Goods transportation state monitoring system based on Internet of Things

Abstract

The invention discloses a cargo transportation state monitoring system based on the Internet of Things, and relates to the technical field of logistics management. Electromagnetic signal intensity of each frequency band in a cargo box is recorded in real time through an electromagnetic interference monitoring module, and multi-point temperature data and temperature deviation information collected by a redundant sensor module are combined; the data processing and transmission module is used for preprocessing the signals and uploading the signals to the cloud platform, and the comprehensive analysis module extracts spectrum interference intensity abnormal features and temperature difference fluctuation features and accurately evaluates the influence of electromagnetic interference on the sensor data precision; then, the interference division module divides the interference event into instantaneous interference and continuous interference, the early warning processing module delays the instantaneous interference to trigger the temperature control equipment to adjust and send low-level early warning, and the continuous interference is deeply analyzed to generate a graded early warning signal and guide a coping strategy; and the data reliability, the equipment stability and the cargo protection capability of the cold chain transportation system in a complex electromagnetic environment are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a cargo transportation status monitoring system based on the Internet of Things. Background Art

[0002] Cargo transportation status monitoring based on the Internet of Things refers to the use of Internet of Things technology to monitor and manage the status information of goods during transportation in real time, such as location, temperature, humidity, vibration, tilt, light and other environmental parameters. This system usually includes sensors, communication modules, cloud platforms and data analysis tools. Sensors collect cargo status information, and communication modules transmit data to the cloud. After data analysis, real-time warnings and reports can be provided to help logistics companies and customers improve transportation efficiency and safety. Specifically, in cold chain logistics, temperature is a key factor when transporting fresh food or vaccines. After the goods are loaded, the temperature sensor in each cargo box monitors the temperature data in real time and transmits the data to the cloud platform through the cellular network. Once the temperature exceeds the preset range, the system will immediately send an alarm to the driver and management personnel to remind them to take measures (such as adjusting the refrigeration equipment or re-planning the transportation route). This approach ensures the quality of the goods while improving transportation transparency and customer satisfaction.

[0003] The prior art has the following deficiencies: Some cold chain transport vehicles use high-power motors or other electrical equipment that may generate electromagnetic interference (EMI) to the communication or reading of sensors. This can cause sensors to collect erroneous data or fail to communicate properly. In addition, when electromagnetic interference causes sensor data to be distorted, temperature control equipment may receive incorrect information, resulting in abnormal temperature regulation. For example, when transporting fresh fruit, the temperature control equipment may over-refrigerate the fruit, causing it to freeze, affecting its quality and value. Summary of the invention

[0004] The purpose of the present invention is to provide a cargo transportation status monitoring system based on the Internet of Things to solve the shortcomings of the background technology.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a cargo transportation status monitoring system based on the Internet of Things, comprising an electromagnetic interference monitoring module, a redundant sensor module, a data processing and transmission module, a comprehensive analysis module, an interference division module and an early warning processing module; Electromagnetic interference monitoring module: multiple electromagnetic interference monitors are installed in the cargo box to record the electromagnetic signal strength data in several frequency bands in real time; Redundant sensor module: includes at least two temperature sensors, which are used to simultaneously collect temperature data in the cargo box and calculate temperature deviation data between the temperature sensors in several time periods; Data processing and transmission module: used to pre-process electromagnetic signal strength data and temperature deviation data, and upload the processed data to the cloud platform through the cellular network; Comprehensive analysis module: extract the abnormal characteristics of spectrum interference intensity in the pre-processed electromagnetic signal strength data and the temperature difference fluctuation characteristics in the temperature deviation data, and evaluate the influence of electromagnetic interference on the accuracy of sensor data based on the extracted abnormal characteristics of spectrum interference intensity and temperature difference fluctuation characteristics; Interference classification module: used to classify interference events into instantaneous interference and continuous interference according to the degree of influence of electromagnetic interference on sensor data accuracy; Early warning processing module: For instantaneous interference events, the delay triggers the adjustment of temperature control equipment and sends a low-level early warning notification to the management personnel; for continuous interference events, further analysis is conducted on the impact of electromagnetic interference on sensor data accuracy within a fixed time period, and continuous interference events are graded according to the analysis results.

[0006] Preferably, in the comprehensive analysis module, after analyzing the abnormal characteristics of the spectrum interference intensity in the extracted electromagnetic signal strength data, a spectrum interference intensity abnormality index is generated, and the spectrum interference intensity abnormality index is obtained by: Suppose the spectrum signal data is X, with a dimension of n×m, where n is the number of samples and m is the number of features. is the signal strength of the i-th sample in the j-th frequency band. First, X is standardized and the expression is: ; In the formula, is the standardized data, is the mean signal strength of the jth frequency band, is the standard deviation of the signal strength in the jth frequency band, and the standardized matrix Z is obtained. The covariance matrix C of the standardized matrix Z is calculated, and the expression is: ; Where C is the m×m covariance matrix, each element represents the linear correlation between frequency band j and frequency band k, T is the matrix transpose, and the covariance matrix C is decomposed by eigenvalue to obtain: ; In the formula, is the kth eigenvalue, indicating the variance contribution rate of the principal component, is the kth eigenvector, corresponding to the direction of the principal component, the eigenvalues ​​are arranged in descending order, the first p largest eigenvalues ​​and the corresponding eigenvectors are taken to form the principal component subspace, and the standardized data Z is projected into the principal component space: ; Where Y is the principal component matrix after projection, with a dimension of n×p, is a matrix composed of the first p eigenvectors with a dimension of m×p. The original data is reconstructed using the projected principal component data Y: ; In the formula, The dimension of the reconstructed standardized data is n×m, and the reconstruction error of each sample is calculated: ; In the formula, is the reconstruction error of the i-th sample, according to the reconstruction error Calculate the spectrum interference intensity anomaly index, the expression is: ; In the formula, is the mean of all sample reconstruction errors, std(E) is the standard deviation of all sample reconstruction errors, and HKS is the spectrum interference intensity anomaly index.

