A durian quality grading and traceability information association management method

Abstract

The application discloses a durian quality grading and traceability information association management method, mainly relates to the quality analysis and traceability management field, aims at the problem of increasing uncertainty in the prior art by simply ensuring the quality of durians according to logistics planning, and specifically includes pre-transport quality grading, interference parameter identification, transportation log establishment and pre-quality detection at a red line node. The application is a whole-link, traceable and self-adaptive durian quality control system. In the overall technical scheme, the quality dynamic control of durians from picking and sorting to terminal transportation, accurate interference traceability and self-adaptive adjustment of transportation strategy are realized, and the durian grading accuracy, traceability efficiency and finished product good product rate are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of quality analysis and traceability management technology, specifically a method for managing the correlation between durian quality grading and traceability information. Background Technology

[0002] As a high-value tropical specialty fruit, durian has seen its consumer market continue to expand, and its cross-border and domestic cold chain transportation industry chain is becoming increasingly mature. It has formed a complete industrial system from orchard picking, centralized sorting, cold chain storage and transportation to terminal sales. However, the quality control and traceability management of the durian industry are still in a stage of extensive development. The overall technical level of the industry lags behind the demand for large-scale production and high-quality consumption, and there are many technical shortcomings that need to be addressed.

[0003] For example, the patent application number 2025119567939 discloses a method and device for optimizing durian logistics considering preservation constraints. It determines the central set of each logistics vehicle and the dynamic preservation feature set based on the preservation characteristics. It constructs a logistics route map by introducing a durian quality decay model. Through information interaction training of the logistics route map, it clarifies the optimal combination time and optimal combination objects for each logistics vehicle to combine logistics. It narrows the scope of logistics combination based on preservation constraints, reduces the logistics time, cost and cargo damage of large-scale logistics vehicles, and effectively ensures the quality of durian.

[0004] However, in actual durian transportation, it is difficult to conduct quality analysis before transportation, making it impossible to make transportation decisions and diversify transportation methods while providing data support for traceability. In addition, it is difficult to supervise durian quality based on quantitative parameters during transportation, making it impossible to make reasonable decisions on whether durian transportation should be carried out, rather than simply relying on logistics planning to ensure durian quality.

[0005] To address the aforementioned technical shortcomings, a solution is proposed that aims to analyze the quality grade of durians and make rational transportation decisions, and to link the quality grade of durians before transportation with traceability information, thereby improving the quality control management of durian products during transportation. Summary of the Invention

[0006] The purpose of this invention is to solve the problems mentioned above by proposing a method for the management of durian quality grading and traceability information.

[0007] The objective of this invention can be achieved through the following technical solution: a method for managing the association between durian quality grading and traceability information, the specific steps of which are as follows:

[0008] Step 1: Quality grading before transportation. Conduct quality analysis on the transportation stage after the durians are harvested, make transportation decisions based on the quality grade, determine the quality grade before transportation, and link it with traceability information.

[0009] Step 2: Interference parameter identification. Interference is identified based on the transportation or storage stage of the same type of durian, and fluctuation parameters are extracted when the quality changes. The type of interference parameter is determined by fluctuation parameter analysis.

[0010] Step 3: Establish transportation logs. After determining the quality grade of durian, establish transportation tasks and create transportation logs during the transportation phase. Record events in the transportation logs and extract parameters based on the data generated by the recorded events. Compare these parameters with interference parameters to identify transportation interference nodes.

[0011] Step 4: Quality inspection before the red line node. Perform quality inspection based on the transportation interference node, and adjust the traceability information according to the quality changes.

[0012] Furthermore, the quality analysis process in step one is as follows:

[0013] The durian is stimulated by mechanical knocking or sound waves to make it vibrate, and the vibration signal is collected by the sensor. Based on the collected vibration signal, the resonant frequency feature, sound velocity feature, and attenuation feature are extracted from the vibration signal. The resonant frequency feature, sound velocity feature, and attenuation feature are uniformly marked as an intrinsic feature set. A single durian is marked as the main body of the production line, and the feature set is analyzed.

