Freight quality detection method and device based on frame box monitoring data, equipment, storage medium and program product

Abstract

The application relates to a freight quality detection method and device based on frame box monitoring data, computer equipment, a readable storage medium and a program product, and relates to the technical field of computers. The application can improve the scientificity and accuracy of freight quality detection. The method comprises the following steps: acquiring dynamic monitoring data and operation data comprising an on-site cumulative duration and an entry and exit registration number, performing data cleaning on the dynamic monitoring data to obtain environmental temperature data and three-axis gravity acceleration; determining a temperature abnormality cumulative duration according to the environmental temperature data and a temperature threshold condition, and determining an impact abnormality cumulative number according to the three-axis gravity acceleration and an acceleration threshold condition; determining a temperature abnormality rate according to the temperature abnormality cumulative duration and the on-site cumulative duration, and determining an impact abnormality rate according to the impact abnormality cumulative number and the entry and exit registration number; determining a first target grade and a second target grade according to the temperature abnormality rate and the impact abnormality rate, and obtaining a freight quality detection result.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for freight quality inspection based on frame container monitoring data. Background Technology

[0002] Freight quality is a crucial indicator for evaluating how well flat rack container transportation services meet customer needs and expectations, directly impacting customer satisfaction and a company's competitiveness. Freight quality inspection is a vital step in ensuring the safe, accurate, and timely arrival of goods during flat rack container transportation. Through systematic inspection and evaluation, potential problems can be effectively identified and resolved, improving service quality and customer satisfaction.

[0003] Traditional technologies for inspecting the quality of flat rack containers rely heavily on manual visual inspection or single-dimensional data (such as the number of impacts). However, manual visual inspection is easily affected by factors such as the professional knowledge and work experience of the personnel involved, and is highly subjective. On the other hand, using single-dimensional data for quality inspection is rather one-sided and too rough. As a result, both of these inspection methods have low accuracy and cannot effectively control the quality of flat rack containers. Summary of the Invention

[0004] Therefore, it is necessary to provide a freight quality inspection method, apparatus, computer equipment, computer-readable storage medium, and computer program product based on frame container monitoring data to address the above-mentioned technical problems.

[0005] Firstly, this application provides a freight quality inspection method based on frame container monitoring data, including:

[0006] The system acquires dynamic monitoring data and operational data for the target frame box within a unit testing cycle. The dynamic monitoring data is then cleaned to obtain ambient temperature data and triaxial gravitational acceleration. The operational data includes the cumulative on-site time and the number of times the equipment enters and exits the site.

[0007] The cumulative duration of temperature anomalies is determined based on the ambient temperature data and temperature threshold conditions, and the cumulative number of impact anomalies is determined based on the triaxial gravitational acceleration and acceleration threshold conditions.

[0008] The temperature anomaly rate is determined based on the cumulative duration of the temperature anomaly and the cumulative duration of presence, and the impact anomaly rate is determined based on the cumulative number of impact anomalies and the number of entry and exit registrations.

[0009] Based on the temperature anomaly rate and the shock anomaly rate, a first target level and a second target level are determined from multiple candidate freight quality levels, and freight quality detection results are obtained based on the first target level and the second target level.

[0010] In one embodiment, the temperature threshold conditions include a low temperature threshold condition, a high temperature threshold condition, and a temperature change threshold condition; determining the cumulative duration of temperature anomalies based on the ambient temperature data and the temperature threshold conditions includes:

[0011] Based on the ambient temperature data, the low temperature threshold condition, and the high temperature threshold condition, a first cumulative duration of temperature range abnormality is determined; based on the ambient temperature data and the temperature change threshold condition, a second cumulative duration of temperature change rate abnormality is determined; based on the first cumulative duration and the second cumulative duration, the cumulative duration of temperature abnormality is obtained.

[0012] In one embodiment, the acceleration threshold conditions include longitudinal threshold conditions, lateral threshold conditions, and vertical threshold conditions; determining the cumulative number of impact anomalies based on the triaxial gravitational acceleration and acceleration threshold conditions includes:

[0013] Based on the triaxial gravitational acceleration, longitudinal acceleration, lateral acceleration, and vertical acceleration are obtained; the number of longitudinal impact anomalies is determined based on the longitudinal acceleration and the longitudinal threshold condition, the number of lateral impact anomalies is determined based on the lateral acceleration and the lateral threshold condition, and the number of vertical impact anomalies is determined based on the vertical acceleration and the vertical threshold condition; based on the number of longitudinal impact anomalies, the number of lateral impact anomalies, and the number of vertical impact anomalies, the cumulative number of impact anomalies is obtained.

[0014] In one embodiment, the candidate freight quality level includes an excellent level, a good level, a qualified level, and a warning level; before determining the first target level and the second target level from multiple candidate freight quality levels based on the temperature anomaly rate and the shock anomaly rate, the method further includes: determining the indicator threshold ranges corresponding to the excellent level, the good level, the qualified level, and the warning level, respectively.

