying data of environmental parameters during cold chain transportation, the environmental changes are quantified;

[0038] Perform entropy analysis on the time-varying data of instrument signals to capture information changes caused by signal fluctuations;

[0039] A time-varying entropy transfer model between environmental parameters and instrument signals is established to analyze the information transfer characteristics between environmental parameters and instrument signals:

[0040] Calculate the entropy transfer of environmental parameters to instrument signals: ;in, It represents the entropy transfer of environmental parameters to instrument signals. It represents the conditional entropy of the instrument signal under known environmental parameters;

[0041] According to the time-varying entropy transfer results, the potential risk level of the instrument requiring calibration due to environmental influences is evaluated.

[0042] In a preferred embodiment, during cold chain transportation, a comprehensive analysis is performed on the degree of measurement potential error caused by the thermal hysteresis effect of the instrument and the degree of potential calibration of the instrument due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too large, specifically:

[0043] The entropy transfer amount of the instrument signal is normalized by the heat conduction path disturbance index and the environmental parameters, and the entropy transfer amount of the instrument signal is weighted by the normalized heat conduction path disturbance index and the environmental parameters to calculate the calibration risk coefficient of the cold chain transportation instrument;

[0044] When the calibration risk coefficient of the cold chain transportation instrument exceeds the preset threshold, it is determined that the measurement accuracy risk of the instrument in the cold chain transportation scenario is too high;

[0045] When the calibration risk coefficient of the cold chain transportation instrument does not exceed the preset threshold, it is determined that the measurement accuracy risk level of the instrument in the cold chain transportation scenario is normal.

[0046] On the other hand, the present invention provides an instrument measurement detection and calibration system, including a pre-transportation analysis module, a pre-transportation judgment module, a thermal hysteresis analysis module, a time-varying entropy analysis module, and an instrument calibration judgment module;

[0047] Pre-transportation analysis module: Before the cold chain transportation begins, the altitude of the cold chain transportation route is analyzed to determine whether the rapid altitude change of the cold chain transportation route is serious; by analyzing the historical error event sequence and environmental parameter sequence of the instrument electrical contact point, it is determined whether the probability of intermittent measurement errors caused by condensation is too high;

[0048] Pre-transportation judgment module: Based on whether the cold chain transportation route has a serious rapid change in altitude and whether the probability of intermittent measurement errors caused by condensation is too high, it is determined whether to perform instrument calibration before cold chain transportation;

[0049] Thermal hysteresis analysis module: By analyzing the disturbance of the instrument heat conduction path caused by the frequent opening and closing of the compartment door during cold chain transportation, the error degree of measurement hidden dangers caused by the thermal hysteresis effect of the instrument is evaluated;

[0050] Time-varying entropy analysis module: Analyze the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation, and evaluate the potential risk of instrument calibration due to environmental influences;

[0051] Instrument calibration judgment module: During cold chain transportation, a comprehensive analysis is conducted on the degree of measurement potential error caused by the thermal hysteresis effect of the instrument and the degree of potential calibration required due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too great.

[0052] The technical effects and advantages of the instrument measurement detection and calibration method and system of the present invention are as follows:

[0053] 1. By analyzing the altitude changes of the cold chain transportation route, the potential impact of rapid altitude changes on the instrument's measurement accuracy can be determined in advance, avoiding data deviations caused by changes in air pressure. Secondly, based on the analysis of the historical error event sequence of the instrument's electrical contacts and the environmental parameter sequence, the probability of intermittent errors caused by condensation can be effectively evaluated, and the impact of condensation on instrument performance can be predicted and prevented in advance. Based on a comprehensive analysis of rapid altitude changes and condensation phenomena, it is possible to accurately determine whether instrument calibration is required. This method avoids the problems of over-calibration and ignoring potential error hazards.

[0054] 2. By analyzing the disturbance of the heat conduction path caused by the frequent opening and closing of the carriage doors, the hidden dangers of the thermal hysteresis effect on the measurement accuracy are quantitatively evaluated, so as to calibrate the instrument in time to ensure the accuracy of the data. By analyzing the time-varying entropy transfer between environmental parameters and instrument signals, it is possible to dynamically evaluate the impact of environmental factors on instrument signals and further improve the reliability of measurements. Comprehensively analyze the hidden dangers of the instrument caused by thermal hysteresis effects and environmental influences, and through weighted calculation of the calibration hidden danger coefficient of cold chain transportation instruments, determine whether the instrument needs to be calibrated, avoid unnecessary calibration operations, improve transportation efficiency, and ensure the continuity of data accuracy. It can not only improve the measurement reliability of instruments in cold chain transportation, but also reduce the waste of resources caused by insufficient or excessive calibration, and effectively ensure the safety and quality of goods in cold chain transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of an instrument measurement detection and calibration method of the present invention;

