Pork quality evaluation method and system for cold chain transportation

Abstract

This invention discloses a method and system for assessing pork quality in cold chain transportation, relating to the field of pork quality testing technology. The method includes the following steps: collecting multiple types of data reflecting changes in pork quality throughout the cold chain transportation process; establishing a unified time-series list; marking the fluctuation trends of various data types in relation to temperature and humidity changes; generating a dynamic data draft; analyzing the time differences in the dynamic data draft; extracting synchronous change segments of pH value and volatile basic nitrogen; and separately labeling and summarizing segments lacking synchronous characteristics to form a list of asynchronous characteristics. This invention, by establishing a unified time-series list of multiple data types, achieves dynamic correlation and continuous tracking of physicochemical indicators and environmental parameters, improving the real-time performance and accuracy of quality assessment; and through asynchronous characteristic identification and dynamic adjustment mechanisms, it adaptively corrects asynchronous phenomena, ensuring that the assessment of freshness and spoilage remains stable and reliable under environmental fluctuations.

Description

Technical Field

[0001] This invention relates to the field of pork quality testing technology, specifically to a method and system for assessing pork quality for cold chain transportation. Background Technology

[0002] Pork quality assessment for cold chain transportation refers to a comprehensive method that continuously, objectively, and quantitatively analyzes changes in pork quality under low-temperature conditions throughout the entire storage and transportation process. This method utilizes multi-source sensing technology, big data processing, intelligent analysis, and dynamic evaluation models. The core of this method is the collection of physicochemical indicators (volatile basic nitrogen, pH, moisture content, etc.) and microbiological indicators during cold chain transportation. Through big data processing, noise removal and outlier correction are achieved, constructing a multi-dimensional quality indicator system. Combined with a dynamic weight allocation algorithm, freshness and spoilage are assessed in real time, and the results are output in a visualized form, enabling intelligent monitoring and grading of pork quality throughout the entire cold chain transportation process. This method overcomes the limitations of traditional methods that rely on manual sampling, have slow response times, and struggle to reflect dynamic changes during transportation. It provides data-driven technical support for quality control, risk warning, and traceability management in cold chain meat transportation.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, pork quality assessment typically relies on a single indicator or fixed threshold, lacking the ability to identify the dynamic relationships between multiple indicators. When pH and volatile basic nitrogen fluctuate asynchronously during cold chain transportation, the system may mistake a sudden change in one indicator for detection noise, thus overlooking potential quality anomalies. Such asynchronous phenomena often occur in scenarios involving sudden changes in temperature and humidity, sensor response delays, or uneven local heat exchange. This leads to the assessment process failing to accurately reflect the true changes within the meat, easily masking signs of spoilage and preventing timely risk warnings, thereby affecting the overall quality, safety, and reliability of the cold chain transportation process.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for evaluating pork quality for cold chain transportation, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the quality of pork for cold chain transportation, comprising the following steps:

[0008] Collect various types of data reflecting changes in pork quality throughout the entire cold chain transportation process, establish a unified time series list, and mark the fluctuation trends of various data with corresponding changes in temperature and humidity to generate dynamic data drafts that can be used for subsequent analysis.

[0009] The dynamic data draft was sorted out for time differences, and synchronous change segments of pH value and volatile basic nitrogen were extracted. Segments that did not have synchronous characteristics were separately marked and summarized into a list of asynchronous characteristics, which provided a basis for subsequent difference identification.

[0010] Based on the asynchronous feature list, backtrack time series data to identify time segments that continuously shift during short-term fluctuations. The obtained segments are mapped to temperature and humidity change nodes to construct an anomaly coupling diagram, which is used to characterize the potential sources of quality shifts.

[0011] By using anomaly coupling diagrams to conduct comparative analysis of indicators, extracting the cross-change patterns between indicators, tracking the direction and duration of change over a continuous time period, and generating a change coupling draft, this can be used to locate key turning points in pork quality trends.

[0012] Based on the change coupling draft, dynamic adjustment operations are performed, and breathing-style micro-expansion control is implemented on the time response rhythm of the quality assessment process. An inverse time interval rotation method is introduced so that the response step of freshness and spoilage degree is automatically adjusted with changes in temperature and humidity, thereby achieving adaptive repair and continuous stable assessment of asynchronous phenomena.

[0013] Preferably, the steps for generating dynamic data drafts are as follows:

[0014] Collect various types of data throughout the entire cold chain transportation process, including the pH value, moisture content, volatile basic nitrogen value, conductivity, color parameters, temperature, humidity, and airflow velocity of pork. Simultaneous recording of physicochemical properties and environmental conditions is achieved at continuous sampling intervals.

[0015] The collected data are sorted in chronological order to create a time series list. The collection period of different indicators is adjusted by uniform time granularity so that pH value, volatile basic nitrogen, moisture content, temperature and humidity have corresponding relationships at the same time step.

[0016] The changes in physicochemical indicators in the time series list are mapped to changes in temperature and humidity to form a set of trend segments;

[0017] By integrating time series lists and trend segment sets, a dynamic data draft is generated to reflect the dynamic coupling relationship between pork quality change trajectory and environmental conditions.

[0018] Preferably, the asynchronous feature list generation steps are as follows:

[0019] The dynamic data draft was analyzed for differences over time. Based on time points, the data on pH value, volatile basic nitrogen, moisture content, temperature and humidity were compared in segments to identify the relationship between the two changes over time.

[0020] The intervals in which pH value and volatile basic nitrogen change in the same direction within the same time period are extracted from the time difference analysis results and defined as synchronous change segments. The start and end times, change amplitude and corresponding temperature and humidity parameters are recorded.

[0021] Time intervals that do not have synchronous characteristics are marked, and the direction of change, duration of delay, and correspondence with temperature and humidity fluctuations are recorded.

[0022] All asynchronous segments are summarized to generate an asynchronous feature list. The start time, end time, direction of change, and corresponding environmental information of each segment are recorded with time as the main axis for difference identification.

[0023] Preferably, the asynchronous feature list is arranged in chronological order, and segments with the same asynchronous features within consecutive time periods are merged to form continuously distributed asynchronous feature intervals. At the same time, synchronous change segments are compared and labeled with asynchronous segments to construct a time-series correlation map reflecting the relationship between quality changes and environmental fluctuations.