[0015] The step of determining a first target level and a second target level among multiple candidate freight quality levels based on the temperature anomaly rate and the shock anomaly rate includes: determining the target indicator threshold ranges in which the temperature anomaly rate and the shock anomaly rate are located within multiple indicator threshold ranges, and determining the first target level and the second target level among multiple candidate freight quality levels based on the target indicator threshold ranges.

[0016] In one embodiment, the dynamic monitoring data includes initial ambient temperature data and initial triaxial gravitational acceleration. The step of cleaning the dynamic monitoring data to obtain the ambient temperature data and triaxial gravitational acceleration includes:

[0017] Sensor fault data, interference data, redundant data, and missing data are detected in the initial ambient temperature data and the initial triaxial gravitational acceleration data; the sensor fault data, interference data, and redundant data are removed, and the missing data is filled in to obtain the ambient temperature data and the triaxial gravitational acceleration data.

[0018] In one embodiment, obtaining the freight quality inspection result based on the first target level and the second target level includes:

[0019] If the first target level and the second target level are the same, then the first target level or the second target level is used as the freight quality level; if the first target level and the second target level are different, then the lower of the first target level and the second target level is used as the freight quality level; corresponding early warning information is generated according to the freight quality level, and the freight quality detection result is generated based on the freight quality level and the early warning information.

[0020] Secondly, this application also provides a freight quality inspection device based on frame container monitoring data, comprising:

[0021] The data acquisition module is used to acquire dynamic monitoring data and operational data of the target frame box within a unit detection cycle, and to perform data cleaning on the dynamic monitoring data to obtain ambient temperature data and triaxial gravitational acceleration; the operational data includes the cumulative on-site time and the number of entry and exit registrations;

[0022] An anomaly identification module is used to determine the cumulative duration of temperature anomalies based on the ambient temperature data and temperature threshold conditions, and to determine the cumulative number of impact anomalies based on the triaxial gravitational acceleration and acceleration threshold conditions.

[0023] The numerical calculation module is used to determine the temperature anomaly rate based on the cumulative duration of the temperature anomaly and the cumulative duration of presence, and to determine the impact anomaly rate based on the cumulative number of impact anomalies and the number of entry and exit registrations.

[0024] The grade determination module is used to determine a first target grade and a second target grade from multiple candidate freight quality grades based on the temperature anomaly rate and the shock anomaly rate, and to obtain the freight quality inspection result based on the first target grade and the second target grade.

[0025] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0026] The system acquires dynamic monitoring and operational data for the target frame container within a unit inspection cycle. The dynamic monitoring data is cleaned to obtain ambient temperature data and triaxial gravitational acceleration. The operational data includes cumulative on-site time and the number of entry / exit registrations. Based on the ambient temperature data and temperature threshold conditions, the cumulative duration of temperature anomalies is determined, and based on the triaxial gravitational acceleration and acceleration threshold conditions, the cumulative number of impact anomalies is determined. Based on the cumulative duration of temperature anomalies and the cumulative on-site time, the temperature anomaly rate is determined, and based on the cumulative number of impact anomalies and the number of entry / exit registrations, the impact anomaly rate is determined. Based on the temperature anomaly rate and the impact anomaly rate, a first target level and a second target level are determined from multiple candidate freight quality levels. Based on the first target level and the second target level, the freight quality inspection result is obtained.

[0027] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0028] The system acquires dynamic monitoring and operational data for the target frame container within a unit inspection cycle. The dynamic monitoring data is cleaned to obtain ambient temperature data and triaxial gravitational acceleration. The operational data includes cumulative on-site time and the number of entry / exit registrations. Based on the ambient temperature data and temperature threshold conditions, the cumulative duration of temperature anomalies is determined, and based on the triaxial gravitational acceleration and acceleration threshold conditions, the cumulative number of impact anomalies is determined. Based on the cumulative duration of temperature anomalies and the cumulative on-site time, the temperature anomaly rate is determined, and based on the cumulative number of impact anomalies and the number of entry / exit registrations, the impact anomaly rate is determined. Based on the temperature anomaly rate and the impact anomaly rate, a first target level and a second target level are determined from multiple candidate freight quality levels. Based on the first target level and the second target level, the freight quality inspection result is obtained.

[0029] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0030] The system acquires dynamic monitoring and operational data for the target frame container within a unit inspection cycle. The dynamic monitoring data is cleaned to obtain ambient temperature data and triaxial gravitational acceleration. The operational data includes cumulative on-site time and the number of entry / exit registrations. Based on the ambient temperature data and temperature threshold conditions, the cumulative duration of temperature anomalies is determined, and based on the triaxial gravitational acceleration and acceleration threshold conditions, the cumulative number of impact anomalies is determined. Based on the cumulative duration of temperature anomalies and the cumulative on-site time, the temperature anomaly rate is determined, and based on the cumulative number of impact anomalies and the number of entry / exit registrations, the impact anomaly rate is determined. Based on the temperature anomaly rate and the impact anomaly rate, a first target level and a second target level are determined from multiple candidate freight quality levels. Based on the first target level and the second target level, the freight quality inspection result is obtained.

