An in-transit monitoring and management method and system for logistics transportation

Abstract

The present application relates to the technical field of data processing, more particularly, the present application relates to a kind of in-transit monitoring management method and system for logistics transportation, the method comprises: the mean of adjacent data of any data in vehicle speed data sequence is as the ideal value of the data, the noise intensity of the data is calculated and corrected, noise intensity represents the difference between the data and ideal value, from the adjacent data of the preset filter window of the data, the data with smaller noise degree after correction is added to filter window, to obtain the first updated filter window, and repeat the update process based on the updated filter window, calculate the preferred degree of each updated filter window, to use the preferred degree to meet the filter window of preset screening condition to the data mean filtering, and based on the filtered data monitoring transportation process.The present application guarantees the accuracy of filtered vehicle speed data, and can realize accurate monitoring of logistics transportation process.

Description

Technical Field

[0001] This invention relates to the field of data processing technology. More specifically, this invention relates to a method and system for in-transit monitoring and management of logistics transportation. Background Technology

[0002] In logistics transportation, transportation safety and process monitoring have become important development trends, and monitoring the speed of logistics vehicles is a key aspect. Real-time monitoring of vehicle speed allows for a direct understanding of the progress of transportation tasks and prediction of vehicle arrival times. Furthermore, real-time monitoring can detect speed anomalies, thereby ensuring transportation safety. To accurately monitor the logistics transportation process, the collected vehicle speed data needs to be cleaned. Mean filtering, as a commonly used and simple effective filtering method, can effectively process the vehicle speed data collected during logistics transportation, achieving good data cleaning results.

[0003] In related technologies, such as the patent application with publication number CN116643951A, a method for monitoring and collecting big data in cold chain logistics transportation is disclosed. The method includes: transforming the initial temperature data sequence and the temperature data sequence into a two-dimensional coordinate system to obtain a first temperature anomaly reference value and a second temperature anomaly reference value for each collection cycle; obtaining the collection cycle in which temperature anomalies exist based on the first temperature anomaly reference value and the second temperature anomaly reference value; thereby obtaining the sensor in which anomalies exist; and adjusting and replacing it.

[0004] In related technologies, the impact of noise is not fully considered when identifying abnormal data. This may lead to inaccuracies in the accuracy assessment of abnormal sensors, making it impossible to correctly replace them. Consequently, the accuracy of data collected based on the replaced sensors is lower, thus affecting the monitoring effectiveness of the logistics and transportation process. Summary of the Invention

[0005] To address the problem of inaccurate monitoring of the logistics transportation process due to low data accuracy, this invention provides a method and system for in-transit monitoring and management of logistics transportation.

[0006] According to a first aspect of the present invention, a method for monitoring and managing logistics transportation in transit is provided, comprising:

[0007] Obtain vehicle speed data sequences during logistics transportation;

[0008] The average of the neighboring data of any data in the vehicle speed data sequence is taken as the ideal value of the data. The noise intensity of the data is calculated. The noise intensity represents the difference between the data and the ideal value. The noise intensity is corrected by using the average noise intensity of the data in the preset neighborhood range of the data. The correction value is the normalized value of the product of the average noise intensity and the noise intensity.

[0009] From the adjacent data of the preset filtering window of the data, the data with lower noise level after correction is added to the filtering window to obtain the first updated filtering window. The update process is repeated based on the updated filtering window. The optimization degree of the filtering window after each update is calculated. The data is then mean filtered using the filtering window whose optimization degree meets the preset screening conditions, and the transportation process is monitored based on the filtered data.

[0010] Preferredness: ; For the first The optimality of the filter window after the next update; For the first The noise intensity after data correction; For the first In the filtered window after the second update, except for the first The average of the corrected noise intensity of all data except for the given data.

[0011] This invention can adaptively adjust the position and size of the filter window when performing mean filtering on vehicle speed data. This avoids the under-filtering and filtered waves that occur when using a filter window with a fixed position and size in traditional mean filtering. It ensures the accuracy of the filtered vehicle speed data, thereby enabling accurate monitoring of the logistics transportation process based on highly accurate vehicle speed data.

[0012] Preferably, the noise intensity of the data is calculated, including:

[0013] The difference between any data point in the vehicle speed data sequence and its ideal value is considered as the mutability of the data.