[0024] Preferably, the steps for constructing the abnormal coupling graph are as follows:

[0025] The asynchronous feature list is backtracked and imported to determine the location range of asynchronous segments in the time series, and data such as pH value, volatile basic nitrogen, moisture content, temperature and humidity are extracted to form a local time series set.

[0026] Fluctuation backtracking analysis is performed on local time series sets to identify time segments that exhibit continuous shift characteristics within a short time range, and the direction of change, duration, and temperature and humidity change characteristics are recorded.

[0027] The continuous offset time segments are correlated with temperature and humidity change nodes to establish a mapping relationship between time interval, offset direction and duration.

[0028] An anomaly coupling diagram is constructed based on the time correspondence, with time as the horizontal axis and changes in quality indicators and environmental parameters as the vertical distribution. The coupling relationship types are classified and labeled to characterize the potential sources of quality deviation.

[0029] Preferably, when constructing the anomaly coupling graph, the continuous offset time segments are classified and labeled according to the time delay type and the direction of change. The offset segments that overlap with the temperature change node are marked as direct correspondences, the offset segments that have time lags are marked as delayed correspondences, and the offset segments with the same direction of change are marked as co-directional correspondences. This forms a multi-dimensional time correlation in the anomaly coupling graph, which is used to distinguish different types of quality offset features.

[0030] Preferably, the steps for generating the change-coupled draft are as follows:

[0031] The temporal relationships and index change trajectories in the abnormal coupling diagram are layered and expanded to determine the comparative analysis range of pH value, volatile basic nitrogen, moisture content, temperature and humidity, and to extract continuous data intervals that include the process before and after the environmental disturbance.

[0032] Cross-variation comparison was performed on the time-expanded anomalous coupling plot data to extract the forward, reverse, and delayed coupling relationships between pH value and volatile basic nitrogen within the same time interval, and the direction of change, duration, and environmental conditions were recorded.

[0033] Track the direction and duration of changes over a continuous period of time to identify the trend stages and turning points of indicator changes;

[0034] A draft of the change coupling was generated, which integrates the cross-relationship of multiple indicators with environmental parameters in chronological order to form a time-series framework for locating key turning points in pork quality trends.

[0035] Preferably, when generating the change coupling draft, each cross relationship is labeled with the corresponding environmental fluctuation type and transportation stage information, and a two-layer comparison structure of quality indicators and environmental parameters is established on the timeline. The overlapping and turning points of the cross trajectories of multiple indicators are displayed by overlaying, so as to improve the accuracy and continuity of pork quality trend positioning.

[0036] Preferably, based on the change coupling draft, dynamic adjustment operations are performed, and a breathing-style micro-expansion control is implemented on the time response rhythm of the quality assessment process. An inverse time interval alternation method is introduced, so that the response step of freshness and spoilage degree is automatically adjusted with changes in temperature and humidity. The steps are as follows:

[0037] Based on the draft of the change coupling, the time response rhythm of the quality assessment process is initially set, and the time stages are divided according to the turning points of quality indicators, duration and environmental change nodes, and the assessment cycle and time step are determined.

[0038] Implement breathing-style micro-expansion control, expanding and contracting the step size between adjacent evaluation cycles to keep the time resolution of quality evaluation consistent with environmental changes;

[0039] An inverse time interval alternation method is introduced to alternately adjust the response step size for freshness and spoilage, and to fine-tune the response interval based on the rate of change of temperature and humidity.

[0040] By integrating the breathing-style micro-expansion control with the results of the reverse phase time interval rotation, a continuous and stable quality assessment rhythm is formed, which is used to achieve adaptive repair of asynchronous phenomena and continuous assessment stability.

[0041] A pork quality assessment system for cold chain transportation includes a data acquisition and time series construction module, a synchronization relationship identification module, an anomaly coupling construction module, a change feature analysis module, and a dynamic adaptive adjustment module.

[0042] The data acquisition and time series construction module collects various types of data reflecting changes in pork quality throughout the cold chain transportation process, establishes a unified time series list, marks the fluctuation trends of various data with corresponding changes in temperature and humidity, and generates dynamic data drafts that can be used for subsequent analysis.

[0043] The synchronization relationship identification module sorts out the time differences in the dynamic data draft, extracts the synchronous change segments of pH value and volatile basic nitrogen, and separately marks the segments that do not have synchronous characteristics and summarizes them into a list of asynchronous characteristics, providing a basis for subsequent difference identification.

[0044] The anomaly coupling construction module backtracks time series data based on the asynchronous feature list, identifies time segments that continuously shift during short-term fluctuations, maps the acquired segments to temperature and humidity change nodes, and constructs an anomaly coupling diagram to characterize the potential sources of quality shifts.

[0045] The change feature analysis module uses anomaly coupling diagrams to conduct comparative analysis of indicators, extract the cross-change patterns between indicators, track the direction and duration of change over a continuous time period, and generate a change coupling draft to locate key turning points in pork quality trends.

[0046] The dynamic adaptive adjustment module performs dynamic adjustment operations according to the change coupling draft, implements breathing-style micro-expansion control on the time response rhythm of the quality assessment process, and introduces an inverse time interval rotation method to automatically adjust the response step of freshness and spoilage degree with changes in temperature and humidity, thereby achieving adaptive repair and continuous stable assessment of asynchronous phenomena.

[0047] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0048] This invention establishes a unified time-series list of multiple data types throughout the cold chain transportation process and marks the fluctuation trends of physicochemical indicators and environmental parameters accordingly. This enables dynamic correlation and continuous tracking of quality data, allowing the evaluation results to reflect the quality changes of pork during transportation in real time. This multi-source data time alignment method avoids misjudgments based on single indicators, transforming quality assessment from discrete sampling to continuous monitoring. This improves the completeness of data representation and the timeliness of assessment, ensuring dynamic visibility and real-time controllability of the quality change process.