[0031] The aforementioned freight quality inspection method, apparatus, computer equipment, computer-readable storage medium, and computer program product based on frame container monitoring data first acquire dynamic monitoring data of the target frame container within a unit inspection cycle, including operational data such as cumulative on-site time and entry / exit registration times. The dynamic monitoring data is then cleaned to obtain ambient temperature data and triaxial gravitational acceleration. Next, the cumulative duration of temperature anomalies is determined based on the ambient temperature data and temperature threshold conditions, and the cumulative number of impact anomalies is determined based on triaxial gravitational acceleration and acceleration threshold conditions. Then, the temperature anomaly rate is determined based on the cumulative duration of temperature anomalies and cumulative on-site time, and the impact anomaly rate is determined based on the cumulative number of impact anomalies and entry / exit registration times. Finally, based on the temperature anomaly rate and impact anomaly rate, a first target level and a second target level are determined from multiple candidate freight quality levels, and the freight quality inspection result is obtained based on the first target level and the second target level. This application integrates dynamic monitoring data (ambient temperature and gravitational acceleration) from the Internet of Things (IoT) of the flat rack containers, as well as operational data such as the number of entries and exits and the duration of on-site storage, to accurately quantify the potential damage risks and adverse effects on goods during operations. It also uses computer technology to assist in the objective, quantifiable, and traceable detection of the freight quality of flat rack containers entering and leaving stations and storage yards, thereby improving the scientific nature and accuracy of freight quality detection and providing data support for freight quality control, partner hierarchical management, and process optimization. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is an application environment diagram of a freight quality inspection method based on frame container monitoring data in one embodiment.

[0034] Figure 2 This is a flowchart illustrating a freight quality inspection method based on frame container monitoring data in one embodiment.

[0035] Figure 3 This is a flowchart illustrating the steps for determining the cumulative duration of temperature anomalies in one embodiment;

[0036] Figure 4 This is a flowchart illustrating a freight quality inspection method based on frame container monitoring data in a specific embodiment.

[0037] Figure 5 This is a structural block diagram of a freight quality inspection device based on frame container monitoring data in one embodiment.

[0038] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0040] The freight quality inspection method based on frame container monitoring data provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown illustrates this. In this environment, the terminal can communicate with the server via a network. The data storage system can store the data that the server needs to process. The data storage system can be integrated onto the server or located on the cloud or other network servers. In situations such as... Figure 1 In the application environment shown, the terminal can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0041] In one embodiment, such as Figure 2 As shown, a freight quality inspection method based on frame container monitoring data is provided. This method can be applied to... Figure 1 In the terminal, the method may include the following steps:

[0042] Step S201: Obtain dynamic monitoring data and operational data for the target frame box within the unit detection cycle; perform data cleaning on the dynamic monitoring data to obtain ambient temperature data and triaxial gravitational acceleration; the operational data includes the cumulative on-site time and the number of entry and exit registrations.

[0043] The target frame box can be a frame box equipped with an IoT (Internet of Things) monitoring module, and the dynamic monitoring data can be collected by the IoT monitoring module.

[0044] The dynamic monitoring data includes initial ambient temperature data and initial triaxial gravitational acceleration. The initial ambient temperature data can be obtained by an IoT temperature sensor, and the initial triaxial gravitational acceleration can be obtained by an IoT triaxial acceleration sensor.

[0045] The cumulative on-site time is used to evaluate the cumulative value of the storage time of a single container at the target site during each testing cycle, which can be calculated using the following formula:

[0046]

[0047] In the above formula, The total time spent in the audience. For the first The number of hours of storage on-site each time.

[0048] The number of entry and exit registrations is used to evaluate the sum of the cumulative number of times a single container enters and exits the target site within the evaluation unit's testing cycle. It can be calculated using the following formula:

[0049]

[0050] In the above formula, Number of times to enter and exit the venue This refers to the number of times a single container enters the site. This refers to the number of times a single box leaves the factory.

[0051] It should be noted that in this embodiment, a single container is used as the smallest evaluation unit to ensure the accuracy and traceability of the evaluation. For different application scenarios, a weighted comprehensive score can also be performed based on the scale of the number of containers participating in the evaluation at each site, combined with the statistical values ​​(average, maximum, and minimum values) of the core indicators (temperature anomaly rate and shock anomaly rate), to achieve fair comparison between sites of different sizes.

[0052] Specifically, in response to the received freight quality inspection command, the terminal acquires dynamic monitoring data and operational data for the target frame container within the unit inspection cycle, performs data cleaning on the initial ambient temperature data and initial triaxial gravitational acceleration in the dynamic monitoring data, and obtains preprocessed ambient temperature data and triaxial gravitational acceleration.