[0014] Furthermore, a frequency threshold range corresponding to the resonance frequency feature is set, and immature fruits, overripe fruits, and mature fruits are identified by comparing the frequency threshold ranges.

[0015] Furthermore, the external features of the main body of the production line are extracted simultaneously. The external features are represented as the surface defect area and the length of the surface fruit thorn defect of the main body of the production line. When external features exist, the corresponding main body of the production line is sent and set separately. Otherwise, the internal and external features of the corresponding main body of the production line are analyzed simultaneously and sent for decision-making.

[0016] Furthermore, the specific process for setting up individual delivery is as follows:

[0017] If the main body of the current production line consists of unripe fruit, the source of the transport will be traced back to indicate that the fruit has collided with the source.

[0018] If the current production line mainly consists of overripe fruit, the source of the traceable delivery indicates a chaotic planning, and the attenuation characteristics are extracted. Based on the quantitative data of the current attenuation characteristics, the red line value is lowered.

[0019] If the main body of the current production line consists of mature fruit, it is inferred that the defect is caused by occasional interference. External feature identification is performed. If the area red line threshold and length red line threshold are not exceeded, specific transportation is carried out.

[0020] Furthermore, the specific process of simultaneously analyzing and delivering decisions is as follows:

[0021] Unripe fruits are temporarily stored and their ripening progress is analyzed. Sound velocity characteristics are monitored. If the sound velocity value continuously decreases in consecutive adjacent monitoring periods and drops to the maximum value of the ripe sound velocity range, then the fruits are transported and the storage environment during the transport stage is consistent with the current temporary storage environment. If the sound velocity value drops to the maximum value of the ripe sound velocity range in consecutive adjacent monitoring periods and approaches the minimum value of the ripe sound velocity range, then the transport is stopped.

[0022] Furthermore, the process for identifying interference parameters in step two is as follows:

[0023] Establish quantitative parameters for maturity progress analysis, record initial characteristic values ​​and floating trajectories, and simultaneously record delivery and storage environment parameters; determine the time of parameter fluctuation, collect the change in the floating trajectory of the quantitative parameters before and after the fluctuation time; collect the duration of the same trend in the floating trajectory within the two time periods corresponding to two adjacent fluctuation times;

[0024] If the change in the quantization parameter's floating trajectory exceeds the change threshold, or if the duration of the floating trajectory following the same trend exceeds the same trend duration threshold, then the corresponding parameter fluctuation moment will be marked as an interference node, and the corresponding transmission and storage environment parameters will be marked as interference parameters.

[0025] Furthermore, the process of establishing the transportation log in step three is as follows:

[0026] Establish a transportation log for the main production line, extract surrounding environmental parameters, and record the fluctuation range of similar storage environment parameters when fluctuations occur; obtain the frequency of fluctuation of similar storage environment parameters when surrounding environmental parameters are within the same range of influence.

[0027] Furthermore, if the fluctuation range of the same type of storage environment parameters does not exceed the parameter fluctuation range threshold, and the fluctuation frequency of the same type of storage environment parameters does not exceed the parameter fluctuation frequency threshold, then the current node is marked as a stable control node. If the storage environment parameters are consistent with the interference parameters at this time, then it is marked as a node with no interference. Otherwise, the current node is marked as a fluctuating control node. If the storage environment parameters are consistent with the interference parameters at this time, then it is marked as a node causing interference.

[0028] Furthermore, when there is no environmental interference, the change range of the quantitative parameters of maturity progress analysis is compared. If the quantitative parameters of maturity progress analysis do not exceed the set red line threshold and the change range of the quantitative parameters of maturity progress analysis is still within the set range, transportation continues according to the current route. The current time when there is no environmental interference is not used as the source tracing time. When there is interference, if the interval between the quantitative parameters of maturity progress analysis and the set red line threshold is shortening, transportation is suspended and sampling is carried out. In the later source tracing, the current interference inducing node and the corresponding processing flow after the node is generated are used as the source tracing direction.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] 1. This invention enables precise, non-destructive grading of durians before harvesting. It relies on an excitation device and dual sensors to collect three core intrinsic characteristics: resonant frequency, sound velocity, and attenuation. Combined with machine vision to identify external defects, it constructs a multi-dimensional quality grading system that links internal and external factors, completely eliminating the drawbacks of subjective human judgment and significantly improving the standardization and accuracy of grading. At the same time, it also enables precise source identification of defects, accurately distinguishing between three types of defects: external impact defects, defects caused by untimely storage and transportation, and occasional minor defects. It also allows for the development of differentiated disposal strategies such as waste sorting, source warnings, batch control, and short-distance, short-time transportation to minimize the loss of high-quality fruit.