[0056] Figure 2 The present invention is a schematic structural diagram of an instrument measurement, detection and calibration system. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] Figure 1 The present invention provides an instrument measurement detection calibration method, which comprises the following steps:

[0060] S1: Before the start of cold chain transportation, analyze the altitude of the cold chain transportation route to determine whether the rapid change in altitude on the cold chain transportation route is serious; by analyzing the historical error event sequence and environmental parameter sequence of the instrument's electrical contacts, determine whether the probability of intermittent measurement errors caused by condensation is too large.

[0061] S2: Determine whether to perform instrument calibration before cold chain transportation based on whether the rapid altitude changes along the cold chain transportation route are serious and whether the probability of intermittent measurement errors caused by condensation is too high.

[0062] S3: By analyzing the disturbance of the instrument's heat conduction path caused by the frequent opening and closing of carriage doors during cold chain transportation, the degree of measurement error caused by the thermal hysteresis effect of the instrument is evaluated.

[0063] S4: Analyze the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation, and evaluate the potential risk of instrument calibration due to environmental influences.

[0064] S5: During cold chain transportation, a comprehensive analysis is conducted on the degree of potential measurement errors caused by the thermal hysteresis effect of the instrument and the degree of potential calibration required due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too great.

[0065] Analyze the altitude of the cold chain transportation route to determine whether the rapid altitude change of the cold chain transportation route is serious, including:

[0066] Use geographic information system to collect latitude, longitude and elevation data of cold chain transportation routes:

[0067] Use high-precision GPS equipment, combined with geographic information system (GIS) software, to collect continuous sampling points along the cold chain transportation route. The sampling interval can be set to one sampling point every 100 meters to ensure the density of the data.

[0068] The collected geographic information data includes the latitude, longitude and elevation of each sampling point. The sampling data should be guaranteed to have high accuracy, and the GPS equipment should achieve sub-meter positioning accuracy (error less than 1 meter) so that subsequent analysis can accurately reflect the actual altitude changes.

[0069] In order to ensure the accuracy of the data, the collected data must be initially filtered and cleaned to remove data points with large errors. If there is a deviation between the sampling point location and the actual route, the data can be corrected by interpolation or map correction.

[0070] Calculate the rate of change of altitude between adjacent collection points:

[0071] The altitude change rate can be calculated as the ratio of the altitude difference between adjacent sampling points to the horizontal distance between adjacent sampling points.

[0072] The horizontal distance between adjacent sampling points can be calculated using the spherical triangulation formula or the Euclidean geometry method, which will not be described in detail here.

[0073] Mark the rapid altitude change sections where the altitude change rate exceeds the predetermined threshold:

[0074] After calculating the altitude change rate of each sampling point, it is necessary to mark the segments where the change rate exceeds the predetermined threshold. The predetermined threshold should be set based on industry standards or historical data. For example, if the altitude change exceeds 50 meters per kilometer, it can be considered a rapid altitude change.

[0075] An automated script (such as Python, R, etc.) can be used to traverse all sampling points, calculate the elevation change rate in real time, and mark the sections that exceed the threshold. This can be achieved through conditional judgment: if the change rate of a section is greater than a predetermined threshold, the starting point and end point of the section are recorded and stored in the identification list.

[0076] Calculate the total length of the fast-changing section and its proportion in the entire cold chain transportation route:

[0077] The length of each marked segment is accumulated to obtain the total length of the rapidly changing segment, and the ratio of the total length of the rapidly changing segment to the total length of the cold chain transportation route is marked as the rapid altitude change ratio.

[0078] According to the proportion of the rapid change section, judge whether the rapid change in altitude of the cold chain transportation route is serious:

[0079] When the proportion of rapid altitude changes exceeds a predetermined critical value (for example, 20%), it is determined that the rapid altitude changes in the cold chain transportation route are serious, which may affect the stability of the instrument measurement; otherwise, it is determined that the rapid altitude changes in the cold chain transportation route are not serious.