[0007] Preferably, in the comprehensive analysis module, the temperature difference fluctuation characteristics in the extracted temperature deviation data are analyzed to generate a temperature difference fluctuation index, and the method for obtaining the temperature difference fluctuation index is: Assume that the temperature difference data is T(t), which is defined as a one-dimensional time series with a length of N and a sampling interval of Δt. ; Use Fourier transform to convert the time series T(t) into frequency domain representation F(f), the expression is: ; In the formula, For frequency The complex value of , j is the imaginary unit, is the kth frequency component, from Extracting the Amplitude Spectrum , the expression is: ; In the formula, for The real and imaginary parts of Indicates the contribution intensity of the frequency component to the temperature difference fluctuation, extracts the main frequency characteristics, and calculates the total spectrum energy of the temperature difference sequence , the expression is: ; Reflects the overall intensity of the entire temperature difference fluctuation and extracts the energy of the high-frequency components , the expression is: ; In the formula, is the threshold frequency of the high-frequency component, and calculates the proportion of high-frequency energy in the total energy , the expression is: ; In the formula, The proportion of violent fluctuations in the temperature difference data is quantified, and the temperature difference fluctuation index CHQ is calculated as the normalized form of the high-frequency energy proportion, and the expression is: ; In the formula, GHQ is the temperature fluctuation index, , is the mean and standard deviation of the high-frequency energy proportion, which is used for normalization.

[0008] Preferably, in the comprehensive analysis module, the spectrum interference intensity anomaly index and the temperature difference fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as inputs of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the impact value label of the electromagnetic interference on the sensor data accuracy as the prediction target, and takes minimizing the sum of the prediction errors of all electromagnetic interference impact value labels on the sensor data accuracy as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The impact value of the electromagnetic interference on the sensor data accuracy is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0009] Preferably, the interference classification module is used to classify interference events into instantaneous interference and continuous interference according to the degree of influence of electromagnetic interference on sensor data accuracy, specifically: The acquired electromagnetic interference impact value on the sensor data accuracy is compared with the impact value reference threshold preset according to historical data. If the electromagnetic interference impact value on the sensor data accuracy is greater than or equal to the impact value reference threshold, it means that the electromagnetic interference has a high impact on the sensor data accuracy. In this case, a high interference degree signal is generated and the interference event is classified as continuous interference. If the electromagnetic interference impact value on the sensor data accuracy is less than the impact value reference threshold, it means that the electromagnetic interference has a low impact on the sensor data accuracy. In this case, a low interference degree signal is generated and the interference event is classified as instantaneous interference.

[0010] Preferably, in the early warning processing module, for continuous interference events, the degree of influence of electromagnetic interference on sensor data accuracy within a fixed time period is further analyzed, and continuous interference events are graded according to the analysis results, specifically: For continuous interference events, that is, the impact degree of electromagnetic interference generated within a fixed time period on the sensor data accuracy is greater than or equal to the impact degree value reference threshold, the impact degree values ​​of electromagnetic interference generated within a subsequent fixed time period that are greater than or equal to the impact degree value reference threshold on the sensor data accuracy are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and the data set is analyzed. Based on the analysis results, the continuous interference events are graded.

[0011] Preferably, if the mean of the impact degree values ​​in the data set is greater than or equal to the reference threshold of the mean of the impact degree values, and the standard deviation of the impact degree values ​​is less than the reference threshold of the standard deviation of the impact degree values, the interference degree is high and stable, and a first-level warning signal is generated at this time, and the interference source needs to be checked first, and redundant data or protection mode is enabled in the system; If the mean of the impact value is greater than or equal to the reference threshold of the mean of the impact value, and the standard deviation of the impact value is greater than or equal to the reference threshold of the standard deviation of the impact value, the interference level is high and the fluctuation is large. At this time, a secondary warning signal is generated to monitor the interference fluctuation trend, delay system adjustment decisions, and optimize the sensor data processing algorithm; If the mean of the impact value is less than the reference threshold of the mean of the impact value, and the standard deviation of the impact value is greater than or equal to the reference threshold of the standard deviation of the impact value, the interference level is low but the fluctuation is large. At this time, a level 3 warning signal is generated and monitoring needs to be continued without immediate intervention. If the mean of the impact degree values ​​is less than the reference threshold of the mean of the impact degree values, and the standard deviation of the impact degree values ​​is less than the reference threshold of the standard deviation of the impact degree values, the interference level is low and the fluctuation is small. At this time, no early warning is required, the system maintains normal operation, and no additional adjustment measures are taken.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention effectively solves the problem of sensor data distortion caused by electromagnetic interference during cold chain transportation by integrating electromagnetic interference monitoring, redundant sensor data acquisition, cloud data processing and transmission, comprehensive analysis and early warning mechanisms. The impact of interference is quantitatively evaluated using the spectrum interference intensity anomaly index and the temperature difference fluctuation index, and the analysis accuracy is further improved through machine learning models to provide a scientific basis for the classification and early warning of interference events. The present invention can accurately identify instantaneous interference and continuous interference events in complex electromagnetic environments, and based on a graded early warning strategy based on the degree of interference impact, it can achieve delayed triggering and intelligent response to equipment adjustments, effectively ensuring the quality and transportation safety of cold chain goods.