[0014] The confidence level of abrupt change is calculated using the standard deviation of data within the neighborhood of the data. The confidence level is negatively correlated with the standard deviation. The confidence level is used as a weight to weight the abrupt change and obtain the noise intensity of the data.

[0015] This invention can eliminate the influence of normal fluctuations, which cause normal data to behave similarly to noise changes in a local area.

[0016] Preferred methods for obtaining differences include:

[0017] Calculate the absolute value of the difference between any data point in the vehicle speed data sequence and its ideal value to obtain the difference between the data point and its ideal value.

[0018] This invention can accurately assess the degree to which each data point in a vehicle speed data sequence deviates from the normal trend of change within a local range, thereby obtaining the abrupt change of the corresponding data within a local range.

[0019] The preferred reliability satisfies the following relationship:

[0020] ;

[0021] In the formula, For the first The reliability of the mutability of individual data; For the first The standard deviation of data within the neighborhood of each data point; It is a natural exponential function.

[0022] Preferred methods for obtaining the neighborhood range include:

[0023] Taking any data point in the vehicle speed data sequence as the center, select a data segment containing a preset number of data points to obtain the neighborhood range of that data point.

[0024] Preferably, the noise intensity is corrected using the average noise intensity of data within a preset neighborhood range of the data, satisfying the following relationship:

[0025] ;

[0026] In the formula, For the first The noise intensity after data correction; For the first Noise intensity of each data point; For the first Within the neighborhood of the data, the first Noise intensity of each data point; For the first The amount of data within the neighborhood of each data point; It is the hyperbolic tangent function.

[0027] This invention can eliminate the possibility that data may have high noise levels due to sudden braking or acceleration of the vehicle, thereby enabling accurate assessment of the likelihood that each data point is noise.

[0028] Preferably, the mean filter is applied to any data using a filter window whose preference meets the preset screening criteria, including:

[0029] The filter window that meets the preset screening criteria is used as the target window. The average value of all data in the target window is used to replace any data to perform mean filtering on any data.

[0030] This invention avoids the filtering and under-filtering issues that occur when performing mean filtering on vehicle speed data, thus ensuring the accuracy of the filtered vehicle speed data.

[0031] According to a second aspect of the present invention, an in-transit monitoring and management system for logistics transportation is provided, the system including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the first aspect of the present invention.

[0032] The present invention has the following effects:

[0033] 1. When performing mean filtering on vehicle speed data, this invention can adaptively adjust the position and size of the filtering window, which can avoid under-filtering and filtering wave phenomena that exist when performing mean filtering on vehicle speed data, ensuring the accuracy of the filtered vehicle speed data, thereby achieving accurate monitoring of the logistics transportation process.

[0034] 2. The optimization degree of the updated filter window calculated by the present invention can accurately evaluate the filtering effect when the corresponding data is mean filtered with the data in the corresponding filter window, so as to accurately select the best filter window when mean filtering each data, and ensure the accuracy of the adaptive filter window.

[0035] 3. This invention integrates data from multiple sources to calculate the corrected noise intensity of each data point in the vehicle speed data sequence, and can accurately assess the possibility that the vehicle speed data is noise. Attached Figure Description

[0036] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0037] Figure 1 This is a flowchart illustrating the steps of an embodiment of the present invention for an in-transit monitoring and management method for logistics transportation. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0040] Reference Figure 1 A method for in-transit monitoring and management of logistics transportation, comprising steps S1-S4, as detailed below:

[0041] S1: Obtain vehicle speed data sequences during logistics transportation.

[0042] Specifically, a vehicle speed sensor can be installed at a suitable location on the vehicle. Then, during the logistics transportation process, the readings of the vehicle speed sensor can be collected at a certain frequency, such as 1Hz, to obtain a sequence of vehicle speed data during the logistics transportation process. This embodiment does not impose any special limitation on the data collection frequency.

[0043] S2: Take the mean of the adjacent data of any data in the vehicle speed data sequence as the ideal value of the data, and calculate the noise intensity of the data. The noise intensity represents the difference between the data and the ideal value.