[0053] Step S202: Determine the cumulative duration of temperature anomalies based on ambient temperature data and temperature threshold conditions, and determine the cumulative number of impact anomalies based on triaxial gravitational acceleration and acceleration threshold conditions.

[0054] Among them, the temperature threshold conditions must be set strictly in accordance with the standard operating procedures (SOP) for cargo storage, combined with cargo endurance test data, and reasonably set short-term deviations from the tolerance range; the cumulative duration of temperature anomalies can be defined as the cumulative number of hours during which the ambient temperature exceeds the safety threshold range or experiences short-term drastic fluctuations while a single container is stored on-site within a unit testing cycle.

[0055] The setting of acceleration threshold conditions should take into account the cargo fragility value (product technical manual), the International Society for Safe Transport (ISTA) test standards, and the analysis results of historical cargo damage case data. The cumulative number of impact anomalies can be defined as the cumulative number of times a single container experiences impact / vibration acceleration exceeding the safety threshold during the loading and unloading process at the entrance, the loading and unloading process at the exit, and the transfer process within the yard within a unit testing cycle.

[0056] Specifically, the terminal analyzes and calculates based on ambient temperature data and temperature threshold conditions to obtain the cumulative duration of temperature anomalies, and analyzes and calculates based on triaxial gravitational acceleration and acceleration threshold conditions to obtain the cumulative number of impact anomalies.

[0057] Step S203: Determine the temperature anomaly rate based on the cumulative duration of temperature anomalies and the cumulative duration of presence, and determine the impact anomaly rate based on the cumulative number of impact anomalies and the number of entry and exit registrations.

[0058] The temperature anomaly rate reflects the stability of temperature control during storage and quantifies the probability of potential damage to goods due to temperature control system failure or environmental anomalies. The temperature anomaly rate can be calculated using the following formula:

[0059]

[0060] In the above formula, For temperature anomaly rate, This represents the cumulative duration of temperature anomalies. This is the cumulative time spent in the event; it is important to note that it is necessary to ensure... and To ensure consistency across time dimensions, periods with missing data were removed.

[0061] The impact anomaly rate reflects the frequency of violent loading and unloading and improper transfer during each entry and exit operation, and is directly related to the risk of cargo impact damage. The impact anomaly rate can be calculated using the following formula:

[0062]

[0063] In the above formula, To impact the anomaly rate, To accumulate the number of times the impact is abnormal, This records the number of times data is entered and exited; it is important to note that invalid or abnormal data, such as data from sensors affected by magnetic interference, must be removed to ensure accuracy. This represents the number of valid events.

[0064] Specifically, the terminal calculates the temperature anomaly rate based on the cumulative duration of temperature anomalies and the cumulative duration of presence, and calculates the impact anomaly rate based on the cumulative number of impact anomalies and the number of entry and exit registrations.

[0065] Step S204: Based on the temperature anomaly rate and the shock anomaly rate, determine the first target level and the second target level from multiple candidate freight quality levels, and obtain the freight quality inspection results based on the first target level and the second target level.

[0066] The candidate freight quality levels include excellent, good, qualified, and alert; the first target level is the level corresponding to the temperature anomaly rate, and the second target level is the level corresponding to the shock anomaly rate.

[0067] Specifically, the terminal determines the target indicator threshold ranges for temperature anomaly rate and shock anomaly rate within multiple indicator threshold ranges, and determines the first target level and the second target level among multiple candidate freight quality levels based on the target indicator threshold ranges; when the first target level and the second target level are inconsistent, the lower level is used as the final service quality level.

[0068] In this embodiment, dynamic monitoring data of the target frame container within a unit detection cycle, as well as operational data including cumulative on-site time and entry / exit registration times, are first acquired. The dynamic monitoring data is then cleaned to obtain ambient temperature data and triaxial gravitational acceleration. Next, the cumulative duration of temperature anomalies is determined based on the ambient temperature data and temperature threshold conditions, and the cumulative number of impact anomalies is determined based on triaxial gravitational acceleration and acceleration threshold conditions. Then, the temperature anomaly rate is determined based on the cumulative duration of temperature anomalies and cumulative on-site time, and the impact anomaly rate is determined based on the cumulative number of impact anomalies and entry / exit registration times. Finally, based on the temperature anomaly rate and impact anomaly rate, a first target level and a second target level are determined from multiple candidate freight quality levels. Based on the first target level and the second target level, the freight quality detection result is obtained. This application integrates dynamic monitoring data (ambient temperature and gravitational acceleration) from the Internet of Things (IoT) of the flat rack containers, as well as operational data such as the number of entries and exits and the duration of on-site storage, to accurately quantify the potential damage risks and adverse effects on goods during operations. It also uses computer technology to assist in the objective, quantifiable, and traceable detection of the freight quality of flat rack containers entering and leaving stations and storage yards, thereby improving the scientific nature and accuracy of freight quality detection and providing data support for freight quality control, partner hierarchical management, and process optimization.