[0013] 2. The present invention significantly improves the stability and reliability of the cold chain transport status monitoring system through innovative data analysis and processing solutions. In particular, in the case of continuous interference, the hierarchical processing of interference events is achieved through the analysis method of mean and standard deviation, which helps managers to take precise adjustment measures according to different warning levels. In addition, the system can also reduce the misoperation and energy loss of temperature control equipment, while enhancing the real-time monitoring capability of the cargo transportation environment, and improving the efficiency and intelligence level of cold chain logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0017] For examples, see Figure 1 As shown, the cargo transportation status monitoring system based on the Internet of Things described in this embodiment includes an electromagnetic interference monitoring module, a redundant sensor module, a data processing and transmission module, a comprehensive analysis module, an interference division module and an early warning processing module; Electromagnetic interference monitoring module: multiple electromagnetic interference monitors are installed in the cargo box to record the electromagnetic signal strength data in several frequency bands in real time; Redundant sensor module: includes at least two temperature sensors, which are used to simultaneously collect temperature data in the cargo box and calculate temperature deviation data between the temperature sensors in several time periods; Data processing and transmission module: used to pre-process electromagnetic signal strength data and temperature deviation data, and upload the processed data to the cloud platform through the cellular network; Comprehensive analysis module: extract the abnormal characteristics of spectrum interference intensity in the pre-processed electromagnetic signal strength data and the temperature difference fluctuation characteristics in the temperature deviation data, and evaluate the influence of electromagnetic interference on the accuracy of sensor data based on the extracted abnormal characteristics of spectrum interference intensity and temperature difference fluctuation characteristics; Interference classification module: used to classify interference events into instantaneous interference and continuous interference according to the degree of influence of electromagnetic interference on sensor data accuracy; Early warning processing module: For instantaneous interference events, the delay triggers the adjustment of temperature control equipment and sends a low-level early warning notification to the management personnel; for continuous interference events, further analysis is conducted on the impact of electromagnetic interference on sensor data accuracy within a fixed time period, and continuous interference events are graded according to the analysis results.

[0018] In the electromagnetic interference monitoring module, the electromagnetic interference monitor should have: Frequency band range: support frequency band monitoring from 50Hz to 1GHz, covering common sources of electromagnetic interference: Low frequency band (50Hz-1kHz): related to interference from industrial equipment such as motors and inverters. Medium frequency band (1kHz-30MHz): related to interference from communication equipment or switching power supplies. High frequency band (30MHz-1GHz): related to wireless communication (such as Wi-Fi, 4G / 5G) and microwave interference. Signal acquisition accuracy: Dynamic range: -120dBm to 0dBm, ensuring the ability to capture signals from weak interference to strong interference. Resolution bandwidth (RBW): support RBW of at least 10kHz for identifying refined interference features. Real-time and data transmission: Sampling frequency: ≥1 times / second to ensure real-time monitoring of interference changes. Data transmission: built-in cellular communication module or connected to the vehicle gateway, supports real-time uploading of interference data to the cloud platform. Working temperature range: -20°C to 60°C, adapting to cold chain transportation environment. Protection level: IP67, dustproof and waterproof, ensuring the normal operation of the device in humid or condensing conditions.

[0019] Arrange multiple monitors evenly at key locations in the cargo box (such as near sensors and next to refrigeration equipment) to capture electromagnetic signals in different areas. Increase the density of monitors at locations with known interference sources (such as near motors or inverters). Maintain an appropriate distance between monitors (such as ≥1 meter) to avoid mutual influence of signals between monitors. Small cargo box (≤10m³): Install 2-3 monitors to cover the front, middle, and rear of the cargo box respectively. Medium and large cargo boxes (10-30m³): Install 4-6 monitors, arranged symmetrically along both sides of the cargo box, focusing on covering the sensors and refrigeration equipment locations. In cases where the sensor layout is complex or the interference sources are densely distributed, increase the number of monitors or adopt a movable layout.

[0020] Each monitor collects electromagnetic signals at fixed time intervals (such as 1 second) and records the signal strength (unit: dBm) and the corresponding frequency. The data is marked with a timestamp and sent to the data processing module in real time. Signal preprocessing includes: Noise reduction processing: remove background noise through a bandpass filter to retain only the effective signal in the interference frequency band. Peak detection: extract the interference peak signal in a specific frequency band to locate the strongest interference source. Feature extraction: record the intensity mean, peak value and spectrum distribution characteristics of the interference signal. The data is transmitted to the cloud or local storage device through the on-board gateway. The system integrates and analyzes the multi-point monitoring data to generate a thermal distribution map of the electromagnetic interference in the cargo box.

[0021] Calibrate the monitor regularly (such as once every 3 months) to ensure measurement accuracy, especially after long-term use in a cold chain environment. When the monitor detects that the interference signal strength exceeds the set threshold (such as -50dBm), the alarm mechanism is immediately triggered and the driver or management platform is notified. For example, in cold chain transportation, four electromagnetic interference monitors are installed in the cargo box: two monitors are located at the front and rear of the cargo box, covering the refrigeration equipment; two monitors are located in the middle of the cargo box, close to the temperature sensor. Capture high-frequency interference signals during the operation of the refrigeration equipment in real time, and monitor medium and low-frequency interference near the sensor. When a medium-frequency interference signal of -60dBm is detected, the system generates an interference source location map through the cloud platform, prompting the driver to adjust the operating parameters of the refrigeration equipment to reduce interference.