[0080] By analyzing the historical error event sequence and environmental parameter sequence of the instrument's electrical contacts, determine whether the probability of intermittent measurement errors caused by condensation is too high, including:

[0081] Obtain the historical error event sequence of the instrument electrical contact points, and simultaneously collect the environmental parameter sequence of the corresponding time period:

[0082] Each error event contains a timestamp, error type, and error magnitude. The timestamp records the specific time when the error occurred, the error type distinguishes different types of errors, such as intermittent or continuous errors, and the error magnitude indicates the magnitude of the deviation of the measured value from the normal value.

[0083] Environmental parameters include temperature, humidity, and dew point temperature. These parameters are also accompanied by timestamps to reflect the dynamic changes of the environment.

[0084] The error event sequence can be expressed as: ;in,

[0085] Indicated in The time of the error event at a time point, represents the error margin, Indicates the type of error (for example, intermittent or persistent).

[0086] The environmental parameter sequence is expressed as: ;in, , and Represents temperature, humidity and dew point temperature respectively, all synchronized with the time point.

[0087] In order to ensure the accuracy of the data, it is necessary to synchronize the error events and environmental parameters in time. The two are aligned in the time dimension through interpolation to ensure the accuracy of subsequent analysis.

[0088] Define the normal measurement state and the error state, and calculate the transition probability from the normal state to the error state:

[0089] Define the normal measurement state and error state of the system, and calculate the transition probability from the normal state to the error state based on historical data.

[0090] Normal status means that the instrument measurement data is within a reasonable error range and is not affected by condensation. If the measured error margin is lower than the set threshold, the system is considered to be in a normal state.

[0091] The normal measurement state can be defined as: ;in, Indicates at time The system is in normal measurement state. Indicates the error margin threshold under normal conditions.

[0092] When the measured error magnitude exceeds the set threshold, the meter enters an error state. This error may be caused by condensation or other external environmental influences.

[0093] The error state can be defined as: ;in, Indicates at time The system enters an error state.

[0094] The probability of transitioning from the normal state to the error state can be calculated by the following formula: ; represents the probability of transitioning from the normal state to the error state; is the indicator function, indicating that The number of events that transition from the normal state to the error state; Indicates the total observation time The total number of time periods during which the system is in a normal state.

[0095] The Markov chain Monte Carlo method is used to construct a probability model for intermittent errors:

[0096] In order to predict the probability of future errors, a probability model of intermittent errors can be constructed using Markov chain and Monte Carlo methods.

[0097] The Markov chain describes the transition behavior of the system from one state to another through the state transition matrix. This matrix represents the probability of each state transitioning to other states at a certain moment in the future.

[0098] The state transfer matrix is ​​expressed as follows: ;in, is the state transition matrix, represents the probability that the system remains in the normal state, represents the probability of the system transitioning from the normal state to the error state, represents the probability that the system recovers from the error state to the normal state, Represents the probability that the system remains in an error state.

[0099] The Monte Carlo method generates future state sequences based on the state transition matrix through a large number of random simulations. This method can predict the possible states of the system under different environmental conditions in the future.

[0100] By simulating the probability model of intermittent errors, the probability distribution of errors under future environmental conditions is predicted to determine whether the probability of intermittent measurement errors caused by condensation is too large:

[0101] Based on the aforementioned Markov chain model, the probability of error occurrence under future environmental conditions can be predicted. By simulating future environmental data, the possibility of errors in the system in the future can be obtained.

[0102] At each future moment, the probability of a system error can be derived from the state transition matrix.

[0103] The probability distribution of error occurrence is: ;in Indicates future time The probability of an error occurring, It represents the probability that the system is in an error state given the state transition matrix.

[0104] By accumulating the probability of future errors, it is possible to determine whether the risk of errors is beyond the acceptable range. If the risk is too high, measures need to be taken to intervene or calibrate.

[0105] The risk of error is assessed as: ;in, It represents the total probability error risk value. The larger the total probability error risk value is, the greater the probability of intermittent measurement error caused by condensation.

[0106] When the total probability error risk value is greater than the corresponding preset threshold, it is determined that the probability of occurrence of the intermittent measurement error caused by condensation is too large; otherwise, it is determined that the probability of occurrence of the intermittent measurement error caused by condensation is not too large.

[0107] Among them, the preset threshold corresponding to the total probability error risk value is usually based on historical data and the risk tolerance of specific application scenarios. First, by analyzing the historical error events and environmental parameters of the long-term operating instrument system, the normal error range and the frequency of abnormal error events under different environmental conditions are determined. Combined with statistical methods, such as analyzing the error risk distribution under different scenarios, the probability distribution curve of error occurrence when the risk is higher is determined. Then, a reasonable error probability is selected as the threshold for risk assessment, so that it can issue an early warning when an abnormal error is detected, while avoiding excessive intervention.