[0031] 2. This invention also constructs a dynamic interference identification mechanism for durian storage and transportation, breaking through the limitations of traditional static parameter comparison. By tracking the fluctuation trajectory, slope change, and trend duration of intrinsic characteristic parameters in real time, combined with environmental parameter fluctuation data, it can achieve comprehensive and accurate identification. At the same time, it also quantitatively analyzes the causal relationship between durian ripening progress and storage and transportation environment, providing accurate quantitative data support for subsequent transportation log node determination, quality re-inspection, traceability adjustment, and environmental optimization. This enables precise control of storage and transportation interference from the source, thus avoiding the problem of large-scale quality deterioration caused by continuous environmental interference in advance.

[0032] 3. This invention also establishes a standardized intelligent transportation log with precise timestamps, which fully records the environmental control process, parameter fluctuation range, fluctuation frequency and other core data of the entire transportation process, and fully restores the real control status of the transportation scenario; and realizes the fine classification of transportation nodes through standardized threshold judgment, accurately distinguishing stable control nodes, interference-free safe nodes, abnormal fluctuation nodes and interference-induced nodes. Attached Figure Description

[0033] Fig. 1 The flowchart for the steps of this invention is mainly used to illustrate the logical relationship between the data flow and the physical execution layer;

[0034] Fig. 2 This is a flowchart of the pre-transport quality grading method in this invention, which mainly represents the core logical process of this case: internal characteristics (acoustic / vibration) + external characteristics (visual) → eliminating defective products or determining the transportation strategy. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0036] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0037] Please see Figs. 1-2 As shown, a method for managing the association between durian quality grading and traceability information is described, with the following specific steps:

[0038] Step 1: Quality grading before transportation. Conduct quality analysis on durians during the transportation stage after harvesting, make transportation decisions based on quality grades, determine the quality grade before transportation and link it with traceability information to improve traceability efficiency.

[0039] Step 2: Interference parameter identification. Interference is identified based on the transportation or storage stage of the same type of durian, and fluctuation parameters are extracted when the quality changes. The type of interference parameter is determined by fluctuation parameter analysis.

[0040] Step 3: Establish transportation logs. After determining the quality grade of durian, establish transportation tasks and create transportation logs during the transportation phase. Record events in the transportation logs and extract parameters based on the data generated by the recorded events. Compare these parameters with interference parameters to identify transportation interference nodes.

[0041] Step 4: Quality inspection before the red line node. Perform quality inspection based on the transportation interference node, and adjust the traceability information according to the quality changes.

[0042] The quality analysis process in step one is as follows:

[0043] The durian is vibrated by mechanically striking it or using sound waves, and the vibration signals are collected by sensors, such as microphones and piezoelectric sensors. It should be noted that the core hardware is the excitation device (such as a small hammer, air valve, or horn).

[0044] Then, based on the collected vibration signals, the resonant frequency characteristics, sound velocity characteristics, and attenuation characteristics of the vibration signals are extracted;

[0045] The resonance frequency characteristic is represented by performing a Fast Fourier Transform (FFT) on the vibration signal, converting the time-domain signal into the frequency domain, and finding the frequency point with the largest amplitude, which is the main resonance frequency.

[0046] The sound velocity characteristic represents the speed at which sound waves propagate through the internal medium of a durian. It is directly related to the hardness and density of the flesh. The specific collection method involves placing two vibration sensors at a certain distance on the surface of the durian, applying a tapping excitation to one end, accurately measuring the time difference between the arrival of the sound waves at the two sensors, and obtaining the velocity based on the distance.