[0049] This invention identifies the dynamic response relationship between quality indicators and environmental factors by constructing a list of asynchronous features and an anomaly coupling diagram. During the evaluation process, it introduces a breathing-style micro-expansion control and an inverse time interval alternation method, enabling the time response rhythm to adaptively adjust to changes in temperature and humidity. Through this dynamic adjustment mechanism, the evaluation system can automatically correct asynchronous phenomena caused by response lag, ensuring that the evaluation results of freshness and spoilage remain continuous and stable under fluctuating environments, thereby improving the accuracy and environmental adaptability of quality evaluation. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0051] Figure 1 This is a flowchart of a method for evaluating pork quality in cold chain transportation according to the present invention.

[0052] Figure 2 This is a schematic diagram of a pork quality assessment system for cold chain transportation according to the present invention. Detailed Implementation

[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0054] This invention provides, for example Figure 1 The method for assessing pork quality for cold chain transportation, as shown, includes the following steps:

[0055] Collect various types of data reflecting changes in pork quality throughout the entire cold chain transportation process, establish a unified time series list, and mark the fluctuation trends of various data with corresponding changes in temperature and humidity to generate dynamic data drafts that can be used for subsequent analysis.

[0056] To monitor the real-time changes in pork quality throughout the cold chain transportation process, and to ensure the consistency and comparability of different data types over time, a multi-source data fusion approach is adopted, utilizing continuous data collection, time correlation, feature tagging, and draft generation. The specific implementation steps are as follows:

[0057] Comprehensive data collection across multiple data types is conducted throughout the entire cold chain transportation process. This collection covers the entire low-temperature environment from slaughter to the transportation terminal, synchronously recording the physicochemical properties and environmental conditions reflected in changes in pork quality. Physicochemical properties include the pork's pH value, moisture content, volatile basic nitrogen value, conductivity, and color parameters; environmental conditions include temperature, humidity, airflow velocity, and spatial temperature differences within the refrigerated container. During the collection process, continuous sampling intervals are set to achieve equidistant acquisition of various data types along the time axis, ensuring a complete correspondence between physicochemical and environmental data at each sampling moment. The response latency of different sensing units is also considered during the collection process, and a time-series alignment strategy ensures logical consistency of data at the same time points, providing a unified reference basis for various data types in the time dimension. In this way, the pork state information at each moment during cold chain transportation is mapped one-to-one with the external environmental state, providing raw input for subsequent time series construction.

[0058] After obtaining various types of raw data, a unified time series model was constructed. The time series construction process used the cold chain transportation timeline as the main thread, arranging the collected physicochemical indicators and environmental parameters in chronological order to form a continuous time series data chain. During this process, all types of data were uniformly sorted according to their collection timestamps, and interpolation was performed as needed to ensure the continuity of each indicator on the timeline. For inconsistencies caused by differences in collection cycles, adjustments were made by standardizing the time granularity to ensure that indicators such as pH, volatile basic nitrogen, moisture content, temperature, and humidity corresponded at the same time step. Simultaneously with sequence construction, transportation stage information, such as loading, initial cooling, constant temperature transportation, and unloading during transit, was added to each data point to achieve a multi-dimensional mapping between data and transportation status. The final time series list not only includes the values ​​of each indicator but also the data source, collection time, transportation stage, and environmental parameters at the time of collection, thus forming an information structure with complete semantics.

[0059] After the time series inventory is constructed, the fluctuation trends of various data are mapped to corresponding changes in temperature and humidity. This mapping process, based on the time series, compares the dynamic changes of physicochemical indicators with environmental fluctuations point by point, identifying the response of each indicator at temperature or humidity change nodes. For example, when temperature fluctuates briefly, the changing trends and directions of pH, volatile basic nitrogen, and moisture content within that time period are mapped; when humidity changes, the time difference between changes in surface moisture content and internal moisture distribution in meat products is mapped. Through continuous time mapping, the response delay, magnitude of change, and trend pattern of each indicator under environmental disturbances can be observed. To avoid data isolation, the mapping process not only records changes at the current time point but also segments the fluctuation trends of continuous time periods, forming a set of "trend segments." Each trend segment includes a time interval, direction of change, rate of change, and correspondence with temperature and humidity nodes. This mapping method can comprehensively describe the coupling relationship between pork quality and environmental fluctuations at different stages of the cold chain, enabling subsequent analysis to be conducted dynamically on a trend-by-trend basis.

[0060] After trend labeling is completed, a dynamic data draft is generated for subsequent analysis. This dynamic data draft uses a time-series list as its core structure and trend labeling results as an extension layer, forming a composite information set that includes both raw data and reflects changing trends. During draft generation, various physicochemical indicators and environmental parameters are integrated along the time dimension, with each time point containing numerical records, trend descriptions, and corresponding environmental labels. Through this integration, the dynamic data draft can not only show the trajectory of pork quality changes throughout the cold chain transportation process but also demonstrate the dynamic correspondence between various indicators and environmental conditions. Once the draft is generated, a continuous and traceable data system is formed, covering the entire cold chain transportation process in terms of time span, encompassing both quality and environmental attributes in its data dimensions, and featuring both static measurements and dynamic trends in its information structure. Using this draft, subsequent quality assessment can directly analyze the evolution of pork freshness and potential risk characteristics from three dimensions: time, environment, and quality.

[0061] The dynamic data draft was sorted out for time differences, and synchronous change segments of pH value and volatile basic nitrogen were extracted. Segments that did not have synchronous characteristics were separately marked and summarized into a list of asynchronous characteristics, which provided a basis for subsequent difference identification.

