Agricultural whole industry chain tracing method and system based on big data

Abstract

The invention discloses an agricultural whole industrial chain traceability method and system based on big data, particularly relates to the technical field of agricultural Internet of Things and intelligent decision making, and is used for solving the problems of cause and effect chain breakage, damage-causing factor positioning deviation and insufficient traceability reliability caused by neglecting irreversibility of biological growth in the existing agricultural traceability system. The method comprises the following steps: dividing time sequence data fragments based on an agricultural growth stage, restoring a time sequence rule of crop growth, and dynamically marking a time window of a key abnormal event so as to lock a damage-causing action interval; quality inspection and transportation parameters are mapped into a system trajectory by combining a phase-space reconstruction technology, and a target growth stage associated with a biological dynamic rule is screened; through contribution weight analysis and stress resistance data fusion, identifying a main cause damage factor and a synergistic effect relationship thereof; and finally, the tracing path is dynamically corrected based on a historical verification result, and the threshold value and the weight of the agricultural rule base are optimized, so that the accuracy and the credibility of agricultural whole industry chain tracing are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural Internet of Things and intelligent decision-making, and more specifically, to a method and system for tracing the entire agricultural industrial chain based on big data. Background Art

[0002] Currently, the agricultural traceability system generally uses technical means such as Internet of Things sensors, blockchain, and two-dimensional codes to collect and store data on various links of agricultural product production, processing, logistics, etc. By deploying field sensors, equipment log recording systems, and consumer-side code scanning query functions, a digital traceability framework covering the entire chain has been constructed. For example, environmental parameters such as soil temperature and humidity and meteorological data in the production link can be transmitted to the cloud in real time, and the quality inspection reports in the processing link and the temperature and humidity records in the logistics link are ensured to be tamper-proof through blockchain technology. Consumers can view the full life cycle information of the product by scanning the two-dimensional code.

[0003] However, the prior art does not fully consider the essential characteristics of the irreversible growth of agricultural organisms, resulting in a fundamental contradiction between the data backtracking logic and biological laws. Specifically, it is manifested as follows: The growth process of crops has time unidirectionality (for example, the growth period state cannot be traced back after harvesting), which will cover up the details of key growth events (such as the critical window period affected by extreme weather). When the product quality is abnormal, it is impossible to accurately locate the loss-causing factors in the production link (such as the lack of irrigation or the outbreak of pests and diseases during a specific growth period). The data storage logic is disjointed from the dynamic laws of biological growth, ultimately leading to the deviation of the traceability conclusion from the true causal chain and reducing the accuracy and credibility of the entire industrial chain traceability. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for tracing the entire agricultural industrial chain based on big data to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for tracing the entire agricultural industrial chain based on big data, including the following steps: S1. Obtain the original data of the entire agricultural industrial chain, including crop growth parameters, environmental parameters, quality inspection parameters, and transportation environment parameters; S2. Divide the crop growth parameters and environmental parameters according to the agronomic growth stage to generate time series data segments for each stage; S3. Dynamically mark key abnormal events and time windows in the time series data segments based on the key event thresholds in the agronomic rule library; S4. Reconstruct the quality inspection parameters and transportation environment parameters into a dynamic system trajectory through phase space, and generate a trajectory similarity index based on the reference trajectory predefined in the agronomic rule library to screen the target growth stage; S5. Analyze the superposition effect of key abnormal events in the target growth stage to generate the contribution weights of each key abnormal event, and identify the main loss-causing factors and synergistic factors in combination with crop stress resistance data; S6. Modify the traceability path according to the contribution weight distribution of the main loss-causing factors, and reverse-verify and iteratively update the key event thresholds and contribution weights in the agronomic rule library.

[0006] In a preferred embodiment, crop growth parameters are collected by Internet of Things sensors deployed in the field, including leaf surface temperature, stem diameter, and photosynthetic rate; environmental parameters include soil pH value, rainfall, and light intensity, which are obtained synchronously through weather stations and soil probes; quality inspection parameters include pesticide residue detection results and microbial indicators in the processing link, and transportation environment parameters include temperature and humidity fluctuation data of the cold chain carriage.

[0007] In a preferred embodiment, the agronomic growth stage is divided into germination stage, vegetative growth stage, reproductive growth stage, and maturity stage according to the phenological period of the target crop; crop growth parameters and environmental parameters are divided into stages according to the time intervals corresponding to the phenological periods to generate time series data segments for each stage; The time series data segments are extracted by the sliding time window algorithm, and the window length of the sliding time window is dynamically adjusted according to the biological characteristics of each growth period.

[0008] In a preferred embodiment, the key event thresholds stored in the agronomic rule library include extreme high temperature event thresholds, pest and disease outbreak thresholds, and irrigation shortage thresholds, and the key event thresholds are generated based on a historical disaster record statistical model; Dynamically marking key abnormal events in the time series data segments is achieved by comparing the real-time data stream with the key event thresholds. When the crop growth parameters or environmental parameters continuously exceed the key event thresholds for a preset duration, they are marked as key abnormal events; The time window of a key abnormal event is recorded as the start time and the end time; each key abnormal event is bound to a corresponding stage identifier.