[0047] The attenuation characteristic represents the degree to which sound wave energy is absorbed and lost when it propagates inside the durian. The specific acquisition method is to obtain a clear time-domain vibration signal, analyze the curve of signal amplitude attenuation over time, and calculate its attenuation coefficient, that is, the rate of energy loss.

[0048] The resonant frequency characteristics, sound velocity characteristics, and attenuation characteristics are uniformly labeled as an intrinsic feature set. Data analysis is performed based on the intrinsic feature set. A single durian is labeled as the main body of the production line, and the intrinsic feature set extracted during the sorting stage is analyzed.

[0049] Set the frequency threshold range corresponding to the resonance frequency feature. When the resonance frequency feature of the main body of the production line exceeds the maximum value of the frequency threshold range, it is defined as a high frequency feature, that is, it is identified as an unripe fruit. In actual scenarios, the flesh is firm and dense, the resonance frequency is high, and the sound is crisp and short, usually >800Hz.

[0050] When the resonant frequency characteristics of the main body of the production line do not exceed the minimum value of the frequency threshold range, it is defined as an extremely low frequency characteristic, that is, it is identified as an overripe fruit. In actual scenarios, the tissue is hydrated or rotten, the structure is lost, the resonant frequency is extremely low or disappears, and the sound is dull.

[0051] When the resonant frequency characteristics of the main body of the production line are within the frequency threshold range, the mid-to-low frequency characteristics are located, that is, the fruit is identified as mature. In actual scenarios: the flesh softens, forming an ideal cavity between the flesh and the shell, the resonant frequency drops to the mid-to-low frequency range (200-600Hz), and the sound is hollow and reverberant. It should be explained that the frequency threshold range is a threshold range set manually based on the results of durian maturity analysis during the historical sorting stage.

[0052] During the internal feature set identification and sorting stage, the external features of the main body of the production line are extracted simultaneously. The external features are represented as the surface defect area and the surface fruit thorn defect length of the main body of the production line. The surface defect area is obtained by the visual sensor, and the fruit thorn defect length is calculated by combining the length deviation of adjacent fruit thorns with multi-angle photos. That is, the current fruit thorn defect length is obtained based on the adjacent fruit thorn length deviation threshold of the corresponding durian category.

[0053] When the main body of the production line has surface defect area or surface fruit thorn defect length, the corresponding main body of the production line will be sent and set separately. When the main body of the production line does not have surface defect area or surface fruit thorn defect length, the corresponding main body of the production line will be analyzed simultaneously for internal and external characteristics and sent for decision-making.

[0054] The process for setting up individual delivery is as follows:

[0055] If the main body of the current production line consists of unripe fruit, it is inferred that the defect is caused by external interference, i.e., by dropping or picking. In this case, the fruit is sorted out and not transported, and the source of the transport is traced to indicate the source of the collision. If the number of collision fruits from the same source exceeds the set threshold, a warning is issued.

[0056] If the main body of the current production line consists of overripe fruit, it is inferred that the defect is caused by untimely delivery. In this case, the fruit is sorted out and not delivered, and the source of delivery is traced to indicate that the planning is chaotic. Batch control is implemented for subsequent deliveries. Furthermore, the decay characteristics are extracted, and the red line value is lowered based on the quantitative data of the current decay characteristics, i.e. the decay rate value, to avoid the phenomenon of untimely delivery.

[0057] If the current production line consists of mature fruit, it is inferred that the defect is caused by an occasional disturbance. External feature identification is performed. If the defect does not exceed the corresponding set thresholds, specifically the area red line threshold and the length red line threshold, then specific transportation is carried out, namely short-distance transportation or short transportation time transportation, to reduce storage time. It should be explained that the area red line threshold and the length red line threshold are thresholds that are artificially set based on the summary of scenarios that occur during the transportation process and have no impact.