[0062] This study extracts temporal correlation information on pork quality changes from dynamic data archives, fully expressing the relationship between pH value and volatile basic nitrogen changes over time. Asynchronous fluctuation segments are independently identified and categorized. Through multi-layered time analysis, synchronous segment extraction, feature segment annotation, and difference list summarization, a complete dynamic feature identification process is constructed. The specific implementation steps are as follows:

[0063] The dynamic data archive was analyzed to identify temporal differences, clarifying the response rhythm and time correspondence of pH and volatile basic nitrogen during continuous cold chain transportation. Specifically, all time points recorded in the dynamic data archive were selected, and the included data on pH, volatile basic nitrogen, moisture content, temperature, and humidity were unfolded chronologically. Continuous variation intervals within each time period were segmented and compared. By comparing the direction, magnitude, and trend of change of the two indicators in adjacent time periods, the synchronicity of their changes on the time axis was identified. For example, if volatile basic nitrogen also shows an upward trend in the same time interval when pH increases over time, then that interval is considered to exhibit synchronous change characteristics; if the two change in opposite directions or there is a time delay, it is considered asynchronous. To ensure the comprehensiveness of the temporal difference analysis, the continuous time period was divided into multiple fixed-length sub-intervals during the analysis. Each sub-interval was independently judged, and the judgment results were recorded in a time difference comparison table, thus forming an overview of the difference distribution describing the temporal relationship of each indicator, providing a foundation for the next step of extracting synchronous segments.

[0064] After analyzing the time differences, synchronous change segments were extracted from the dynamic data archives, focusing on intervals exhibiting temporal consistency. This process involved sequentially searching a time difference comparison table to identify continuous intervals where pH and volatile basic nitrogen showed the same trend and direction of change within the same time period. These intervals were defined as synchronous change segments. To ensure the continuity and integrity of synchronous segments, adjacent time periods were assessed for continuity during extraction. When the changes in indicators between two time periods remained continuous and their rates of change were similar, they were merged into a single, complete synchronous segment. Simultaneously, the start time, end time, average change amplitude, and corresponding temperature and humidity environmental parameters were recorded for each synchronous segment, forming a set of data segments reflecting the overall trend of pork quality changes under specific cold chain conditions. These synchronous segments reveal the coordinated stages of pork quality changes during cold chain transportation, illustrating the inherent law of pH and volatile basic nitrogen responding to changes in the external environment, and providing a basis for identifying normal quality change processes.

[0065] After the synchronous segments were extracted, time intervals lacking synchronous characteristics were individually labeled. This process was based on time intervals not included in the synchronous segments in the time difference comparison table, analyzing each asynchronous segment segment by segment. For each asynchronous segment, it was first determined whether the changes in pH and volatile basic nitrogen were opposite, or whether a time delay response phenomenon existed. Then, based on the duration and magnitude of the change delay, these characteristics were recorded as asynchronous feature information. During the labeling process, temperature and humidity change data from the dynamic data archive were also incorporated to correlate the time periods of asynchronous phenomena with environmental fluctuation nodes, marking whether the asynchronous interval was related to external temperature abrupt changes, humidity fluctuations, or changes during transportation. For example, if the pH value increased while volatile basic nitrogen remained constant, and this phenomenon occurred during a short-term temperature increase, then this time period was marked as a temperature-induced asynchronous feature interval. Through this segment-by-segment labeling method, asynchronous features were recorded in detail in the form of time, direction of change, degree of delay, and environmental correspondence, laying a data foundation for the systematic summarization of asynchronous features.

[0066] After completing the annotation of asynchronous features, all segments lacking synchronous features were compiled into an asynchronous feature list. This list, organized chronologically, systematically organizes the start time, end time, direction of change, duration of delay, corresponding environmental change type, and related stage information for each asynchronous segment. To facilitate subsequent difference identification, the list is arranged chronologically, and segments with similar asynchronous features within adjacent time periods are merged, creating a continuous set of similar changes at the data level. During list generation, synchronous and asynchronous segments are also compared and labeled, allowing the entire time series to simultaneously display the alternating distribution of both types of change features, thus constructing a complete temporal correlation map. The formation of the asynchronous feature list not only reflects the temporal response differences in pork quality changes but also reveals the relationship between these differences and external environmental disturbances, providing fundamental information for subsequent difference identification and anomaly analysis.

[0067] Based on the asynchronous feature list, backtrack time series data to identify time segments that continuously shift during short-term fluctuations. The obtained segments are mapped to temperature and humidity change nodes to construct an anomaly coupling diagram, which is used to characterize the potential sources of quality shifts.

[0068] This study reveals the intrinsic relationship between pork quality changes and environmental conditions from an asynchronous feature list. Backtracking analysis of time series data identifies time segments exhibiting sustained shifts within short-term fluctuations. These time segments are then mapped one-to-one with temperature and humidity change nodes, thereby constructing an anomaly coupling diagram that reflects the potential sources of quality shifts. The specific implementation steps are as follows:

[0069] The asynchronous feature list is back-imported to determine its specific location range in the original time series. This process uses the start and end times recorded in the asynchronous feature list as indices, mapping them to the timeline in the dynamic data draft to restore the positional distribution of asynchronous segments in the original data sequence. To ensure the accuracy of the time mapping, consecutive asynchronous segments in the list are time-merged; when the time interval between adjacent segments is less than a preset time step, they are treated as the same continuous event and uniformly identified. In this way, a direct mapping relationship is established between the asynchronous feature list and the time series data, allowing the contextual information of each asynchronous segment in the original sequence to be restored. After completing the time mapping, data such as pH value, volatile basic nitrogen, moisture content, temperature, and humidity within the time interval of each asynchronous segment are extracted to form a local time series set with time range identifiers, providing a data foundation for subsequent fluctuation identification.

[0070] Fluctuation backtracking analysis is performed on local time series extracted from time series data to identify time segments exhibiting persistent shifts within a short timeframe. This process extends forward and backward over a certain time range, centered on asynchronous characteristic segments, tracking the changes in pH and volatile basic nitrogen, and recording their direction and duration. By comparing the trends of adjacent time points, it is determined whether there is a continuous upward or downward trend in quality indicators within the segment. If this trend persists over a certain time span without being interrupted by short-term fluctuations in environmental parameters, the time interval is identified as a persistent shift segment. During the identification process, the changes in temperature and humidity within this time range are also recorded to determine whether the shifts in quality indicators have a temporal overlap or lag relationship with environmental factors. For example, if pH continuously rises while temperature experiences a short-term increase in the preceding stage, the shift may be related to heat accumulation or uneven local heat exchange; if volatile basic nitrogen continuously rises while humidity is unstable, it may be related to surface moisture evaporation or active internal metabolism. In this way, the potential causal relationship between quality indicators and environmental changes can be revealed over time.