[0108] Based on whether the cold chain transportation route has a serious rapid change in altitude and whether the probability of intermittent measurement errors caused by condensation is too high, it is determined whether to perform instrument calibration before cold chain transportation. Specifically:

[0109] Rapid changes in altitude may cause changes in ambient pressure and temperature, which in turn affect the measurement accuracy of the instrument. If the altitude change is not severe, the probability of instrument measurement error is low, so no special calibration is required. Secondly, intermittent measurement errors caused by condensation are another important factor. When the ambient humidity is high and the temperature fluctuates greatly, condensation may affect the electrical contacts of the instrument, causing intermittent errors. If the probability of error is low, the risk is also small. Therefore, instrument calibration is only required when one of these two situations reaches a high risk to ensure measurement accuracy and data reliability during cold chain transportation.

[0110] Therefore: when the rapid change in altitude on the cold chain transportation route is not serious and the probability of intermittent measurement errors caused by condensation is not too high, it is determined that cold chain transportation can be carried out directly; otherwise, it is determined that instrument calibration is required before cold chain transportation can be carried out.

[0111] By comprehensively evaluating the error risks caused by altitude changes and condensation in cold chain transportation, it is possible to effectively reduce unnecessary calibration frequency, save time and resources. At the same time, it ensures that calibration is performed in a timely manner in high-risk environments to maintain the measurement accuracy of the instrument, thereby improving the reliability of temperature and humidity monitoring during transportation and ensuring the overall quality of the cold chain.

[0112] By analyzing the disturbance of the instrument heat conduction path caused by the frequent opening and closing of the carriage door during cold chain transportation, the error degree of the measurement hidden dangers caused by the thermal hysteresis effect of the instrument is evaluated, specifically:

[0113] Build a model of the heat conduction path inside the instrument and identify the key paths and nodes of heat transfer:

[0114] Build a physical model that can truly reflect the heat conduction path inside the instrument. The key path of heat transfer in the instrument involves the thermal conductivity of different materials (such as thermal conductivity), the geometric characteristics of structural components and their interaction with the external environment.

[0115] A heat transfer path can be defined as the physical path that heat is transferred through a thermally conductive material. For instruments in cold chain transportation, the critical heat transfer path may be the path from the external environment to the sensor. This involves the thermal conductivity of materials such as metals, plastics, etc., and the contact surfaces between these materials.

[0116] Nodes in the heat conduction path usually refer to key locations where temperature changes are likely to occur during heat transfer, such as material interfaces, sensor contact points, etc. Identifying these nodes helps to accurately locate areas where hysteresis or fluctuations may occur during heat transfer.

[0117] Each part in the model will be associated with the physical properties of heat transfer and modeled using the finite element method to ensure that the conduction of heat through the different components is accurately represented.

[0118] To simulate the temperature fluctuation caused by frequent opening and closing of carriage doors during cold chain transportation, temperature change conditions are imposed on the heat conduction path:

[0119] In cold chain transportation, frequent opening and closing of carriage doors can cause rapid changes in ambient temperature, which in turn affects the heat conduction path inside the instrument. Accurately simulate this temperature fluctuation and apply these changes to the heat conduction model to evaluate the impact of temperature fluctuations on the heat conduction path.

[0120] Assume that the ambient temperature is The change to , the change can be represented by a periodic function, for example: ;in, Indicates at time The ambient temperature at represents the initial ambient temperature, The magnitude of the temperature change, Indicates the frequency of temperature change.

[0121] When temperature changes are applied to the heat conduction model, the boundary conditions of the model will change dynamically over time, reflecting the impact of frequent opening and closing of the car door on the external temperature.

[0122] Boundary conditions for temperature changes: For different parts of the heat conduction path, boundary conditions need to be defined to reflect the temperature changes at the interfaces of different materials. Temperature fluctuations are transmitted through these interfaces and will directly affect the heat conduction path of the internal sensor.

[0123] Combined with random vibration analysis, the behavior of micro-perturbations of the heat conduction path caused by temperature changes is evaluated:

[0124] During frequent temperature fluctuations, the heat conduction path is not only affected by temperature changes, but may also be affected by environmental vibrations (such as the vibration of a refrigerated truck during driving). Therefore, the heat conduction path may produce micro-perturbations, which will affect the stability of heat transfer.