[0058] The specific process of synchronously analyzing and delivering decisions is as follows:

[0059] Unripe fruits are temporarily stored and their ripening progress is analyzed. Using sound velocity characteristics as parameters, the current sound velocity value is recorded. Sound velocity values ​​are monitored during the temporary storage stage. If the sound velocity value corresponding to consecutive adjacent monitoring cycles in the current temporary storage environment continues to decrease and drops to the maximum value in the ripe sound velocity range, then the main body of the current production line will transport the fruit, and the storage environment during the transportation stage will be consistent with the current temporary storage environment, specifically temperature, humidity, etc.

[0060] If, under the current temporary storage environment, the corresponding sound velocity value for consecutive adjacent monitoring cycles drops to the maximum value of the mature sound velocity range and approaches the minimum value of the mature sound velocity range, then the main body of the current production line becomes overripe fruit and transportation stops; "approaching" specifically refers to the distance between the sound velocity value of the current monitoring cycle and the minimum value of the sound velocity range. If, according to the current sound velocity change rate, it is impossible to reach the next monitoring cycle, then the transportation time is too late and it is not suitable for transportation; the corresponding sound velocity range is also a range threshold set artificially based on data extracted from the durian ripening progress monitoring process.

[0061] The interference parameter identification process in step two is as follows:

[0062] Based on any feature in the intrinsic feature set, quantitative parameters for maturity progress analysis of the main body of the production line corresponding to the delivery stage are used. The initial feature value is recorded at the start of delivery, and the floating trajectory at each moment is continuously recorded. The delivery storage environment parameters at the current moment are recorded simultaneously.

[0063] Based on the recorded transport and storage environment parameters, determine the time of parameter fluctuation, collect the changes in the parameter floating trajectory of the main production line before and after the fluctuation time, and express the changes in the slope of the corresponding floating trajectory. Collect the duration of the same trend of the floating trajectory in the two time periods corresponding to two adjacent fluctuation times; that is, the time period after the first fluctuation time and the time period after the second fluctuation time.

[0064] If the change in the floating trajectory of the main analytical parameters of the production line exceeds the change threshold before and after the fluctuation moment, or if the duration of the same trend in the floating trajectory of two adjacent fluctuation moments exceeds the same trend duration threshold, then the corresponding parameter fluctuation moment will be marked as an interference node, and the corresponding transport and storage environment parameters will be marked as interference parameters. The change threshold and the same trend duration threshold are both manually set thresholds that combine the historical transport stages and the corresponding intrinsic characteristics comparison results.

[0065] If the change in the floating trajectory of the main analytical parameters of the production line before and after the fluctuation moment does not exceed the change threshold, and the duration of the same trend in the floating trajectory within the two time periods corresponding to two adjacent fluctuation moments does not exceed the duration threshold of the same trend, then the corresponding parameter fluctuation moment is marked as a stable node, and the corresponding delivery and storage environment parameters are marked as stable parameters.

[0066] The process of creating the transportation log in step three is as follows:

[0067] Based on the transport route and real-time transport progress, a transport log is established for the main body of the current production line. The storage environment control process at each transport moment is recorded. The control efficiency of the storage environment parameters is analyzed based on the control process. The surrounding environment parameters are extracted based on different locations. When the surrounding environment parameters fluctuate, the fluctuation span of the same type of storage environment parameters is recorded. At this time, the fluctuation span is within the stage where the parameter control cycle has not been triggered. For example, it does not exceed the set threshold, but continuous fluctuations will also cause changes in durian storage.

[0068] At the same time, when the surrounding environmental parameters are within the same range of influence, the fluctuation frequency of the same type of storage environment parameters is obtained. At this time, the fluctuation frequency is within the stage of triggering the parameter control cycle. If the parameter fluctuation exceeds the threshold, it will be controlled immediately.

[0069] Based on the fluctuation range and frequency of parameter fluctuations in similar storage environments, a transportation log is created with timestamps attached.

[0070] If the fluctuation range of the same type of storage environment parameters does not exceed the parameter fluctuation range threshold, and the fluctuation frequency of the same type of storage environment parameters does not exceed the parameter fluctuation frequency threshold, then the current node is marked as a stable control node. If the storage environment parameters are consistent with the interference parameters at this time, then it is marked as an interference-free node.