[0069] In one embodiment, the temperature threshold conditions include a low temperature threshold condition, a high temperature threshold condition, and a temperature change threshold condition; such as Figure 3 As shown, in step S202 above, determining the cumulative duration of temperature anomalies based on ambient temperature data and temperature threshold conditions may include the following steps:

[0070] Step S301: Determine the first cumulative duration of the abnormal temperature range based on the ambient temperature data, low temperature threshold conditions, and high temperature threshold conditions.

[0071] Step S302: Determine the second cumulative duration of abnormal temperature change rate based on ambient temperature data and temperature change threshold conditions.

[0072] Step S303: Based on the first cumulative duration and the second cumulative duration, the cumulative duration of temperature anomaly is obtained.

[0073] The low-temperature threshold condition can be an ambient temperature of <-20°C, the high-temperature threshold condition can be an ambient temperature of >50°C, and the temperature change threshold condition can be the rate of temperature change of the chamber. .

[0074] The first cumulative duration can be the number of hours during which the temperature exceeds the safe range (ambient temperature of the container > 50°C or ambient temperature of the container < -20°C) during storage; the second cumulative duration can be the number of hours during which the ambient temperature experiences short-term drastic fluctuations (temperature change rate) during storage. The number of hours of temperature anomalies; the cumulative duration of temperature anomalies can be calculated using the following formula:

[0075]

[0076] In the above formula, This represents the cumulative duration of temperature anomalies. For the first cumulative duration, This is the second cumulative duration.

[0077] Specifically, the terminal identifies the first cumulative duration of abnormal temperature range based on ambient temperature data, low temperature threshold conditions, and high temperature threshold conditions; then it identifies the second cumulative duration of abnormal temperature change rate based on ambient temperature data and temperature change threshold conditions; and finally, it accumulates the first and second cumulative durations to obtain the cumulative duration of temperature abnormality.

[0078] In one embodiment, the acceleration threshold condition includes a longitudinal threshold condition, a lateral threshold condition, and a vertical threshold condition; in step S202 above, determining the cumulative number of impact anomalies based on the triaxial gravitational acceleration and the acceleration threshold condition may include the following steps:

[0079] Based on the triaxial gravitational acceleration, the longitudinal acceleration, lateral acceleration, and vertical acceleration are obtained; the number of longitudinal impact anomalies is determined based on the longitudinal acceleration and the longitudinal threshold condition, the number of lateral impact anomalies is determined based on the lateral acceleration and the lateral threshold condition, and the number of vertical impact anomalies is determined based on the vertical acceleration and the vertical threshold condition; based on the number of longitudinal impact anomalies, the number of lateral impact anomalies, and the number of vertical impact anomalies, the cumulative number of impact anomalies is obtained.

[0080] The longitudinal threshold condition can be longitudinal acceleration > 3g (g represents gravitational acceleration); the lateral threshold condition can be lateral acceleration > 2g; and the vertical threshold condition can be vertical acceleration > 4g.

[0081] The cumulative number of impact anomalies can be the number of impact anomalies (longitudinal acceleration > 3g, lateral acceleration > 2g or vertical acceleration > 4g) during loading / unloading / transportation.

[0082] Specifically, the terminal decomposes the triaxial gravitational acceleration to obtain longitudinal acceleration, lateral acceleration, and vertical acceleration; it identifies the number of longitudinal impact anomalies based on longitudinal acceleration and a longitudinal threshold condition, the number of lateral impact anomalies based on lateral acceleration and a lateral threshold condition, and the number of vertical impact anomalies based on vertical acceleration and a vertical threshold condition; then it accumulates the number of longitudinal impact anomalies, the number of lateral impact anomalies, and the number of vertical impact anomalies to obtain the cumulative number of impact anomalies.

[0083] In one embodiment, the candidate freight quality level includes an excellent level, a good level, a qualified level, and a warning level; before determining the first target level and the second target level among multiple candidate freight quality levels based on the temperature anomaly rate and the shock anomaly rate, the method of this application further includes the following steps: determining the indicator threshold ranges corresponding to the excellent level, the good level, the qualified level, and the warning level respectively.

[0084] In step S204 above, determining the first target level and the second target level from multiple candidate freight quality levels based on the temperature anomaly rate and the shock anomaly rate may include the following steps:

[0085] The target indicator threshold ranges for temperature anomaly rate and shock anomaly rate are determined within multiple indicator threshold ranges, and the first target level and the second target level are determined among multiple candidate freight quality levels based on the target indicator threshold ranges.

[0086] Among them, the candidate freight quality levels include, but are not limited to, excellent, good, qualified and alert. The threshold range of indicators and evaluation descriptions corresponding to each candidate freight quality level are shown in Table 1.

[0087] Table 1. Threshold ranges and evaluation descriptions for the four candidate freight quality levels.