[0125] Assume that the ambient vibration is The performance of the above is a random process The vibration can be modeled by statistical methods, for example using a normally distributed random process: ;in, Indicates at time The vibration intensity on represents the average value of vibration intensity, Represents the variance of vibration intensity.

[0126] This random vibration analysis allows the evaluation of vibration-induced heat path excursions. The effects of vibration can be applied to the nodes of the heat path to simulate micro-perturbation behavior during frequent temperature changes.

[0127] The heat conduction path disturbance index is calculated based on the offset and disturbance frequency of the heat conduction path. The degree of measurement error caused by the thermal hysteresis effect of the instrument is quantified based on the heat conduction path disturbance index:

[0128] The heat conduction path offset is defined as the time The physical offset of the heat transfer path. This offset can be obtained through simulation: ;in, Indicates at time The thermal path offset under is the initial offset,

[0129] The perturbation frequency of the heat conduction path represents the perturbation frequency of the heat conduction path under the action of vibration, which can be calculated by frequency analysis method. Assume that the frequency distribution of vibration is normal distribution: ;in, represents the perturbation frequency of the heat conduction path, represents the average disturbance frequency, represents the variance of the disturbance frequency.

[0130] The heat transfer path perturbation index can be calculated by combining the offset and the perturbation frequency: ;in, It represents the heat conduction path disturbance index, which reflects the overall disturbance degree of the heat conduction path; is the total observation time.

[0131] The calculated heat conduction path disturbance index can be used to quantify the potential measurement error caused by the thermal hysteresis effect of the instrument. That is, the larger the heat conduction path disturbance index is, the higher the degree of potential measurement error caused by the thermal hysteresis effect of the instrument is, and the more significant the disturbance to the heat conduction path is, which may lead to a decrease in the temperature measurement accuracy of the instrument.

[0132] Analyze the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation, and evaluate the potential risk of instrument calibration due to environmental influences, specifically:

[0133] Correlate the environmental parameters and instrument signal data during cold chain transportation and define the entropy transfer path:

[0134] In cold chain transportation, changes in environmental parameters will directly affect the stability and accuracy of instrument signals. Environmental parameters (such as temperature, humidity, etc.) in cold chain transportation are associated with instrument signals to identify the dependency between the two through the information transmission path.

[0135] Through the data synchronization mechanism, the time series data of environmental parameters and instrument signals are aligned to ensure that they are analyzed in the same time frame. The associated parameters include ambient temperature, humidity, and the corresponding instrument output signal.

[0136] The entropy transfer path refers to the energy or information flow path from the environmental parameters to the instrument signal. In this case, the purpose of defining the entropy transfer path is to quantify the impact of environmental parameters on the instrument signal and to measure the efficiency of information transfer from the environment to the instrument signal through information theory methods.

[0137] The formula of entropy transfer path is as follows: ;in, Indicates environmental parameters To the instrument signal The entropy transfer path between represents the entropy value of the environmental parameters, Represents the entropy value of the instrument signal.

[0138] Through the above path, the impact path of environmental parameters on instrument signals can be clearly defined, and a basis can be provided for the subsequent quantification of entropy transfer.

[0139] By calculating the entropy of the time-varying data of environmental parameters during cold chain transportation, the environmental changes can be quantified:

[0140] By analyzing the time-varying data of environmental parameters during cold chain transportation, the entropy value at each time point is calculated to quantify the changes in environmental parameters.

[0141] For the time-varying data of environmental parameters, entropy in information theory is used to quantify uncertainty. Assuming that the environmental parameters change over time as a set of time series data, the entropy calculation formula is: ;in, Indicates at a point in time The environmental parameter value at Indicates the environmental parameters at a point in time The probability distribution at is the total number of time points in the environmental parameter sequence, Represents the index value in the time series, Indicates the first a point in time.

[0142] The entropy value of environmental parameters reflects the degree of fluctuation of environmental parameters. The larger the entropy value, the higher the uncertainty of environmental changes. During cold chain transportation, large environmental fluctuations may interfere with instrument measurement.

[0143] Perform entropy analysis on the time-varying data of the instrument signal to capture the information changes caused by signal fluctuations:

[0144] Assuming that the instrument signal data changes over time as a set of time series data, a similar entropy formula can be used for analysis: ;in, Indicates at a point in time The instrument signal value at Indicates the instrument signal at the time point The probability distribution at .

[0145] By analyzing the entropy of the instrument signal, we can capture the information changes brought about by the signal fluctuation. If the entropy value of the signal is too large, it means that the instrument signal fluctuates violently, and the measurement accuracy of the system may be affected. is the total number of time points in the instrument signal sequence.