[0071] If the fluctuation range of the same type of storage environment parameters exceeds the parameter fluctuation range threshold, or the fluctuation frequency of the same type of storage environment parameters exceeds the parameter fluctuation frequency threshold, then the current node is marked as the control fluctuation node. If the storage environment parameters are consistent with the interference parameters at this time, then it is marked as the interference cause node.

[0072] The quality inspection process before the red line node in step four is as follows:

[0073] When there is no environmental interference, extract the real-time internal feature set and compare the change range of the maturity progress analysis quantitative parameters. If the real-time maturity progress analysis quantitative parameters do not exceed the set red line threshold and the change range of the maturity progress analysis quantitative parameters is still within the set range, then it is determined that the current transportation is undisturbed and transportation continues according to the current route. When the main body of the current batch production line has an abnormality later, the current time when there is no environmental interference will not be used as the traceability time. Moreover, the time when there is no environmental interference can assist in traceability and perform time period filtering, that is, the time period before and after the current time is a safe time period.

[0074] It should be explained that at this moment there is no situation where the red line threshold is exceeded or the range is not exceeded;

[0075] When the interference trigger node is reached, if the interval between the real-time maturity progress analysis quantitative parameters and the set red line threshold shows a shortening trend, it is necessary to suspend transportation and conduct sampling inspections on the main body of the production line. Environmental control adjustments should be made according to the current interference parameter type, and if necessary, the transportation route should be changed to end the transportation of the main body of the production line as soon as possible. In the later source tracing, the current interference trigger node and the corresponding handling process after the node is generated should be used as the source tracing direction.

[0076] It should be explained that at this moment, there is no situation where the trend of shortening does not exist.

[0077] In summary, a dynamic temporary storage and monitoring mechanism is set up for unripe fruit. By monitoring the ripening progress in real time through sound velocity characteristics, the mechanism can accurately determine transportable nodes and deterioration nodes, preventing blind abandonment or ineffective transportation. This lays a data foundation for subsequent full-process traceability and control, effectively improving the overall efficiency of traceability work. At the same time, log data is linked with previous interference parameters and quality grading data to build a full-time, full-dimensional transportation traceability database, which greatly improves the accuracy and efficiency of subsequent quality anomaly traceability.

[0078] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for managing the correlation between durian quality grading and traceability information, characterized in that, The specific steps are as follows: Step 1: Quality grading before transportation. Conduct quality analysis on the transportation stage after the durians are harvested, make transportation decisions based on the quality grade, determine the quality grade before transportation, and link it with traceability information. Step 2: Interference parameter identification. Interference is identified based on the transportation or storage stage of the same type of durian, and fluctuation parameters are extracted when the quality changes. The type of interference parameter is determined by fluctuation parameter analysis. Step 3: Establish transportation logs. After determining the quality grade of durian, establish transportation tasks and create transportation logs during the transportation phase. Record events in the transportation logs and extract parameters based on the data generated by the recorded events. Compare these parameters with interference parameters to identify transportation interference nodes. Step 4: Quality inspection before the red line node. Perform quality inspection based on the transportation interference node, and adjust the traceability information according to the quality changes.

2. The method for managing durian quality grading and traceability information according to claim 1, characterized in that, The quality analysis process in step one is as follows: The durian is stimulated by mechanical knocking or sound waves to make it vibrate, and the vibration signal is collected by the sensor. Based on the collected vibration signal, the resonant frequency feature, sound velocity feature, and attenuation feature are extracted from the vibration signal. The resonant frequency feature, sound velocity feature, and attenuation feature are uniformly marked as an intrinsic feature set. A single durian is marked as the main body of the production line, and the feature set is analyzed.

3. The method for managing durian quality grading and traceability information according to claim 2, characterized in that, Set the frequency threshold range corresponding to the resonance frequency feature, and identify unripe fruit, overripe fruit and ripe fruit by comparing the frequency threshold range.