[0071] The identified persistent offset time segments are correlated with temperature and humidity change nodes to establish a mapping relationship between them. In this process, using change nodes in the environmental data sequence as reference points, each persistent offset segment is paired with temporally adjacent temperature and humidity change nodes. The time interval, offset direction, and duration of each pairing are recorded. When the start time of the offset segment highly overlaps with the temperature change node, the relationship is marked as a direct correspondence; when the offset segment occurs later than the temperature change node by a certain duration, it is marked as a delayed correspondence; if the change direction of the offset segment is consistent with the humidity change trend, it is marked as a co-directional correspondence. Through this hierarchical correspondence method, a time-matching table between quality changes and environmental factors is constructed, ensuring that each persistent offset segment has a clear environmental reference in the time dimension. Furthermore, to enhance data traceability, the transportation stage information, such as the constant temperature maintenance stage, environmental switching stage, or loading / unloading stage, is also recorded in each correspondence, enabling the differentiation of the background of the offset phenomenon during analysis. Through this correspondence process, quality changes in the time series are effectively associated with specific environmental change nodes, providing a causal basis for subsequent anomaly coupling construction.

[0072] An anomaly coupling diagram was constructed based on time correspondence to characterize the potential sources of quality deviations. The diagram uses time as the horizontal axis and changes in quality indicators and environmental parameters as the vertical axes, visually arranging continuous deviation segments and temperature and humidity change nodes in chronological order. Each continuous deviation segment is represented as an independent trajectory in the diagram and connected to the corresponding environmental change node by lines, forming multiple intersecting lines to visually demonstrate the coupling characteristics between quality and environmental changes. During construction, each coupling relationship was categorized and labeled according to its time delay type and direction of change, allowing different types of anomalies to be distinguished in the diagram. This method clearly shows which stages in the cold chain transportation process exhibit asynchronous phenomena between quality indicators and environmental parameters, and the correspondence between these asynchronous phenomena and temperature fluctuations, humidity abrupt changes, or stage transitions. The anomaly coupling diagram not only reflects anomalies at single time points but also demonstrates dynamic deviation trajectories over continuous time periods, thus revealing the potential driving factors of pork quality changes.

[0073] By using anomaly coupling diagrams to conduct comparative analysis of indicators, extracting the cross-change patterns between indicators, tracking the direction and duration of change over a continuous time period, and generating a change coupling draft, this can be used to locate key turning points in pork quality trends.

[0074] To further reveal the temporal patterns of pork quality changes and the interactions among multiple indicators from the anomaly coupling diagram, a systematic comparative analysis of various physicochemical indicators and environmental parameters was conducted. A draft change coupling diagram was generated through cross-variation feature extraction, continuous time period tracking, and change pattern reconstruction to identify key turning points in quality change trends. The specific implementation steps are as follows:

[0075] The temporal relationships and indicator change trajectories contained in the anomaly coupling plot are layered and expanded to determine the range of indicators and time baseline for comparative analysis. This process uses pH, volatile basic nitrogen, moisture content, temperature, and humidity recorded in the anomaly coupling plot as core indicators, rearranging the change curves in chronological order to ensure a complete cross-reference basis on the same time axis. To ensure the continuity of the analysis, the change trajectory of each indicator is segmented, extending a certain time span forward and backward from each anomaly event to extract a complete data interval containing the changes before and after the environmental disturbance. Overlapping time segments are merged, ensuring that each time segment contains at least one environmental parameter change and two quality parameter responses, guaranteeing that the comparative analysis can be conducted within a complete context. In this way, local events in the anomaly coupling plot are restored to multi-dimensional change scenarios within continuous time periods, laying a time-series foundation for subsequent cross-pattern extraction.

[0076] Cross-variation comparisons were performed on the time-expanded anomaly coupling graph data to identify coupling relationships and common change characteristics among multiple indicators. This process uses time as the main thread, analyzing the direction and rate of change of each indicator within the same time interval segment by segment. By comparing the changing trends of different indicators, cross-variation relationships with synchronicity, reciprocity, or lag were extracted. Specifically, when pH and volatile basic nitrogen show changes in the same direction within the same time interval, it is determined to be a positive coupling relationship; when they are in opposite directions, it is determined to be a negative coupling relationship; when one indicator's change lags behind another, it is recorded as a delayed coupling relationship. To ensure the continuous expression of cross-variation relationships, temporally adjacent segments with consistent change characteristics were merged, and the duration of the coupling and the corresponding environmental parameter status were recorded. In this process, the direction and magnitude of environmental parameter changes were also included in the comparison, so that each cross-variation not only reflects the correlation between quality indicators but also reflects their response to environmental changes. For example, when temperature fluctuations cause pH to rise first and then fall, while volatile basic nitrogen continues to rise, this interval is identified as a temperature-driven negative coupling relationship. By comparing each segment in this way, a set of basic relationships describing the interactive characteristics of multiple indicators is formed.

[0077] After obtaining the cross-relation set, the direction and duration of changes within consecutive time periods are tracked to reveal the dynamic evolution of pork quality changes. This process, based on the cross-relation set, connects each time segment in chronological order, forming a temporal chain from environmental disturbance to quality response. During the analysis, the duration of the change direction of each quality indicator is tracked, and it is determined whether this persistence extends to the next time stage. When the change direction of an indicator remains consistent across multiple consecutive time periods, it is considered to be in a stable trend phase; when the change direction reverses, it is marked as a trend inflection point. Simultaneously, the rate and phase of change of temperature and humidity within the same time interval are recorded to determine whether quality trend changes are synchronized with environmental changes. When the change directions of pH and volatile basic nitrogen cross and reverse, and the reversal point is adjacent to the change node of environmental parameters, it is determined to be a critical response interval of the quality trend. In this process, by tracking both persistence and direction, short-term fluctuations and long-term trends of quality indicators can be distinguished, allowing for a complete characterization of the quality evolution process in terms of temporal continuity.