[0146] A time-varying entropy transfer model between environmental parameters and instrument signals is established to analyze the information transfer characteristics between environmental parameters and instrument signals:

[0147] A time-varying entropy transfer model is established. The time-varying entropy transfer model is used to evaluate how changes in environmental parameters affect the fluctuations of instrument signals. The transfer model can be expressed in terms of conditional entropy, which is the contribution of environmental parameters to changes in instrument signals. The formula is as follows: ;in, It represents the entropy transfer of environmental parameters to instrument signals. It represents the conditional entropy of the instrument signal under known environmental parameters.

[0148] The amount of entropy transfer from environmental parameters to instrument signals reflects the intensity of the impact of environmental parameter changes on instrument signals. If the amount of entropy transfer from environmental parameters to instrument signals is large, it means that environmental fluctuations have a significant impact on instrument signals, which may lead to an increase in instrument measurement errors.

[0149] According to the time-varying entropy transfer results, the potential risk of the instrument needing calibration due to environmental influences is evaluated:

[0150] The greater the entropy transfer of environmental parameters to instrument signals, the higher the risk of the instrument needing calibration due to environmental influences. This indicates that the more significant the impact of environmental changes on instrument signals, the more likely it is that the measurement error will increase, and therefore the more likely it is that the instrument needs to be calibrated.

[0151] During cold chain transportation, a comprehensive analysis is conducted on the degree of potential measurement errors caused by the thermal hysteresis effect of the instrument and the degree of potential calibration required by the instrument due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too great. Specifically:

[0152] The heat conduction path disturbance index and environmental parameters are normalized to the entropy transfer of the instrument signal, and the normalized heat conduction path disturbance index and environmental parameters are weighted to the entropy transfer of the instrument signal to calculate the cold chain transportation instrument calibration hidden danger coefficient. The calculation of the cold chain transportation instrument calibration hidden danger coefficient can be realized in the following way:

[0153] set up and They represent the weight coefficients of the heat conduction path disturbance index and the entropy transfer of the environmental parameters to the instrument signal. The calculation formula for the calibration risk coefficient of cold chain transportation instruments is as follows: ;in, Indicates the potential risk coefficient of cold chain transportation instrument calibration. and is the preset weight coefficient, and and are both greater than 0, reflecting the relative importance of thermal hysteresis effect and environmental influence; and They are the normalized heat conduction path disturbance index and the entropy transfer of environmental parameters to instrument signals.

[0154] The cold chain transport instrument calibration risk coefficient calculated above is used to evaluate the comprehensive calibration risk level of the instrument. When the cold chain transport instrument calibration risk coefficient exceeds the preset threshold , it is determined that the measurement accuracy of the instrument in the cold chain transportation scenario has a high risk and needs to be calibrated or adjusted, including:

[0155] Instrument calibration: Recalibrate the instrument at the end of transportation or during transportation to ensure its measurement accuracy.

[0156] Optimize environmental conditions: Adjust the cold chain transportation environment, such as reducing temperature fluctuations or vibration sources, to reduce the impact of the environment on the instrument.

[0157] Continuous monitoring: Strengthen real-time monitoring of instrument data, identify anomalies in a timely manner and adjust transportation strategies to prevent error accumulation from causing damage to goods.

[0158] When the cold chain transportation instrument calibration risk coefficient does not exceed the preset threshold , it is determined that the measurement accuracy risk level of the instrument in the cold chain transportation scenario is normal, and no measures are needed for the time being.

[0159] in, It is the threshold of the calibration risk of cold chain transportation instruments, which is usually set based on historical data, industry standards and experimental analysis. First, by analyzing a large amount of instrument calibration data in the cold chain transportation process, the normal working range of the instrument under different environmental and operating conditions is determined. Combined with the actual impact of thermal hysteresis and environmental influence on measurement accuracy, the upper threshold of the instrument measurement error is found. In addition, through simulation experiments and scenario tests, this value is adjusted so that it can reflect the calibration requirements of the instrument in harsh environments. The setting is designed to ensure the reliability and accuracy of the measurement and avoid over-frequent calibration.

[0160] Example 2

[0161] The difference between Example 2 of the present invention and Example 1 is that this example introduces an instrument measurement, detection and calibration system.

[0162] Figure 2 A structural schematic diagram of an instrument measurement detection and calibration system of the present invention is given, which includes a pre-transportation analysis module, a pre-transportation judgment module, a thermal hysteresis analysis module, a time-varying entropy analysis module and an instrument calibration judgment module.