4. The method for managing durian quality grading and traceability information according to claim 3, characterized in that, Simultaneously extract the external features of the main body of the production line. The external features are represented by the surface defect area and the length of the surface fruit thorn defect of the main body of the production line. When external features exist, the corresponding main body of the production line is sent and set separately. Otherwise, the internal and external features of the corresponding main body of the production line are analyzed simultaneously and sent for decision-making.

5. The method for managing durian quality grading and traceability information according to claim 4, characterized in that, The specific process for setting up individual delivery is as follows: If the main body of the current production line consists of unripe fruit, the source of the transport will be traced back to indicate that the fruit has collided with the source. If the current production line mainly consists of overripe fruit, the source of the traceable delivery indicates a chaotic planning, and the attenuation characteristics are extracted. Based on the quantitative data of the current attenuation characteristics, the red line value is lowered. If the main body of the current production line consists of mature fruit, it is inferred that the defect is caused by occasional interference. External feature identification is performed. If the area red line threshold and length red line threshold are not exceeded, specific transportation is carried out.

6. The method for managing durian quality grading and traceability information according to claim 4, characterized in that, The specific process of synchronously analyzing and delivering decisions is as follows: Unripe fruits are temporarily stored and their ripening progress is analyzed. Sound velocity characteristics are monitored. If the sound velocity value continuously decreases in consecutive adjacent monitoring periods and drops to the maximum value of the ripe sound velocity range, then the fruits are transported and the storage environment during the transport stage is consistent with the current temporary storage environment. If the sound velocity value drops to the maximum value of the ripe sound velocity range in consecutive adjacent monitoring periods and approaches the minimum value of the ripe sound velocity range, then the transport is stopped.

7. The method for managing durian quality grading and traceability information according to claim 1, characterized in that, The interference parameter identification process in step two is as follows: Establish quantitative parameters for maturity progress analysis, record initial characteristic values ​​and floating trajectories, and simultaneously record delivery and storage environment parameters; determine the time of parameter fluctuation, collect the change in the floating trajectory of the quantitative parameters before and after the fluctuation time; collect the duration of the same trend in the floating trajectory within the two time periods corresponding to two adjacent fluctuation times; If the change in the quantization parameter's floating trajectory exceeds the change threshold, or if the duration of the floating trajectory following the same trend exceeds the same trend duration threshold, then the corresponding parameter fluctuation moment will be marked as an interference node, and the corresponding transmission and storage environment parameters will be marked as interference parameters.

8. The method for managing durian quality grading and traceability information according to claim 1, characterized in that, The process of creating the transportation log in step three is as follows: Establish a transportation log for the main production line, extract surrounding environmental parameters, and record the fluctuation range of similar storage environment parameters when fluctuations occur; obtain the frequency of fluctuation of similar storage environment parameters when surrounding environmental parameters are within the same range of influence.

9. A method for managing durian quality grading and traceability information according to claim 8, characterized in that, If the fluctuation range of the same type of storage environment parameters does not exceed the parameter fluctuation range threshold, and the fluctuation frequency of the same type of storage environment parameters does not exceed the parameter fluctuation frequency threshold, then the current node is marked as a stable control node. If the storage environment parameters are consistent with the interference parameters at this time, then it is marked as a node with no interference. Otherwise, the current node is marked as a fluctuating control node. If the storage environment parameters are consistent with the interference parameters at this time, then it is marked as a node causing interference.

10. A method for managing the association between durian quality grading and traceability information according to claim 9, characterized in that, When there is no environmental interference, the change range of the quantitative parameters of maturity progress analysis is compared. If the quantitative parameters of maturity progress analysis do not exceed the set red line threshold and the change range of the quantitative parameters of maturity progress analysis is still within the set range, transportation continues according to the current route. The current time when there is no environmental interference is not used as the time for tracing the source. When there is interference, if the interval between the quantitative parameters of maturity progress analysis and the set red line threshold is shortening, transportation is suspended and sampling is carried out. In the later tracing, the current interference inducing node and the corresponding processing flow after the occurrence of the node are used as the tracing direction.

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