[0078] After completing cross-comparison and trend tracking, a change coupling draft was generated to pinpoint key turning points in pork quality trends. This draft uses time as its main axis and the cross-change relationships of various indicators as its main thread, integrating the coupling characteristics between quality parameters and environmental parameters in chronological order to form a temporal framework that comprehensively reflects quality change trends. During the draft's construction, each cross-relationship and its corresponding environmental background were marked on a unified timeline, forming a two-layered comparison structure of quality indicators and environmental parameters. For time segments with multiple coupling relationships, the cross-trajectories between different indicators were displayed through overlay to reveal their overlap and turning points in the time dimension. Each turning point is accompanied by information on the duration before and after the change, the direction of change, the type of environmental fluctuation, and related transportation stages, thus ensuring the draft's temporal completeness and semantic relevance. The generation of the change coupling draft not only achieves a structured expression of the multi-indicator change process but also provides a foundation for subsequent dynamic adjustments. Through this draft, the key time periods for the transition of pork quality from a fresh state to a deteriorating state can be clearly identified, and the corresponding patterns between environmental disturbances and quality changes can be clarified, thus providing logical support for the dynamic weight allocation and response rhythm adjustment in the subsequent quality assessment stage.

[0079] Based on the change coupling draft, dynamic adjustment operations are performed, breathing micro-expansion control is implemented on the time response rhythm of the quality assessment process, and an inverse time interval rotation method is introduced to enable the response step of freshness and spoilage degree to automatically adjust with temperature and humidity changes, thereby achieving adaptive repair and continuous stable assessment of asynchronous phenomena.

[0080] To ensure the pork quality assessment process maintains continuous adaptability and dynamic equilibrium in response to environmental changes, dynamic adjustment operations are implemented based on the change coupling draft. This involves gradually adjusting the time response rhythm and alternating control of reverse intervals to achieve adaptive repair of asynchronous phenomena and improve assessment stability. The specific implementation steps are as follows:

[0081] Based on the draft change coupling protocol, an initial time response rhythm for the quality assessment process was set to determine the basic assessment cycle and time step. This process uses the inflection points, durations, and environmental change nodes of quality indicators described in the draft change coupling protocol as references, dividing the entire cold chain transportation cycle into multiple consecutive time stages. Each time stage corresponds to a specific assessment rhythm interval, used to guide the response rate of subsequent quality assessment processes. In the initial setting, the frequency of changes in multi-dimensional parameters such as pH, volatile basic nitrogen, moisture content, temperature, and humidity was used as the basis for adjusting the time step, ensuring that the response cycles of each parameter remain relatively coordinated over time. In this way, the quality assessment process establishes a basic rhythm adapted to environmental changes in the time dimension, enabling the assessment behavior to be updated synchronously with the dynamic environmental changes during cold chain transportation. After completing the initial rhythm setting, a time response framework is formed, providing a rhythm baseline for the implementation of breathable micro-expansion control.

[0082] After the time response rhythm is set, a breathing-style micro-expansion control operation is executed to achieve continuous expansion and contraction of the assessment time step, thereby giving the assessment process a flexible response characteristic. The implementation of breathing-style micro-expansion control is based on the time response framework. By extending and contracting the step between adjacent assessment cycles with small amplitudes, the time resolution of the quality assessment remains balanced within a stable range. When the cold chain transportation environment maintains a constant temperature and quality indicators tend to stabilize, the time step is slightly expanded from the original rhythm to reduce data fluctuations caused by frequent responses. When temperature or humidity fluctuates, the time step automatically contracts to a more frequent interval to improve the responsiveness to quality changes. Through this periodic breathing-style control, the quality assessment process can automatically adjust its time rhythm in different environmental stages, ensuring that the continuity of the assessment results is coordinated with the dynamics of environmental changes. Throughout this process, the amplitude of the breathing-style expansion and contraction is always constrained by the trend information in the change coupling draft, ensuring that the adjustment of the assessment rhythm is always consistent with the actual quality change process.

[0083] After implementing the breathing-style micro-expansion control, a reverse-phase time interval rotation method is introduced to balance the response steps of freshness and spoilage degree during the assessment process, enabling their changing trends to dynamically complement each other over time. This process uses the historical change direction of the quality indicators as a reference, rotating the time steps in reverse within adjacent periods, alternating the assessment intervals for freshness and spoilage degree indicators. When the freshness indicator is in a dense sampling state in the previous period, its time step is appropriately extended in the next period; correspondingly, when the spoilage degree indicator is in a delayed sampling state in the previous period, its time step is shortened in the next period. This reverse rotation mechanism achieves a dynamic balance in the time response of the two indicators, thus avoiding time deviations caused by a single indicator dominating the assessment process. To maintain overall assessment consistency, the rate of change of environmental parameters is also considered during the implementation of the reverse rotation. When the rate of temperature increase exceeds a certain threshold, the response interval for freshness automatically tightens to enhance sensitivity; when humidity remains stable, the response interval for spoilage degree is appropriately widened to maintain assessment stability. In this way, the timing of quality assessment develops a self-correcting characteristic amidst the dynamic changes in the cold chain environment, ensuring that the assessment results remain stable and continuous under the interaction of multiple indicators.

[0084] After completing the reverse time interval rotation, an overall dynamic adjustment operation is executed, integrating the results of the breathing-style micro-expansion control with the reverse time interval rotation method to form a continuous and stable quality assessment rhythm. This process uses the time series of the entire cold chain transportation process as the main thread, sequentially connecting the time response adjustment results of each stage to ensure a seamless transition in the assessment rhythm over time. By comparing the step size changes of adjacent time periods, the equilibrium and offset intervals of the time response are identified. Within the offset interval, the breathing-style contraction mode is preferentially used to increase the response frequency, while within the equilibrium interval, the rotation mode is maintained to preserve assessment continuity. Simultaneously, the trends in temperature and humidity are used as reference conditions for fine-tuning the assessment rhythm. When the environmental change trend is stable, the current step size is maintained; when the environmental change trend fluctuates, periodic time interval compensation is performed, allowing the assessment cycle to automatically adapt to environmental disturbances. In this process, the quality assessment process forms a closed-loop control structure driven by the change coupling draft logic, with time rhythm adjustment as the core mechanism, and environmental change feedback as a dynamic correction method. The resulting quality assessment timeline maintains an adaptive balance throughout the cold chain transportation process, ensuring that the assessment outputs of freshness and spoilage remain continuous and stable under changing environmental conditions, thus guaranteeing that changes in pork quality can be continuously tracked and accurately reflected.