[0044] It should be noted that during logistics transportation, vehicles are affected by road conditions and may experience bumps. When a vehicle experiences bumps, the speed sensor may be subjected to physical impact, resulting in noise in the collected speed data. Noise typically manifests as abrupt changes within a local range. Determining the ideal value of the data through linear interpolation can reflect the expected value of the corresponding data or the predicted local trend of change. Therefore, this invention, by calculating the difference between the actual value and the ideal value of the corresponding data, can assess the degree to which the corresponding data deviates from the normal trend of change within a local range, thereby obtaining the noise intensity of the corresponding data.

[0045] In one exemplary embodiment of the present invention, the noise intensity of any data point in the vehicle speed data sequence can be determined through the following steps:

[0046] Step 1: The difference between any data point in the vehicle speed data sequence and its ideal value is taken as the mutability of the data.

[0047] In one exemplary embodiment of the present invention, the difference between any data point in the vehicle speed data sequence and the ideal value of that data can be determined through the following steps:

[0048] Calculate the absolute value of the difference between any data point in the vehicle speed data sequence and its ideal value to obtain the difference between the data point and its ideal value.

[0049] Optionally, the ratio between any data point in the vehicle speed data sequence and the ideal value of that data point can be calculated to obtain the difference between the data point and the ideal value. This embodiment does not impose any particular limitation on the method of determining the difference between the data points.

[0050] Step 2: Calculate the confidence level of the mutability using the standard deviation of the data in the neighborhood of the data. The confidence level is negatively correlated with the standard deviation. Use the confidence level as a weight to weight the mutability and obtain the noise intensity of the data.

[0051] Specifically, the reliability of the abrupt change of any data point in the vehicle speed data sequence satisfies the following relationship:

[0052] ;

[0053] In the formula, For the first The reliability of the mutability of individual data; For the first The standard deviation of data within the neighborhood of each data point; Let be the natural exponential function, where the natural exponential function is defined by the natural constant. An exponential function with base 0.

[0054] Among them, when When the value is small, it indicates that the data fluctuations within the neighborhood are relatively stable. Therefore, if the data shows a large variability, the cause of this variability is more likely to be noise, and the reliability of the variability is relatively high. Conversely, when... A large value indicates that the data fluctuations within the neighborhood of the data are relatively drastic. Therefore, when it is determined that the data has a large variability, the reason for the large variability is more likely to be normal data fluctuation, and the reliability of the variability of the data is relatively low.

[0055] In another embodiment, the negative or reciprocal of the standard deviation of data within the neighborhood of any given data can be used as the confidence level of the data's mutability.

[0056] Furthermore, once the mutability of any data point and the reliability of that mutability are determined, the noise intensity of that data can be calculated. Specifically, the noise intensity of any data point in the vehicle speed data sequence satisfies the following relationship:

[0057] ;

[0058] In the formula, For the first The noise level of each data point; , as well as The first The value of the first data point, the first The value of the data and the first data +1 data value; For the first The standard deviation of data within the neighborhood of each data point; It is the absolute value symbol.

[0059] in, Reflects the first The ideal value for each data point; Reflects the first The larger the difference between the true and ideal values ​​of a data point, the greater the variability of the data and the greater the corresponding noise intensity. Reflects the first The higher the value, the greater the reliability of the mutability of the data.

[0060] In one exemplary embodiment of the present invention, the neighborhood range of any data in the vehicle speed data sequence can be determined through the following steps:

[0061] Taking any data point in the vehicle speed data sequence as the center, select a data segment containing a preset number of data points to obtain the neighborhood range of that data point.

[0062] Optionally, the preset value can be recorded as When the amount of data on either side of any data point in the vehicle speed data sequence is less than If so, discard the data from that side and combine it with the data from the other side. A data segment consisting of several data points is used as the neighborhood range of that data. In this embodiment... =3, This embodiment does not impose any special limitation on the amount of data contained within the neighborhood range.

[0063] S3: Using the average noise intensity of the data within the preset neighborhood range, the noise intensity is corrected. The correction value is the normalized value of the product of the average noise intensity and the noise intensity.

[0064] It should be noted that during logistics transportation, sudden braking or acceleration may occur, leading to significant abrupt changes in vehicle speed data at corresponding moments. This results in normal data containing extremely high noise levels, necessitating correction for the calculated noise intensity. Road conditions causing vehicle bumps can also cause vehicle vibrations, during which speed data is typically subject to continuous noise interference. Furthermore, sudden braking or acceleration causing abrupt changes in speed data is often instantaneous. Therefore, this invention, based on this characteristic, corrects the noise intensity of corresponding data by assessing the overall noise level within a neighborhood range, thereby avoiding excessive noise in normal data and accurately assessing the likelihood of data being noise.