[0022] In the redundant sensor module, at least two temperature sensors are installed in each cargo box. For larger cargo boxes or scenarios with uneven temperature distribution, the number of sensors can be increased (such as 4-6). Sensors should be distributed as evenly as possible in the cargo box to cover areas where the temperature may vary greatly. Increase the density of sensors in areas that are susceptible to interference or environmental impacts (such as near refrigeration equipment or cargo-intensive areas). Measurement range: Supports -40°C to +85°C to meet the needs of cold chain environments. Accuracy requirements: High-precision sensors with a tolerance of ±0.1°C are used to monitor goods that are sensitive to temperature fluctuations (such as vaccines). Response time: Fast response capability (<2 seconds) to ensure that temperature changes are captured. Communication interface: Supports I²C or UART protocols for easy integration with data processing modules.

[0023] Each sensor collects temperature data at a fixed interval (e.g. 1 second), marks the timestamp and stores it locally or uploads it to the cloud. The deviation calculation formula is: ; T1, T2 are the temperature data of the two sensors, and ΔT is the temperature deviation between the two. Calculate the mean and standard deviation of the deviation data within several time periods (such as 1 minute, 5 minutes) to extract the temperature difference fluctuation characteristics. Set the threshold (such as 0.2°C) according to the type of cargo box and the performance of the temperature control equipment. If ΔT>threshold, mark it as data abnormality. If the threshold is exceeded for multiple times, an alarm is triggered, indicating interference or equipment failure. When the deviation is within the threshold range, the data is considered reliable and directly enters the system analysis. If the deviation exceeds the range, compare and verify it through historical data and spatial models. Weighted processing is performed on the data of the two sensors to reduce the impact of single data distortion: when the deviation of a sensor exceeds a fixed threshold (such as 1°C), the sensor data is directly eliminated.

[0024] Minor Deviation: Deviations are within the threshold range and are marked as minor anomalies that do not require immediate intervention.

[0025] Moderate deviation: The deviation exceeds the threshold for a short time but there is no obvious trend. The system activates data correction and notifies the management backend to pay attention. Severe deviation: The deviation exceeds the threshold continuously, and the system triggers an alarm, prompting you to check the sensor or interference source. Automatically remove abnormal data and activate backup sensors (if there are more than 2 sensors).

[0026] Calibrate the sensors regularly (e.g. every 6 months) to ensure data accuracy, especially in extreme cold chain environments. The module has its own health status monitoring function (such as fault self-diagnosis) to report sensor performance in real time. For example, when transporting vaccines in the cold chain, 4 sensors are installed in the cargo box, located at the four corners of the cargo. The pairwise deviations of the 4 sensors are calculated every minute to generate an average deviation matrix. If it is detected that the deviation between sensor 3 and other sensors continues to exceed 0.5°C, the system automatically removes the data of sensor 3 and enables the data of other sensors to adjust the temperature control equipment.

[0027] In the data processing and transmission module, spectrum intensity data is received through the communication protocol of the monitor (such as I²C, UART or SPI). Real-time temperature values ​​of two or more temperature sensors are received and their deviation is calculated. The collected data is converted into a unified format, such as JSON or CSV format, for subsequent processing and transmission, including fields such as: timestamp, data type (electromagnetic signal strength or temperature deviation), data value, device ID or location identification; a bandpass filter is applied to the electromagnetic signal strength data to filter background noise and irrelevant frequency band signals. A smoothing algorithm (such as moving average or Kalman filter) is used for the temperature deviation data to reduce data fluctuations.

[0028] Identify abnormal peaks (such as interference intensity that is significantly higher than the average) and mark them as suspected interference events. Eliminate abnormal data with deviation values ​​that exceed the set threshold range (such as more than 0.5°C). Pack the processed data into batch data packets according to a fixed time period (such as every minute) to optimize transmission efficiency. Support 4G / 5G communication protocols to ensure high-speed and stable transmission performance. Design an automatic switching mechanism to switch to Wi-Fi or other backup networks when the cellular network signal is weak.

[0029] Encrypt data during transmission (such as TLS / SSL protocol) to prevent tampering or theft. Use verification algorithms (such as CRC or MD5) to verify the integrity of transmitted data to ensure that the uploaded data is correct. Electromagnetic signal and temperature deviation data are uploaded at a frequency of seconds to ensure real-time monitoring in the cloud. For historical data or low-priority data, upload it in minutes or longer time periods to reduce the communication burden.

[0030] Use standardized interfaces (such as RESTful API) to connect to the cloud platform to ensure that data is successfully uploaded and received. Upload log and status monitoring: record the timestamp, data packet size and upload status (success or failure) of each data upload. Automatically retry when the upload fails, or temporarily store locally and wait for the network to recover before uploading again.

[0031] In case of network interruption, temporarily store the unuploaded data in local storage (such as SD card or built-in flash memory) to prevent data loss. After the network is restored, upload the unsent data in batches in chronological order. When the network fails or the cloud is unavailable, enable edge computing devices to analyze the data locally and generate temporary alarms or adjust policies. Regularly check the operating status of the cellular module to detect the communication signal strength and module health status.

[0032] Comprehensive analysis module: extract the abnormal characteristics of spectrum interference intensity in the preprocessed electromagnetic signal strength data and the temperature difference fluctuation characteristics in the temperature deviation data, and evaluate the influence of electromagnetic interference on the accuracy of sensor data based on the extracted abnormal characteristics of spectrum interference intensity and temperature difference fluctuation characteristics.