[0085] This invention establishes a unified time-series list of multiple data types throughout the cold chain transportation process and marks the fluctuation trends of physicochemical indicators and environmental parameters accordingly. This enables dynamic correlation and continuous tracking of quality data, allowing the evaluation results to reflect the quality changes of pork during transportation in real time. This multi-source data time alignment method avoids misjudgments based on single indicators, transforming quality assessment from discrete sampling to continuous monitoring. This improves the completeness of data representation and the timeliness of assessment, ensuring dynamic visibility and real-time controllability of the quality change process.

[0086] This invention identifies the dynamic response relationship between quality indicators and environmental factors by constructing a list of asynchronous features and an anomaly coupling diagram. During the evaluation process, it introduces a breathing-style micro-expansion control and an inverse time interval alternation method, enabling the time response rhythm to adaptively adjust to changes in temperature and humidity. Through this dynamic adjustment mechanism, the evaluation system can automatically correct asynchronous phenomena caused by response lag, ensuring that the evaluation results of freshness and spoilage remain continuous and stable under fluctuating environments, thereby improving the accuracy and environmental adaptability of quality evaluation.

[0087] This invention provides, for example Figure 2 The illustrated pork quality assessment system for cold chain transportation includes a data acquisition and time series construction module, a synchronization relationship identification module, an anomaly coupling construction module, a change feature analysis module, and a dynamic adaptive adjustment module.

[0088] The data acquisition and time series construction module collects various types of data reflecting changes in pork quality throughout the cold chain transportation process, establishes a unified time series list, marks the fluctuation trends of various data with corresponding changes in temperature and humidity, and generates dynamic data drafts that can be used for subsequent analysis.

[0089] The synchronization relationship identification module sorts out the time differences in the dynamic data draft, extracts the synchronous change segments of pH value and volatile basic nitrogen, and separately marks the segments that do not have synchronous characteristics and summarizes them into a list of asynchronous characteristics, providing a basis for subsequent difference identification.

[0090] The anomaly coupling construction module backtracks time series data based on the asynchronous feature list, identifies time segments that continuously shift during short-term fluctuations, maps the acquired segments to temperature and humidity change nodes, and constructs an anomaly coupling diagram to characterize the potential sources of quality shifts.

[0091] The change feature analysis module uses anomaly coupling diagrams to conduct comparative analysis of indicators, extract the cross-change patterns between indicators, track the direction and duration of change over a continuous time period, and generate a change coupling draft to locate key turning points in pork quality trends.

[0092] The dynamic adaptive adjustment module performs dynamic adjustment operations according to the change coupling draft, implements breathing-style micro-expansion control on the time response rhythm of the quality assessment process, and introduces an inverse time interval rotation method to automatically adjust the response step of freshness and spoilage degree with changes in temperature and humidity, thereby achieving adaptive repair and continuous stable assessment of asynchronous phenomena.

[0093] The present invention provides a method for evaluating pork quality for cold chain transportation, which is implemented through the above-mentioned pork quality evaluation system for cold chain transportation. For details of the specific method and process of the pork quality evaluation system for cold chain transportation, please refer to the embodiment of the above-mentioned method for evaluating pork quality for cold chain transportation, which will not be repeated here.

[0094] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating pork quality for cold chain transportation, characterized in that, Includes the following steps: Collect various types of data reflecting changes in pork quality throughout the entire cold chain transportation process, establish a unified time series list, and mark the fluctuation trends of various data with corresponding changes in temperature and humidity to generate dynamic data drafts. The dynamic data draft was sorted out for time differences, and synchronous change segments of pH value and volatile basic nitrogen were extracted. Segments that do not have synchronous characteristics were separately marked and summarized to form a list of asynchronous characteristics. Specifically, this includes: sorting out the differences in dynamic data drafts over time, comparing pH, volatile basic nitrogen, moisture content, temperature and humidity data in segments according to time nodes, and identifying the relationship between the two changes over time. From the time difference analysis results, the intervals in which pH value and volatile basic nitrogen change in the same time period are extracted and defined as synchronous change segments. If the two change in opposite directions or there is a time delay, they are asynchronous change segments. The start and end times, change amplitude and corresponding temperature and humidity parameters are recorded. Time intervals that do not have synchronous characteristics are marked, and the direction of change, duration of delay, and correspondence with temperature and humidity fluctuations are recorded. All asynchronous segments are summarized to generate an asynchronous feature list, recording the start time, end time, direction of change, and corresponding environmental information of each segment with time as the main axis. Based on the asynchronous feature list, backtrack time series data to identify time segments that continuously shift during short-term fluctuations, and match the acquired segments with temperature and humidity change nodes to construct an anomaly coupling diagram; Specifically, this includes: retrospectively importing the list of asynchronous features to determine the location and range of asynchronous segments in the time series, and extracting data such as pH value, volatile basic nitrogen, moisture content, temperature and humidity to form a local time series set; Fluctuation backtracking analysis is performed on local time series sets to identify time segments that exhibit continuous shift characteristics within a short time range, and the direction of change, duration, and temperature and humidity change characteristics are recorded. The specific process for identifying time segments exhibiting continuous shift characteristics within a short time range is as follows: Extending forward and backward over a certain time range centered on the asynchronous characteristic segment, track the changes in pH value and volatile basic nitrogen, and record their direction and duration. By comparing the trends of adjacent time nodes, determine whether there is a continuous upward or downward trend in the quality indicators within the segment. If this trend persists within a certain time span and is not interrupted by short-term fluctuations in environmental parameters, then the time interval is identified as a continuous shift segment. The continuous offset time segments are correlated with temperature and humidity change nodes to establish a mapping relationship between time interval, offset direction and duration. An anomaly coupling diagram is constructed based on the time correspondence, with time as the horizontal axis and changes in quality indicators and environmental parameters as the vertical distribution, and the coupling relationship types are classified and labeled. By using anomaly coupling diagrams to conduct comparative analysis of indicators, extracting the cross-change patterns between indicators, tracking the direction and duration of change over a continuous time period, and generating a draft of change coupling; Specifically, this includes: performing layered analysis on the time relationships and index change trajectories in the abnormal coupling diagram, determining the comparative analysis range of pH value, volatile basic nitrogen, moisture content, temperature and humidity, and extracting continuous data intervals that include the process before and after the environmental disturbance. Cross-variation comparison was performed on the time-expanded anomalous coupling plot data to extract the forward, reverse, and delayed coupling relationships between pH value and volatile basic nitrogen within the same time interval, and the direction of change, duration, and environmental conditions were recorded. Track the direction and duration of changes over a continuous period of time to identify the trend stages and turning points of indicator changes; A draft of the change coupling was generated, which integrates the cross-relationship of multiple indicators with environmental parameters in chronological order to form a time-series framework for locating key turning points in pork quality trends. Based on the change coupling draft, dynamic adjustment operations are performed, and breathing micro-expansion control is implemented on the time response rhythm of the quality assessment process. An inverse time interval rotation method is introduced so that the response step of freshness and spoilage degree is automatically adjusted with changes in temperature and humidity. Specifically, this includes: setting the initial time response rhythm of the quality assessment process based on the draft of the change coupling, dividing the time phases according to the turning points of quality indicators, duration and environmental change nodes, and determining the assessment cycle and time step; Implement breathing-style micro-expansion control, expanding and contracting the step size between adjacent evaluation cycles to keep the time resolution of quality evaluation consistent with environmental changes; Specifically, when the cold chain transportation environment maintains a constant temperature and the quality indicators tend to change steadily, the time step is slightly expanded on the original rhythm to reduce data fluctuations caused by frequent responses; when temperature or humidity fluctuates, the time step is automatically reduced to a more dense interval to improve the responsiveness to quality changes. An inverse time interval alternation method is introduced to alternately adjust the response step size for freshness and spoilage, and to fine-tune the response interval based on the rate of change of temperature and humidity. Specifically, the process uses the historical trend of quality indicators as a reference and rotates the time step in opposite directions within adjacent periods, so that the evaluation interval of freshness indicators and spoilage indicators alternates. When the freshness indicator is in a state of intensive sampling in the previous period, its time step is appropriately extended in the next period. Correspondingly, when the spoilage indicator is in a state of delayed sampling in the previous period, its time step is shortened in the next period. By integrating the results of breathing-style micro-expansion control and the reverse phase time interval rotation, a continuous and stable quality assessment rhythm is formed.