[0163] Pre-transportation analysis module: Before the start of cold chain transportation, the altitude of the cold chain transportation route is analyzed to determine whether the rapid altitude changes in the cold chain transportation route are serious; by analyzing the historical error event sequence and environmental parameter sequence of the instrument's electrical contacts, it is determined whether the probability of intermittent measurement errors caused by condensation is too large.

[0164] Pre-transportation judgment module: Based on whether the rapid change in altitude on the cold chain transportation route is serious and whether the probability of intermittent measurement errors caused by condensation is too high, it is determined whether to perform instrument calibration before cold chain transportation.

[0165] Thermal hysteresis analysis module: By analyzing the disturbance of the instrument's heat conduction path caused by the frequent opening and closing of carriage doors during cold chain transportation, the degree of measurement error caused by the thermal hysteresis effect of the instrument is evaluated.

[0166] Time-varying entropy analysis module: Analyzes the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation, and evaluates the potential risk of instrument calibration due to environmental influences.

[0167] Instrument calibration judgment module: During cold chain transportation, a comprehensive analysis is conducted on the degree of measurement potential error caused by the thermal hysteresis effect of the instrument and the degree of potential calibration required due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too great.

[0168] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0169] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0170] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0171] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0172] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0173] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0174] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0175] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0176] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0177] 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 in the protection scope of the present invention.

Claims

1. An instrument measurement detection and calibration method, characterized in that: The steps include: S1: Before the cold chain transportation begins, analyze the altitude of the cold chain transportation route to determine whether the rapid altitude change of the cold chain transportation route is serious; by analyzing the historical error event sequence and environmental parameter sequence of the instrument electrical contact point, determine whether the probability of intermittent measurement error caused by condensation is too large; S2: Determine whether to perform instrument calibration before cold chain transportation based on whether the cold chain transportation route has a serious rapid change in altitude and whether the probability of intermittent measurement errors caused by condensation is too high; S3: By analyzing the disturbance of the instrument heat conduction path caused by the frequent opening and closing of the carriage doors during cold chain transportation, the degree of measurement error caused by the thermal hysteresis effect of the instrument is evaluated; S4: Analyze the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation, and evaluate the potential risk of instrument calibration due to environmental influences; S5: During cold chain transportation, a comprehensive analysis is conducted on the degree of potential measurement errors caused by the thermal hysteresis effect of the instrument and the degree of potential calibration required by the instrument due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too great; Analyze the altitude of the cold chain transportation route to determine whether the rapid altitude change of the cold chain transportation route is serious, including: Use geographic information systems to collect latitude, longitude and elevation data of cold chain transportation routes; Calculate the altitude change rate between adjacent sampling points: the altitude change rate is the ratio of the altitude difference between adjacent sampling points to the horizontal distance between adjacent sampling points; Marking the rapid altitude change section where the altitude change rate exceeds a predetermined threshold; Count the total length of the fast-changing section and its proportion in the entire cold chain transportation route; According to the proportion of the rapid change section, determine whether the rapid change in altitude of the cold chain transportation route is serious; By analyzing the historical error event sequence and environmental parameter sequence of the instrument's electrical contacts, determine whether the probability of intermittent measurement errors caused by condensation is too high, including: Obtain the historical error event sequence of the instrument electrical contact points, and simultaneously collect the environmental parameter sequence of the corresponding time period; Define the normal measurement state and the error state, and calculate the transition probability from the normal state to the error state; The Markov chain Monte Carlo method is applied to construct a probability model of intermittent error occurrence; By simulating the probability model of intermittent errors, the probability distribution of errors under future environmental conditions is predicted to determine whether the probability of intermittent measurement errors caused by condensation is too large. Based on whether the cold chain transportation route has a serious rapid change in altitude and whether the probability of intermittent measurement errors caused by condensation is too high, it is determined whether to perform instrument calibration before cold chain transportation. Specifically: When the rapid altitude change on the cold chain transportation route is not serious and the probability of intermittent measurement errors caused by condensation is not too high, it is determined that cold chain transportation can be carried out directly; otherwise, it is determined that instrument calibration is required before cold chain transportation; By analyzing the disturbance of the instrument heat conduction path caused by the frequent opening and closing of the carriage door during cold chain transportation, the error degree of the measurement hidden dangers caused by the thermal hysteresis effect of the instrument is evaluated, specifically: Build a model of the heat conduction path inside the instrument and identify the key paths and nodes of heat transfer; Simulate the temperature fluctuation caused by frequent opening and closing of carriage doors during cold chain transportation, and impose temperature change conditions on the heat conduction path; Combined with random vibration analysis, the micro-perturbation behavior of the heat conduction path caused by temperature changes is evaluated; The heat conduction path disturbance index is calculated based on the offset and disturbance frequency of the heat conduction path. The degree of measurement error caused by the thermal hysteresis effect of the instrument is quantified based on the heat conduction path disturbance index: The heat conduction path offset is defined as the time Physical offset of the heat transfer path: ;in, Indicates at time The thermal path offset under is the initial offset, Indicates at time The intensity of vibration on Calculate the perturbation frequency of the heat conduction path: ;in, represents the perturbation frequency of the heat conduction path, represents the average disturbance frequency, represents the variance of the disturbance frequency, Used to represent normal distribution; Calculate the heat conduction path perturbation index: ;in, represents the heat conduction path disturbance index, is the total observation time; Analyze the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation, and evaluate the potential risk of instrument calibration due to environmental influences, specifically: Correlate the environmental parameters and instrument signal data during cold chain transportation and define the entropy transfer path: The formula for the entropy transfer path is as follows: ;in, Indicates environmental parameters To the instrument signal The entropy transfer path between represents the entropy value of the environmental parameters, Indicates the entropy value of the instrument signal; By calculating the entropy of the time-varying data of environmental parameters during cold chain transportation, the environmental changes are quantified; Perform entropy analysis on the time-varying data of instrument signals to capture information changes caused by signal fluctuations; A time-varying entropy transfer model between environmental parameters and instrument signals is established to analyze the information transfer characteristics between environmental parameters and instrument signals: Calculate the entropy transfer of environmental parameters to instrument signals: ;in, It represents the entropy transfer of environmental parameters to instrument signals. It represents the conditional entropy of the instrument signal under known environmental parameters; According to the time-varying entropy transfer results, the potential risk of the instrument needing calibration due to environmental influences is evaluated; During cold chain transportation, a comprehensive analysis is conducted on the degree of potential measurement errors caused by the thermal hysteresis effect of the instrument and the degree of potential calibration required by the instrument due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too great. Specifically: The entropy transfer amount of the instrument signal is normalized by the heat conduction path disturbance index and the environmental parameters, and the entropy transfer amount of the instrument signal is weighted by the normalized heat conduction path disturbance index and the environmental parameters to calculate the calibration risk coefficient of the cold chain transportation instrument; When the calibration risk coefficient of the cold chain transportation instrument exceeds the preset threshold, it is determined that the measurement accuracy risk of the instrument in the cold chain transportation scenario is too high; When the calibration risk coefficient of the cold chain transportation instrument does not exceed the preset threshold, it is determined that the measurement accuracy risk level of the instrument in the cold chain transportation scenario is normal.