[0088]

[0089] In one embodiment, the dynamic monitoring data includes initial ambient temperature data and initial triaxial gravitational acceleration. Step S201 above, which involves cleaning the dynamic monitoring data to obtain the ambient temperature data and triaxial gravitational acceleration, may include the following steps:

[0090] Sensor fault data, interference data, redundant data, and missing data were detected in the initial ambient temperature data and initial triaxial gravitational acceleration data. Sensor fault data, interference data, and redundant data were removed, and missing data were filled in to obtain the ambient temperature data and triaxial gravitational acceleration data.

[0091] Among them, sensor fault data: when temperature sensor data exceeds the sensor range (-40°C~85°C) for 24 consecutive hours or acceleration sensor data exceeds the sensor range (-10g~10g) for 24 consecutive hours, it is judged as fault data and is removed; interference data: Kalman filtering algorithm is used to remove instantaneous abnormal values ​​(such as single abnormal duration <0.1 seconds) caused by magnetic and electromagnetic interference from temperature or acceleration sensors; redundant data: duplicate data is removed, and the earliest valid data is retained; missing data: missing temperature and acceleration sensor data is filled in using interpolation.

[0092] Specifically, the terminal detects sensor fault data, interference data, redundant data, and missing data in the initial ambient temperature data and initial triaxial gravitational acceleration; it removes sensor fault data, interference data, and redundant data according to the corresponding processing methods, and completes the missing data to obtain the cleaned ambient temperature data and triaxial gravitational acceleration.

[0093] In one embodiment, step S204 above, obtaining the freight quality inspection result based on the first target level and the second target level, may include the following steps:

[0094] If the first target level and the second target level are the same, then the first target level or the second target level shall be used as the freight quality level; if the first target level and the second target level are different, then the lower of the first target level and the second target level shall be used as the freight quality level; corresponding early warning information shall be generated according to the freight quality level, and freight quality detection results shall be generated based on the freight quality level and the early warning information.

[0095] Specifically, the terminal determines whether the first target level and the second target level are the same; if the first target level and the second target level are identified as the same, the first target level or the second target level is taken as the freight quality level; if the first target level and the second target level are identified as different, the lower of the first target level and the second target level is taken as the freight quality level; corresponding early warning information is generated based on the freight quality level, and finally a freight quality detection result containing the freight quality level and early warning information is generated.

[0096] In one embodiment, such as Figure 4 As shown, a freight quality inspection method based on frame container monitoring data is provided in a specific embodiment, which includes the following steps:

[0097] Step S401: Obtain dynamic monitoring data and operational data for the target frame box within a unit detection cycle. The dynamic monitoring data includes initial ambient temperature data and initial triaxial gravitational acceleration. The operational data includes cumulative on-site time and number of entry and exit registrations. Detect sensor fault data, interference data, redundant data, and missing data in the initial ambient temperature data and initial triaxial gravitational acceleration. Remove sensor fault data, interference data, and redundant data, and complete the missing data to obtain the ambient temperature data and triaxial gravitational acceleration.

[0098] Step S402: Based on the ambient temperature data, low temperature threshold conditions, and high temperature threshold conditions, determine the first cumulative duration of the abnormal temperature range; based on the ambient temperature data and temperature change threshold conditions, determine the second cumulative duration of the abnormal temperature change rate; based on the first cumulative duration and the second cumulative duration, obtain the cumulative duration of the temperature abnormality.

[0099] Step S403: Based on the triaxial gravitational acceleration, obtain the longitudinal acceleration, lateral acceleration, and vertical acceleration; determine the number of longitudinal impact anomalies based on the longitudinal acceleration and longitudinal threshold conditions, determine the number of lateral impact anomalies based on the lateral acceleration and lateral threshold conditions, and determine the number of vertical impact anomalies based on the vertical acceleration and vertical threshold conditions; based on the number of longitudinal impact anomalies, the number of lateral impact anomalies, and the number of vertical impact anomalies, obtain the cumulative number of impact anomalies.

[0100] Step S404: Determine the temperature anomaly rate based on the cumulative duration of temperature anomalies and the cumulative duration of presence, and determine the impact anomaly rate based on the cumulative number of impact anomalies and the number of entry and exit registrations; determine the indicator threshold ranges corresponding to the excellent, good, qualified, and alert levels respectively; determine the target indicator threshold ranges for the temperature anomaly rate and impact anomaly rate within the multiple indicator threshold ranges respectively, and determine the first target level and the second target level among multiple candidate freight quality levels based on the target indicator threshold ranges respectively.

[0101] Step S405: If the first target level and the second target level are the same, then the first target level or the second target level shall be used as the freight quality level; if the first target level and the second target level are different, then the lower of the first target level and the second target level shall be used as the freight quality level; generate corresponding early warning information according to the freight quality level, and generate freight quality detection results based on the freight quality level and the early warning information.