2. The method for evaluating pork quality for cold chain transportation according to claim 1, characterized in that, The steps for generating dynamic data drafts are as follows: Collect various types of data throughout the entire cold chain transportation process, including the pH value, moisture content, volatile basic nitrogen value, conductivity, color parameters, temperature, humidity, and airflow velocity of pork. Simultaneous recording of physicochemical properties and environmental conditions is achieved at continuous sampling intervals. The collected data are sorted in chronological order to create a time series list. The collection period of different indicators is adjusted by uniform time granularity so that pH value, volatile basic nitrogen, moisture content, temperature and humidity have corresponding relationships at the same time step. The changes in physicochemical indicators in the time series list are mapped to changes in temperature and humidity to form a set of trend segments; By integrating time series lists and trend segment sets, a dynamic data draft is generated to reflect the dynamic coupling relationship between pork quality change trajectory and environmental conditions.

3. The method for evaluating pork quality for cold chain transportation according to claim 1, characterized in that, The list of asynchronous features is arranged in chronological order, and segments with the same asynchronous features within consecutive time periods are merged to form continuously distributed asynchronous feature intervals. At the same time, synchronous change segments are compared and labeled with asynchronous segments to construct a time-series correlation map reflecting the relationship between quality changes and environmental fluctuations.

4. The method for evaluating pork quality for cold chain transportation according to claim 1, characterized in that, When constructing the anomaly coupling graph, the continuous offset time segments are classified and labeled according to the time delay type and the direction of change. The offset segments that overlap with the temperature change node are marked as direct correspondences, the offset segments that have time lags are marked as delayed correspondences, and the offset segments with the same direction of change are marked as co-directional correspondences, thereby forming a multi-dimensional time correlation relationship in the anomaly coupling graph.

5. The method for evaluating pork quality for cold chain transportation according to claim 1, characterized in that, When generating the draft of the change coupling, each cross relationship is labeled with the corresponding environmental fluctuation type and transportation stage information, and a two-layer comparison structure of quality indicators and environmental parameters is established on the timeline. The overlapping and turning points of the cross trajectories of multiple indicators are displayed by overlay.

6. A pork quality assessment system for cold chain transportation, used to implement the pork quality assessment method for cold chain transportation as described in any one of claims 1-5, characterized in that, It includes a data acquisition and time series construction module, a synchronization relationship identification module, an anomaly coupling construction module, a change feature analysis module, and a dynamic adaptive adjustment module: The data acquisition and time series construction module collects various types of data reflecting changes in pork quality throughout the cold chain transportation process, establishes a unified time series list, marks the fluctuation trends of various data with corresponding changes in temperature and humidity, and generates dynamic data drafts. The synchronization relationship identification module sorts out the time differences in the dynamic data draft, extracts the synchronous change segments of pH value and volatile basic nitrogen, and separately marks the segments that do not have synchronous characteristics and summarizes them into a list of asynchronous characteristics. The abnormal coupling construction module backtracks time series data based on the asynchronous feature list, identifies time segments that continuously shift during short-term fluctuations, and maps the acquired segments to temperature and humidity change nodes to construct an abnormal coupling graph. The change feature analysis module uses anomaly coupling diagrams to conduct comparative analysis of indicators, extract the cross-change patterns between indicators, track the direction and duration of change over a continuous time period, and generate a change coupling draft. The dynamic adaptive adjustment module performs dynamic adjustment operations according to the change coupling draft, implements breathing-style micro-expansion control on the time response rhythm of the quality assessment process, and introduces an inverse time interval rotation method to automatically adjust the response step of freshness and spoilage degree with changes in temperature and humidity.

Images