[0065] Specifically, the noise intensity after correction for any data point in the vehicle speed data sequence satisfies the following relationship:

[0066] ;

[0067] In the formula, For the first The noise intensity after data correction; For the first Noise intensity of each data point; For the first Within the neighborhood of the data, the first Noise intensity of each data point; For the first The amount of data within the neighborhood of each data point; It is the hyperbolic tangent function.

[0068] in, Reflects the first The larger the value, the higher the overall noise level of the data in the neighborhood of the data, indicating that the data is more likely to be noise, and the corresponding noise intensity after correction is relatively large.

[0069] Optionally, when the average noise intensity within the neighborhood of any data point is low, it indicates that the overall noise level of the data within that neighborhood is low; if the noise intensity of any data point is high, it indicates that the reason for the high noise intensity of any data point is the sudden braking or acceleration of the vehicle. In this case, it is necessary to reduce the noise intensity of the data point to avoid normal data having a high noise intensity and to ensure that the possibility of each data point in the vehicle speed data sequence being noise can be accurately assessed.

[0070] In another embodiment, it can also be utilized Function, for Normalization is performed, but this implementation does not impose any special restrictions on the normalization method selected.

[0071] S4: From the adjacent data of the preset filtering window of the data, select the data with lower noise level after correction and add it to the filtering window to obtain the first updated filtering window. Repeat the update process based on the updated filtering window, calculate the optimization degree of the filtering window after each update, and use the filtering window with optimization degree that meets the preset screening conditions to perform mean filtering on the data, and monitor the transportation process based on the filtered data.

[0072] It should be noted that mean filtering is a commonly used, simple, and effective filtering method with good results in data cleaning. However, due to the uncertainty of the environment faced by logistics vehicles during operation, the location and intensity of noise often exhibit uncertainty. Traditional mean filtering filters data through a filter window of fixed position and size, which is difficult to adapt to such changes, leading to the possibility of filtered or under-filtered results, affecting the data cleaning effect. Therefore, this invention improves the traditional mean filtering algorithm. Specifically, the improvement involves: pre-setting a filter window for each data point, then iteratively updating the filter window for each data point, and calculating the optimization degree of the filter window after each update. The filtered data is then filtered using filter windows whose optimization degrees meet the preset screening conditions.

[0073] It should be further explained that when using the improved mean filter to filter any data in the vehicle speed data sequence, if the overall noise intensity of the data other than that data in the current filtering window is low, it means that there is a large amount of real data in the current filtering window, and the current filtering window has a relatively high optimization degree. At the same time, if the noise intensity of that data is low, it means that no additional data is needed to filter that data, and the filtering window for that data has a relatively high optimization degree.

[0074] Specifically, the optimality of the filter window after any update satisfies the following relationship:

[0075] ;

[0076] In the formula, For the first The optimality of the filter window after the next update; For the first The noise intensity after data correction; For the first In the filtered window after the second update, except for the first The average of the corrected noise intensity of all data except for the given data.

[0077] Next, taking the first vehicle speed data sequence as an example... Taking filtering data as an example, the filtering process of this invention will be described in detail below:

[0078] First, the first in the preset vehicle speed data sequence The preset filtering window for each data is a window that only contains the corresponding data in this embodiment. Of course, a suitable preset filtering window can also be set according to the specific situation. This embodiment does not make a special limitation on the size of the preset filtering window.

[0079] Then, from the adjacent data of the preset filtering window, the corresponding data with lower corrected noise intensity are selected and added to the preset filtering window to obtain the first updated filtering window. The optimization degree of the first updated filtering window is then calculated and denoted as . ;

[0080] Then, the process of adding data is repeated to obtain the filtered window after each update, and the optimality of the filtered window after each update is calculated, and when Greater than ,at the same time Greater than When, then determine the first The optimality of the updated filter window meets the preset screening conditions, and the optimization of the filter window is achieved using the first... The updated filter window applies the first [number] [item] in the vehicle speed data sequence. Perform mean filtering on the data.