[0102] The beneficial effects of the above embodiments are as follows:

[0103] This application integrates dynamic monitoring data (ambient temperature and gravitational acceleration) from the Internet of Things (IoT) of the flat rack containers, as well as operational data such as the number of entries and exits and the duration of on-site storage, to accurately quantify the potential damage risks and adverse effects on goods during operations. It also uses computer technology to assist in the objective, quantifiable, and traceable detection of the freight quality of flat rack containers entering and leaving stations and storage yards, thereby improving the scientific nature and accuracy of freight quality detection and providing data support for freight quality control, partner hierarchical management, and process optimization.

[0104] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0105] Based on the same inventive concept, this application also provides a freight quality inspection device based on frame container monitoring data for implementing the freight quality inspection method based on frame container monitoring data described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the freight quality inspection device based on frame container monitoring data provided below can be found in the limitations of the freight quality inspection method based on frame container monitoring data described above, and will not be repeated here.

[0106] In one exemplary embodiment, such as Figure 5 As shown, a freight quality inspection device based on frame container monitoring data is provided. The device may include:

[0107] The data acquisition module 501 is used to acquire dynamic monitoring data and operational data of the target frame box within a unit detection cycle. The dynamic monitoring data is cleaned to obtain ambient temperature data and triaxial gravitational acceleration. The operational data includes the cumulative on-site time and the number of entry and exit registrations.

[0108] The anomaly identification module 502 is used to determine the cumulative duration of temperature anomalies based on ambient temperature data and temperature threshold conditions, and to determine the cumulative number of impact anomalies based on triaxial gravitational acceleration and acceleration threshold conditions.

[0109] The numerical calculation module 503 is used to determine the temperature anomaly rate based on the cumulative duration of temperature anomalies and the cumulative duration of presence, and to determine the impact anomaly rate based on the cumulative number of impact anomalies and the number of entry and exit registrations.

[0110] The grade determination module 504 is used to determine the first target grade and the second target grade from multiple candidate freight quality grades based on the temperature anomaly rate and the shock anomaly rate, and to obtain the freight quality inspection result based on the first target grade and the second target grade.

[0111] In one embodiment, the temperature threshold conditions include a low temperature threshold condition, a high temperature threshold condition, and a temperature change threshold condition; the anomaly identification module 502 is further configured to determine a first cumulative duration of temperature range anomaly based on ambient temperature data, the low temperature threshold condition, and the high temperature threshold condition; determine a second cumulative duration of temperature change rate anomaly based on ambient temperature data and the temperature change threshold condition; and obtain the cumulative duration of temperature anomaly based on the first cumulative duration and the second cumulative duration.

[0112] In one embodiment, the acceleration threshold conditions include longitudinal threshold conditions, lateral threshold conditions, and vertical threshold conditions; the anomaly identification module 502 is further configured to obtain longitudinal acceleration, lateral acceleration, and vertical acceleration based on triaxial gravitational acceleration; determine the number of longitudinal impact anomalies based on longitudinal acceleration and longitudinal threshold conditions, determine the number of lateral impact anomalies based on lateral acceleration and lateral threshold conditions, and determine the number of vertical impact anomalies based on vertical acceleration and vertical threshold conditions; and obtain the cumulative number of impact anomalies based on the number of longitudinal impact anomalies, the number of lateral impact anomalies, and the number of vertical impact anomalies.

[0113] In one embodiment, the candidate freight quality level includes an excellent level, a good level, a qualified level, and a warning level; the device may further include: a range determination module, used to determine the index threshold ranges corresponding to the excellent level, the good level, the qualified level, and the warning level respectively; and a level determination module 504, used to determine the target index threshold ranges of the temperature anomaly rate and the shock anomaly rate respectively in the multiple index threshold ranges, and to determine a first target level and a second target level in the multiple candidate freight quality levels according to the target index threshold ranges respectively.

[0114] In one embodiment, the data acquisition module 501 is further configured to detect sensor fault data, interference data, redundant data, and missing data in the initial ambient temperature data and initial triaxial gravitational acceleration; remove sensor fault data, interference data, and redundant data, and complete the missing data to obtain the ambient temperature data and triaxial gravitational acceleration.

[0115] In one embodiment, the grade determination module 504 is further configured to: if the first target grade and the second target grade are the same, then take the first target grade or the second target grade as the freight quality grade; if the first target grade and the second target grade are different, then take the lower of the first target grade and the second target grade as the freight quality grade; generate corresponding early warning information based on the freight quality grade; and generate freight quality detection results based on the freight quality grade and the early warning information.