2. An instrument measurement detection and calibration system, used to implement an instrument measurement detection and calibration method according to claim 1, characterized in that: It includes pre-transportation analysis module, pre-transportation judgment module, thermal hysteresis analysis module, time-varying entropy analysis module and instrument calibration judgment module; Pre-transportation analysis module: Before the cold chain transportation begins, the altitude of the cold chain transportation route is analyzed to determine whether the rapid altitude change of the cold chain transportation route is serious; by analyzing the historical error event sequence and environmental parameter sequence of the instrument electrical contact point, it is determined whether the probability of intermittent measurement errors caused by condensation is too high; Pre-transportation judgment module: Based on whether the cold chain transportation route has a serious rapid change in altitude and whether the probability of intermittent measurement errors caused by condensation is too high, it is determined whether to perform instrument calibration before cold chain transportation; Thermal hysteresis analysis module: By analyzing the disturbance of the instrument heat conduction path caused by the frequent opening and closing of the compartment door during cold chain transportation, the error degree of measurement hidden dangers caused by the thermal hysteresis effect of the instrument is evaluated; Time-varying entropy analysis module: Analyze the time-varying entropy transfer between environmental parameters and instrument signals during cold chain transportation, and evaluate the potential risk of instrument calibration due to environmental influences; Instrument calibration judgment module: During cold chain transportation, a comprehensive analysis is conducted on the degree of measurement potential error caused by the thermal hysteresis effect of the instrument and the degree of potential calibration required due to environmental influences to determine whether the potential measurement accuracy risk of the instrument in the cold chain transportation scenario is too great.

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