[0033] Extract the abnormal characteristics of spectrum interference intensity in the electromagnetic signal strength data after preprocessing, specifically: Strength mean feature: Calculate the average value of electromagnetic signal strength during the monitoring period to measure the overall interference level. A higher mean value indicates that there is a continuous source of interference in the environment. Peak intensity feature: Identify the maximum value of signal strength in the spectrum and the corresponding frequency band position, which can reveal the frequency range of the main interference source, such as device operation or external communication signals. Spectrum distribution feature: Analyze the distribution of interference signals in different frequency bands, record the interference concentrated frequency band and signal width, and help locate specific interference devices or sources. Volatility feature: Identify the stability of the interference signal by monitoring the amplitude of signal strength changes over time. Sharp fluctuations usually indicate intermittent interference, while steady changes may be related to continuously running equipment. Interference event density feature: Count the number of times the interference intensity exceeds the set threshold to quantify the frequency of interference events.

[0034] Extract the temperature difference fluctuation characteristics in the temperature deviation data after preprocessing, specifically: Average deviation feature: Calculate the average value of the temperature deviation between multiple sensors to evaluate the overall consistency. A large average deviation may point to data distortion or interference. Maximum deviation feature: Extract the maximum temperature deviation within the time period to identify serious abnormal events, such as single sensor failure or electromagnetic interference peak. Fluctuation amplitude feature: Analyze the upper and lower fluctuation range of the temperature difference data. A large fluctuation amplitude may indicate that the system is greatly affected by external interference. Deviation trend feature: Observe the trend of temperature difference over time. Continuous rising or falling deviations may indicate system failure or the long-term impact of interference sources. Abnormal deviation event feature: Count the number of abnormal events where the deviation exceeds the set safety range to help identify potential problem areas in the temperature control system.

[0035] After analyzing the abnormal characteristics of the spectrum interference intensity in the extracted electromagnetic signal strength data, a spectrum interference intensity abnormality index is generated. The spectrum interference intensity abnormality index is obtained as follows: Suppose the spectrum signal data is X, with a dimension of n×m, where n is the number of samples (the number of sampling time points), m is the number of features (the signal strength of different frequency bands of the spectrum distribution), is the signal strength of the i-th sample in the j-th frequency band. In order to eliminate the dimensional difference of signal strength in different frequency bands, X is first standardized, and the expression is: ; In the formula, is the standardized data, is the mean signal strength of the jth frequency band, is the standard deviation of the signal strength in the jth frequency band, and the standardized matrix Z (dimension is n×m) is obtained. The covariance matrix C of the standardized matrix Z is calculated as follows: ; Where C is the m×m covariance matrix, each element represents the linear correlation between frequency band j and frequency band k, T is the matrix transpose, and the covariance matrix C is decomposed by eigenvalue to obtain: ; In the formula, is the kth eigenvalue, indicating the variance contribution rate of the principal component, is the kth eigenvector, corresponding to the direction of the principal component. Arrange the eigenvalues ​​in descending order, take the first p largest eigenvalues ​​and the corresponding eigenvectors to form the principal component subspace, and project the standardized data Z into the principal component space: ; Where Y is the principal component matrix after projection, with a dimension of n×p, It is a matrix composed of the first p eigenvectors with a dimension of m×p. The principal component matrix Y captures the main change pattern of the original data. The original data is reconstructed using the projected principal component data Y: ; In the formula, The dimension of the reconstructed standardized data is n×m, and the reconstruction error of each sample is calculated: ; In the formula, is the reconstruction error of the i-th sample, according to the reconstruction error Calculate the spectrum interference intensity anomaly index, the expression is: ; In the formula, is the mean of all sample reconstruction errors, std(E) is the standard deviation of all sample reconstruction errors, and HKS is the spectrum interference intensity anomaly index.

[0036] When the spectrum interference intensity anomaly index is large, it usually indicates that the spectrum signal of a certain sample deviates greatly from the normal distribution predicted by the principal component model, which means that there is strong electromagnetic interference. A large anomaly index indicates that the interference signal is strong, volatile, or distributed in key frequency bands (such as sensor communication bands), which has a significant impact on the communication link or sampling circuit of the sensor data. In this case, the sensor data may have a large deviation or even fail, which directly affects the accuracy and reliability of the monitoring system.

[0037] When the spectrum interference intensity anomaly index is small, it means that the spectrum characteristics of the sample are close to the normal range predicted by the principal component model, and the intensity and distribution of the interference signal have little impact on the sensor data. A small anomaly index indicates that the electromagnetic interference level in the environment is low and stable, the sensor can operate under normal conditions, and the accuracy and consistency of data collection are basically not disturbed. In this case, the system can rely on sensor data for accurate status monitoring and decision-making.

[0038] The temperature difference fluctuation characteristics in the extracted temperature deviation data are analyzed to generate a temperature difference fluctuation index. The method for obtaining the temperature difference fluctuation index is as follows: Assume that the temperature difference data is T(t), which is defined as a one-dimensional time series with a length of N and a sampling interval of Δt. ; Use Fourier transform to convert the time series T(t) into frequency domain representation F(f), the expression is: ; In the formula, For frequency The complex value of contains amplitude and phase information, j is the imaginary unit, is the kth frequency component, in Hz, from Extracting the magnitude (amplitude) spectrum , the expression is: ; In the formula, for The real and imaginary parts of Indicates the contribution intensity of the frequency component to the temperature difference fluctuation. Extract the main frequency characteristics and calculate the total spectrum energy of the temperature difference sequence , the expression is: ; It reflects the overall intensity of the entire temperature difference fluctuation and extracts high-frequency components (such as frequency components) , the expression is: ; In the formula, is the threshold frequency of the high-frequency component, which is used to distinguish between stable fluctuations and violent fluctuations, and calculate the proportion of high-frequency energy in the total energy , the expression is: ; In the formula, The proportion of violent fluctuations in the temperature difference data is quantified, and the temperature difference fluctuation index CHQ is calculated as the normalized form of the high-frequency energy proportion, and the expression is: ; In the formula, GHQ is the temperature fluctuation index, , is the mean and standard deviation of the high-frequency energy proportion, which is used for normalization.