[0116] The modules in the aforementioned freight quality inspection device based on frame container monitoring data can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0117] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a freight quality inspection method based on frame container monitoring data. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0118] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0119] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0120] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0121] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0122] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0123] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0124] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0125] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A freight quality inspection method based on frame container monitoring data, characterized in that, The method includes: The system acquires dynamic monitoring data and operational data for the target frame box within a unit testing cycle. The dynamic monitoring data is then cleaned to obtain ambient temperature data and triaxial gravitational acceleration. The operational data includes the cumulative on-site time and the number of times the equipment enters and exits the site. The cumulative duration of temperature anomalies is determined based on the ambient temperature data and temperature threshold conditions, and the cumulative number of impact anomalies is determined based on the triaxial gravitational acceleration and acceleration threshold conditions. The temperature anomaly rate is determined based on the cumulative duration of the temperature anomaly and the cumulative duration of presence, and the impact anomaly rate is determined based on the cumulative number of impact anomalies and the number of entry and exit registrations. Based on the temperature anomaly rate and the shock anomaly rate, a first target level and a second target level are determined from multiple candidate freight quality levels, and freight quality detection results are obtained based on the first target level and the second target level.

2. The method according to claim 1, characterized in that, The temperature threshold conditions include low temperature threshold conditions, high temperature threshold conditions, and temperature change threshold conditions; determining the cumulative duration of temperature anomalies based on the ambient temperature data and temperature threshold conditions includes: Based on the ambient temperature data, the low temperature threshold condition, and the high temperature threshold condition, determine the first cumulative duration of the temperature range anomaly. Based on the ambient temperature data and the temperature change threshold condition, determine the second cumulative duration of the abnormal temperature change rate; The cumulative duration of the temperature anomaly is obtained based on the first cumulative duration and the second cumulative duration.

3. The method according to claim 1, characterized in that, The acceleration threshold conditions include longitudinal threshold conditions, lateral threshold conditions, and vertical threshold conditions; determining the cumulative number of impact anomalies based on the triaxial gravitational acceleration and acceleration threshold conditions includes: Based on the triaxial gravitational acceleration, the longitudinal acceleration, lateral acceleration, and vertical acceleration are obtained; The number of longitudinal impact anomalies is determined based on the longitudinal acceleration and the longitudinal threshold condition; the number of lateral impact anomalies is determined based on the lateral acceleration and the lateral threshold condition; and the number of vertical impact anomalies is determined based on the vertical acceleration and the vertical threshold condition. The cumulative number of impact anomalies is obtained based on the number of longitudinal impact anomalies, the number of transverse impact anomalies, and the number of vertical impact anomalies.

4. The method according to claim 1, characterized in that, The candidate freight quality levels include Excellent, Good, Acceptable, and Alert; before determining the first target level and the second target level from the multiple candidate freight quality levels based on the temperature anomaly rate and the shock anomaly rate, the process further includes: Determine the threshold range of the indicators corresponding to the excellent level, the good level, the qualified level, and the alert level, respectively; The step of determining a first target level and a second target level from multiple candidate freight quality levels based on the temperature anomaly rate and the shock anomaly rate includes: The target index threshold ranges for the temperature anomaly rate and the shock anomaly rate are determined respectively within the multiple index threshold ranges, and the first target level and the second target level are determined respectively among the multiple candidate freight quality levels based on the target index threshold ranges.

5. The method according to claim 1, characterized in that, The dynamic monitoring data includes initial ambient temperature data and initial triaxial gravitational acceleration. The process of cleaning the dynamic monitoring data to obtain the ambient temperature data and triaxial gravitational acceleration includes: The sensor fault data, interference data, redundant data, and missing data in the initial ambient temperature data and the initial triaxial gravitational acceleration data were detected. The sensor fault data, interference data, and redundant data are removed, and the missing data is filled in to obtain the ambient temperature data and the triaxial gravitational acceleration.

6. The method according to any one of claims 1 to 5, characterized in that, The process of obtaining freight quality inspection results based on the first target level and the second target level includes: If the first target level and the second target level are the same, then the first target level or the second target level shall be used as the freight quality level; If the first target level and the second target level are different, then the lower of the first target level and the second target level shall be used as the freight quality level; Based on the freight quality level, a corresponding early warning message is generated, and based on the freight quality level and the early warning message, the freight quality detection result is generated.

7. A freight quality inspection device based on frame container monitoring data, characterized in that, The device includes: The data acquisition module is used to acquire dynamic monitoring data and operational data of the target frame box within a unit detection cycle, and to perform data cleaning on the dynamic monitoring data to obtain ambient temperature data and triaxial gravitational acceleration; the operational data includes the cumulative on-site time and the number of entry and exit registrations; An anomaly identification module is used to determine the cumulative duration of temperature anomalies based on the ambient temperature data and temperature threshold conditions, and to determine the cumulative number of impact anomalies based on the triaxial gravitational acceleration and acceleration threshold conditions. The numerical calculation module is used to determine the temperature anomaly rate based on the cumulative duration of the temperature anomaly and the cumulative duration of presence, and to determine the impact anomaly rate based on the cumulative number of impact anomalies and the number of entry and exit registrations. The grade determination module is used to determine a first target grade and a second target grade from multiple candidate freight quality grades based on the temperature anomaly rate and the shock anomaly rate, and to obtain the freight quality inspection result based on the first target grade and the second target grade.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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