[0081] In one exemplary embodiment of the present invention, mean filtering of data can be achieved through the following steps:

[0082] The filter window that meets the preset screening criteria is used as the target window. The average value of all data in the target window is used to replace any data to perform mean filtering on any data.

[0083] Furthermore, the mean filter can be applied to each data point in the vehicle speed data sequence based on steps S2-S4 to complete data cleaning. Then, the system can determine the arrival time of the vehicle at the corresponding speed based on the cleaned vehicle speed data, thereby monitoring the logistics transportation process. Moreover, based on the cleaned vehicle speed data, the system can determine whether the vehicle is speeding or exhibiting other abnormal behaviors at the corresponding time, and promptly remind the driver to slow down, thereby improving the safety of logistics transportation.

[0084] The present invention also provides an in-transit monitoring and management system for logistics transportation. The system includes a memory and a processor, and the memory stores a computer program. The computer program integrates the function of an in-transit monitoring and management method for logistics transportation. When the computer program is executed, the accuracy of the filtered vehicle speed data is ensured through an in-transit monitoring and management method for logistics transportation, and accurate monitoring of the logistics transportation process can be achieved.

[0085] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0086] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method for in-transit monitoring and management of logistics transportation, characterized in that, include: Obtain vehicle speed data sequences during logistics transportation; The average of the neighboring data of any data in the vehicle speed data sequence is taken as the ideal value of the data. The noise intensity of the data is calculated, and the noise intensity represents the difference between the data and the ideal value. The noise intensity is corrected by using the average noise intensity of the data within a preset neighborhood of the data. The correction value is the normalized value of the product of the average noise intensity and the noise intensity. From the adjacent data of the preset filtering window of the data, the data with lower noise level after correction is added to the filtering window to obtain the first updated filtering window. The update process is repeated based on the updated filtering window. The optimization degree of the filtering window after each update is calculated. The data is mean filtered using the filtering window whose optimization degree meets the preset screening conditions. The transportation process is monitored based on the filtered data. The degree of preference: ; For the first The optimality of the filter window after the next update; For the first The noise intensity after data correction; For the first In the filtered window after the second update, except for the first The average of the corrected noise intensity of all data except for the given data.

2. The method for in-transit monitoring and management of logistics transportation according to claim 1, characterized in that, The calculation of the noise intensity of the data includes: The difference between any data point in the vehicle speed data sequence and its ideal value is taken as the mutability of the data. The confidence level of the mutability is calculated using the standard deviation of data within the neighborhood of the data. The confidence level is negatively correlated with the standard deviation. The confidence level is used as a weight to weight the mutability, thereby obtaining the noise intensity of the data.

3. The method for in-transit monitoring and management of logistics transportation according to claim 2, characterized in that, The method for obtaining the difference includes: Calculate the absolute value of the difference between any data point in the vehicle speed data sequence and the ideal value of that data point to obtain the difference between the data point and the ideal value of that data point.

4. The method for in-transit monitoring and management of logistics transportation according to claim 2, characterized in that, The credibility satisfies the following relationship: ； In the formula, For the first The reliability of the mutability of individual data; For the first The standard deviation of data within the neighborhood of each data point; It is a natural exponential function.

5. A method for in-transit monitoring and management of logistics transportation according to claim 4, characterized in that, The method for obtaining the neighborhood range includes: Taking any data point in the vehicle speed data sequence as the center, select a data segment containing a preset number of data points to obtain the neighborhood range of that data.

6. The method for in-transit monitoring and management of logistics transportation according to claim 1, characterized in that, The noise intensity is corrected by using the average noise intensity of data within a preset neighborhood range of the data, satisfying the following relationship: ； In the formula, For the first The noise intensity after data correction; For the first Noise intensity of each data point; For the first Within the neighborhood of the data, the first Noise intensity of each data point; For the first The amount of data within the neighborhood of each data point; It is the hyperbolic tangent function.

7. The method for in-transit monitoring and management of logistics transportation according to claim 1, characterized in that, The step of using a filtering window that meets preset screening conditions to perform mean filtering on any data includes: The filter window that meets the preset screening criteria is used as the target window. The average value of all data in the target window is used to replace any data to perform mean filtering on any data.

8. A transit monitoring and management system for logistics transportation, characterized in that, The in-transit monitoring and management system for logistics transportation includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the in-transit monitoring and management method for logistics transportation as described in any one of claims 1-7.

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