[0039] When the temperature difference fluctuation index is larger, it indicates that the temperature difference fluctuation between temperature sensors is significantly enhanced, especially the proportion of high-frequency components increases, showing short-term and drastic fluctuations. This may be due to the instability of sensor data collection caused by strong electromagnetic interference, such as instantaneous signal distortion or reading jump, which makes the sensor unable to accurately reflect the actual temperature conditions in the cargo box. When the fluctuation index is large, the accuracy of sensor data may drop significantly, which may lead to misjudgment of temperature control equipment, thus affecting the safety of goods.

[0040] When the temperature difference fluctuation index is smaller, it indicates that the temperature difference fluctuation between temperature sensors is relatively stable, and the proportion of high-frequency components of the spectrum is low, indicating that the impact of electromagnetic interference on the sensor is limited. The low fluctuation index reflects that the sensor works under good environmental conditions and the collected data has high stability and consistency. At this time, the sensor data has high accuracy and can provide a reliable reference for the temperature control system to ensure the normal operation of cargo status monitoring and control.

[0041] The spectrum interference intensity anomaly index and the temperature difference fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the impact value label of electromagnetic interference on sensor data accuracy as the prediction target, and takes minimizing the sum of prediction errors of all electromagnetic interference impact value labels on sensor data accuracy as the training target. The machine learning model is trained until the sum of prediction errors converges and the model training is stopped. The impact value of electromagnetic interference on sensor data accuracy is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0042] The method for obtaining the influence degree of electromagnetic interference on the sensor data accuracy is as follows: from the comprehensive feature vector training data of the trained machine learning model, the corresponding function expression is obtained: ; In the formula, is the output function of the model, HKS is the spectrum interference intensity anomaly index, GHQ is the temperature difference fluctuation index, is the impact degree of electromagnetic interference on sensor data accuracy.

[0043] Interference classification module: used to classify interference events into instantaneous interference and continuous interference according to the degree of impact of electromagnetic interference on sensor data accuracy.

[0044] The acquired electromagnetic interference impact value on the sensor data accuracy is compared with the impact value reference threshold preset according to historical data. If the electromagnetic interference impact value on the sensor data accuracy is greater than or equal to the impact value reference threshold, it means that the electromagnetic interference has a high impact on the sensor data accuracy. In this case, a high interference degree signal is generated and the interference event is classified as continuous interference. If the electromagnetic interference impact value on the sensor data accuracy is less than the impact value reference threshold, it means that the electromagnetic interference has a low impact on the sensor data accuracy. In this case, a low interference degree signal is generated and the interference event is classified as instantaneous interference.

[0045] Early warning processing module: For instantaneous interference events, the delay triggers the adjustment of temperature control equipment and sends a low-level early warning notification to the management personnel; for continuous interference events, further analysis is conducted on the impact of electromagnetic interference on sensor data accuracy within a fixed time period, and continuous interference events are graded according to the analysis results.

[0046] For instantaneous interference events, the temperature control equipment is adjusted with a delay, and a low-level early warning notification is sent to the management personnel. Specifically, the sensor temperature data and electromagnetic interference signal strength are collected in real time. Interference events are identified by setting instantaneous interference thresholds (such as the amplitude of electromagnetic intensity change or the amplitude of sensor reading jump). Characteristics of instantaneous interference: short duration (such as <10 seconds). The temperature difference fluctuation index or spectrum interference anomaly index increases instantaneously, but there is no continuous trend.

[0047] After a transient interference event occurs, a buffer time (such as 5-10 seconds) is set, during which the system does not trigger the adjustment operation of the temperature control equipment. If the interference disappears within the buffer time (the temperature difference fluctuation index returns to the normal range), there is no need to adjust the equipment. If the temperature data returns to normal after the interference ends, the system ignores the event and continues to run. If the interference persists or multiple transient interferences occur within the buffer time, a higher-level processing mechanism is triggered (such as entering the moderate interference processing process). Even if the interference is not completely over, only low-level adjustments are allowed to the temperature control equipment after the buffer time (such as adjusting the refrigeration power in a small range) to avoid frequent switching that causes equipment overload or excessive fluctuations in cargo temperature.

[0048] When a transient interference event occurs, the interference data is recorded, including: timestamp interference intensity (such as spectrum intensity anomaly index), sensor fluctuation amplitude (such as temperature difference fluctuation index), and a low-priority warning is generated to remind management personnel that transient interference exists, but it does not pose a significant risk to equipment and goods. A low-level warning notification is sent to the management system or mobile terminal, displaying the following information: Event type: transient interference event. Current status: The temperature control device has not triggered adjustment, and the system monitoring is normal. Recommended measures: There is no need for emergency intervention, only subsequent attention to the interference trend is recommended. Warning classification: If multiple consecutive transient interference events occur in a concentrated manner (such as 5 times within 1 hour), it will automatically be upgraded to a medium-priority warning, and management personnel are recommended to investigate the source of the interference.

[0049] For continuous interference events, further analysis is conducted on the impact of electromagnetic interference on sensor data accuracy within a fixed time period, and continuous interference events are graded based on the analysis results, specifically: For continuous interference events, that is, the impact degree of electromagnetic interference generated within a fixed time period on the sensor data accuracy is greater than or equal to the impact degree value reference threshold, the impact degree values ​​of electromagnetic interference generated within a subsequent fixed time period that are greater than or equal to the impact degree value reference threshold on the sensor data accuracy are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and the data set is analyzed. Based on the analysis results, the continuous interference events are graded.

[0050] If the mean of the impact values ​​in the data set is greater than or equal to the reference threshold of the mean of the impact values, and the standard deviation of the impact values ​​is less than the reference threshold of the standard deviation of the impact values, the interference level is high and stable. This means that the continuous interference is strong, but the impact fluctuation is small, and the interference environment is relatively stable. At this time, a first-level warning signal is generated, and the interference source needs to be checked first to try to reduce or shield the interference. Enable redundant data or protection mode in the system to ensure the reliability of sensor data.

[0051] If the mean of the impact value is greater than or equal to the reference threshold of the mean of the impact value, and the standard deviation of the impact value is greater than or equal to the reference threshold of the standard deviation of the impact value, the interference level is high and the fluctuation is large. This indicates that the continuous interference is strong and there are significant fluctuations, which may be caused by the superposition of multiple sources of interference or equipment instability. At this time, a secondary warning signal is generated to monitor the trend of interference fluctuations and appropriately delay system adjustment decisions. Optimize sensor data processing algorithms (such as weighted averaging or filtering) to reduce the impact of fluctuations.

[0052] If the mean of the impact value is less than the reference threshold of the mean of the impact value, and the standard deviation of the impact value is greater than or equal to the reference threshold of the standard deviation of the impact value, the interference level is low but the fluctuation is large. This means that the overall impact of the interference is small, but there is instability, which may be sporadic interference or short-term equipment problems. At this time, a third-level warning signal is generated, and continued monitoring is required without immediate intervention. Record fluctuation events for subsequent environment optimization or equipment fault diagnosis.

[0053] If the mean of the impact value is less than the reference threshold of the mean of the impact value, and the standard deviation of the impact value is less than the reference threshold of the standard deviation of the impact value, the interference level is low and the fluctuation is small. This means that the interference intensity is small and stable, and the impact on the system can be ignored. At this time, no early warning is required, the system maintains normal operation, and no additional adjustment measures are taken. Only data is recorded as a reference for long-term monitoring.

[0054] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding handling measures according to different warning signal levels.

[0055] In this embodiment, intelligent management of electromagnetic interference and temperature monitoring in the cargo box is achieved through multi-module collaboration: the electromagnetic interference monitoring module installs multiple monitors to record the electromagnetic signal strength in each frequency band in real time; the redundant sensor module uses at least two temperature sensors to collect temperature data and calculate the temperature deviation; the data processing and transmission module pre-processes the electromagnetic signal strength data and temperature deviation data, and uploads them to the cloud platform through the cellular network; the comprehensive analysis module extracts the abnormal characteristics of the spectrum interference intensity and the temperature difference fluctuation characteristics, and evaluates the impact of electromagnetic interference on the accuracy of sensor data; the interference classification module classifies interference events into instantaneous interference or continuous interference; the early warning processing module implements hierarchical processing according to the interference type, delays the triggering of temperature control equipment adjustment for instantaneous interference and sends a low-level early warning notification, and performs hierarchical processing after further analysis of continuous interference, providing management personnel with corresponding adjustment strategies and early warning information.

[0056] 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 in the formula are set by technicians in this field according to actual conditions.

[0057] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can 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 can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can 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 can 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 can be a solid-state hard disk.

[0058] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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.

Claims

1. A cargo transportation status monitoring system based on the Internet of Things, characterized by: It includes electromagnetic interference monitoring module, redundant sensor module, data processing and transmission module, comprehensive analysis module, interference division module and early warning processing module; Electromagnetic interference monitoring module: multiple electromagnetic interference monitors are installed in the cargo box to record the electromagnetic signal strength data in several frequency bands in real time; Redundant sensor module: includes at least two temperature sensors, which are used to simultaneously collect temperature data in the cargo box and calculate temperature deviation data between the temperature sensors in several time periods; Data processing and transmission module: used to pre-process electromagnetic signal strength data and temperature deviation data, and upload the processed data to the cloud platform through the cellular network; Comprehensive analysis module: extract the abnormal characteristics of spectrum interference intensity in the pre-processed electromagnetic signal strength data and the temperature difference fluctuation characteristics in the temperature deviation data, and evaluate the influence of electromagnetic interference on the accuracy of sensor data based on the extracted abnormal characteristics of spectrum interference intensity and temperature difference fluctuation characteristics; Interference classification module: used to classify interference events into instantaneous interference and continuous interference according to the degree of influence of electromagnetic interference on sensor data accuracy; Early warning processing module: For instantaneous interference events, the delay triggers the adjustment of temperature control equipment and sends a low-level early warning notification to the management personnel; for continuous interference events, further analysis is conducted on the impact of electromagnetic interference on sensor data accuracy within a fixed time period, and continuous interference events are graded according to the analysis results.

2. The cargo transportation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: In the comprehensive analysis module, the spectrum interference intensity anomaly characteristics in the extracted electromagnetic signal strength data are analyzed to generate a spectrum interference intensity anomaly index. The spectrum interference intensity anomaly index is obtained as follows: Suppose the spectrum signal data is X, with a dimension of n×m, where n is the number of samples and m is the number of features. is the signal strength of the i-th sample in the j-th frequency band. First, X is standardized and the expression is: ; In the formula, is the standardized data, is the mean signal strength of the jth frequency band, is the standard deviation of the signal strength in the jth frequency band, and the standardized matrix Z is obtained. The covariance matrix C of the standardized matrix Z is calculated, and the expression is: ; Where C is the m×m covariance matrix, each element represents the linear correlation between frequency band j and frequency band k, T is the matrix transpose, and the covariance matrix C is decomposed by eigenvalue to obtain: ; In the formula, is the kth eigenvalue, indicating the variance contribution rate of the principal component, is the kth eigenvector, corresponding to the direction of the principal component, the eigenvalues ​​are arranged in descending order, the first p largest eigenvalues ​​and the corresponding eigenvectors are taken to form the principal component subspace, and the standardized data Z is projected into the principal component space: ; Where Y is the principal component matrix after projection, with a dimension of n×p, is a matrix composed of the first p eigenvectors with a dimension of m×p. The original data is reconstructed using the projected principal component data Y: ; In the formula, The dimension of the reconstructed standardized data is n×m, and the reconstruction error of each sample is calculated: ; In the formula, is the reconstruction error of the i-th sample, according to the reconstruction error Calculate the spectrum interference intensity anomaly index, the expression is: ; In the formula, is the mean of all sample reconstruction errors, std(E) is the standard deviation of all sample reconstruction errors, and HKS is the spectrum interference intensity anomaly index.

3. The cargo transportation status monitoring system based on the Internet of Things according to claim 2 is characterized in that: In the comprehensive analysis module, the temperature difference fluctuation characteristics in the extracted temperature deviation data are analyzed to generate a temperature difference fluctuation index. The method for obtaining the temperature difference fluctuation index is as follows: Assume that the temperature difference data is T(t), which is defined as a one-dimensional time series with a length of N and a sampling interval of Δt. ; Use Fourier transform to convert the time series T(t) into frequency domain representation F(f), the expression is: ; In the formula, For frequency The complex value of , j is the imaginary unit, is the kth frequency component, from Extracting the Amplitude Spectrum , the expression is: ; In the formula, for The real and imaginary parts of Indicates the contribution intensity of the frequency component to the temperature difference fluctuation, extracts the main frequency characteristics, and calculates the total spectrum energy of the temperature difference sequence , the expression is: ; Reflects the overall intensity of the entire temperature difference fluctuation and extracts the energy of the high-frequency components , the expression is: ; In the formula, is the threshold frequency of the high-frequency component, and calculates the proportion of high-frequency energy in the total energy , the expression is: ; In the formula, The proportion of violent fluctuations in the temperature difference data is quantified, and the temperature difference fluctuation index CHQ is calculated as the normalized form of the high-frequency energy proportion, and the expression is: ; In the formula, GHQ is the temperature fluctuation index, , is the mean and standard deviation of the high-frequency energy proportion, which is used for normalization.

4. The cargo transportation status monitoring system based on the Internet of Things according to claim 3 is characterized in that: In the comprehensive analysis module, the spectrum interference intensity anomaly index and the temperature difference fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the impact value label of electromagnetic interference on sensor data accuracy as the prediction target, and takes minimizing the sum of prediction errors of all electromagnetic interference impact value labels on sensor data accuracy as the training target. The machine learning model is trained until the sum of prediction errors converges and the model training is stopped. The impact value of electromagnetic interference on sensor data accuracy is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

5. The cargo transportation status monitoring system based on the Internet of Things according to claim 4 is characterized in that: The interference classification module is used to classify interference events into instantaneous interference and continuous interference according to the degree of influence of electromagnetic interference on sensor data accuracy. Specifically: The acquired electromagnetic interference impact value on the sensor data accuracy is compared with the impact value reference threshold preset according to historical data. If the electromagnetic interference impact value on the sensor data accuracy is greater than or equal to the impact value reference threshold, it means that the electromagnetic interference has a high impact on the sensor data accuracy. In this case, a high interference degree signal is generated and the interference event is classified as continuous interference. If the electromagnetic interference impact value on the sensor data accuracy is less than the impact value reference threshold, it means that the electromagnetic interference has a low impact on the sensor data accuracy. In this case, a low interference degree signal is generated and the interference event is classified as instantaneous interference.

6. The cargo transportation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: In the early warning processing module, for continuous interference events, the impact of electromagnetic interference on sensor data accuracy within a fixed time period is further analyzed, and continuous interference events are graded according to the analysis results, specifically: For continuous interference events, that is, the impact degree of electromagnetic interference generated within a fixed time period on the sensor data accuracy is greater than or equal to the impact degree value reference threshold, the impact degree values ​​of electromagnetic interference generated within a subsequent fixed time period that are greater than or equal to the impact degree value reference threshold on the sensor data accuracy are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and the data set is analyzed. Based on the analysis results, the continuous interference events are graded.

7. The cargo transportation status monitoring system based on the Internet of Things according to claim 6 is characterized in that: If the mean of the impact values ​​in the data set is greater than or equal to the reference threshold of the mean of the impact values, and the standard deviation of the impact values ​​is less than the reference threshold of the standard deviation of the impact values, the interference level is high and stable. At this time, a first-level warning signal is generated, and the interference source needs to be checked first, and redundant data or protection mode is enabled in the system; If the mean of the impact value is greater than or equal to the reference threshold of the mean of the impact value, and the standard deviation of the impact value is greater than or equal to the reference threshold of the standard deviation of the impact value, the interference level is high and the fluctuation is large. At this time, a secondary warning signal is generated to monitor the interference fluctuation trend, delay system adjustment decisions, and optimize the sensor data processing algorithm; If the mean of the impact value is less than the reference threshold of the mean of the impact value, and the standard deviation of the impact value is greater than or equal to the reference threshold of the standard deviation of the impact value, the interference level is low but the fluctuation is large. At this time, a level 3 warning signal is generated and monitoring needs to be continued without immediate intervention. If the mean of the impact values ​​is less than the reference threshold of the mean of the impact values, and the standard deviation of the impact values ​​is less than the reference threshold of the standard deviation of the impact values, the interference level is low and the fluctuation is small. At this time, no warning is required, the system maintains normal operation, and no additional adjustment measures are taken.

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