[0009] In a preferred embodiment, the quality inspection parameters and transportation environment parameters are converted into dynamic system trajectories by the phase space reconstruction method; The trajectory similarity index of the dynamic system trajectory is generated by comparing the morphological differences between the current dynamic system trajectory and the reference trajectory predefined in the agronomic rule library; The calculation of the trajectory similarity index integrates the time window overlap ratio of key abnormal events. The time window overlap ratio is the proportion of the overlap duration between the time range of the current trajectory and the time window of key abnormal events under the same stage identifier; The screening of the target growth stage is achieved through the comprehensive determination of the difference between the trajectory similarity index and the preset similarity threshold and the time window overlap ratio. If the trajectory similarity index is lower than the preset similarity threshold and the time window overlap ratio is higher than the preset overlap ratio threshold, it is marked as the target growth stage.

[0010] In a preferred embodiment, the phase space reconstruction method adopts the delay coordinate method, and the embedding dimension and time delay are determined according to the temporal characteristics of the time series data segment. The temporal characteristics include the data sampling frequency and the parameter fluctuation period.

[0011] In a preferred embodiment, the superposition effect analysis of the key abnormal events in the target growth stage is carried out. The key abnormal events include the event type, the time window range, and the associated stage identifier; The contribution weight is generated by the linear combination of the trajectory similarity index and the time window overlap ratio. The weight coefficients of the trajectory similarity index and the time window overlap ratio are preset according to the statistical analysis of historical loss cases; The crop stress resistance data is extracted from a predefined stress resistance database, and the stress resistance database stores the drought tolerance score and disease resistance score of crop varieties; The main loss-causing factor is determined by the product of the contribution weight and the reciprocal of the stress resistance score. If the product value exceeds the preset product threshold, it is marked as the main loss-causing factor; The synergy factor is identified by calculating the time window overlap density of the key abnormal events under the same stage identifier. The time window overlap density is the proportion of the number of overlaps between events to the total number of events. When the proportion exceeds the preset density threshold, it is marked as the synergy factor; The main loss-causing factor and the synergy factor are associated with the key abnormal events, and the output data includes the event type, the contribution weight, and the associated stage identifier.

[0012] In a preferred embodiment, the traceability path correction is achieved by adjusting the priority order of the stage identifier corresponding to the main loss-causing factor in the traceability path. The priority order is arranged from high to low according to the contribution weight; The reverse verification is completed by comparing the consistency of the corrected traceability path with the loss stage of the historical true loss cases; The key event threshold of the agronomic rule base is iteratively updated based on the reverse verification result. If the consistency ratio is lower than the preset verification threshold, the threshold is recalculated according to the parameters of the key abnormal events in the actual loss stage in the historical cases; The contribution weight is iteratively updated by statistically analyzing the distribution frequency of the main loss-causing factor in each growth stage in the corrected traceability path. The weights of the factors with a distribution frequency higher than the preset frequency threshold are increased proportionally.

[0013] In a preferred embodiment, the consistency determination criterion is the coincidence ratio of the first N stage identifiers in the correction path and the actual damage stage in the historical case, and the value of N is determined according to the average number of damage stages in the historical case.

[0014] On the other hand, the present invention provides a big data-based traceability system for the entire agricultural industry chain, including the following modules: Data acquisition module: used to acquire the original data of the entire agricultural industry chain, including crop growth parameters, environmental parameters, quality inspection parameters, and transportation environment parameters; Data segmentation module: used to divide the crop growth parameters and environmental parameters according to the agronomic growth stages to generate time series data segments for each stage; Event marking module: used to dynamically mark the key abnormal events and time windows in the time series data segments based on the key event thresholds of the agronomic rule library; Trajectory analysis module: used to reconstruct the quality inspection parameters and transportation environment parameters into a dynamic system trajectory through phase space, and generate a trajectory similarity index based on the reference trajectory predefined in the agronomic rule library to screen the target growth stage; Factor identification module: used to perform superposition effect analysis on the key abnormal events in the target growth stage to generate the contribution weights of each key abnormal event, and identify the main damage-causing factors and synergistic factors in combination with the crop stress resistance data; Path optimization module: used to correct the traceability path according to the contribution weight distribution of the main damage-causing factors, and reversely verify and iteratively update the key event thresholds and contribution weights of the agronomic rule library.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. By dividing the time series data segments based on the agronomic growth stages, the irreversible biological growth process is transformed into discrete and traceable time series nodes, ensuring that the data storage logic conforms to the natural rhythm of crop growth; by dynamically marking the time windows of key abnormal events, the action intervals of damage-causing factors such as extreme weather and pests and diseases are accurately captured, avoiding the problem of causal chain breakage caused by data mixing in traditional methods; further combining the phase space reconstruction technology, the quality inspection and transportation parameters are mapped into a system trajectory, and the target growth stage is screened through trajectory similarity analysis, so that the traceability path always revolves around the key nodes of biological growth, significantly improving the accuracy of abnormal positioning; 2. Dynamically quantify the impact intensity of key abnormal events through contribution weights, and identify the main loss-causing factors and synergy factors in combination with varietal resistance differences, ensuring that the traceability conclusion reflects both external environmental impacts and the crop's own resistance characteristics; finally, optimize the agronomic rule base through closed-loop iteration, enabling the threshold setting and weight allocation to be dynamically adjusted with the accumulation of historical data, forming an intelligent traceability system that continuously adapts to actual agricultural production; solving the traceability deviation problem caused by the disconnection between static rules and dynamic growth in traditional technologies, and providing a reliable basis for precise decision-making across the entire industrial chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flowchart of the method for tracing the entire agricultural industrial chain based on big data according to the present invention; Figure 2 It is a schematic structural diagram of the system for tracing the entire agricultural industrial chain based on big data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1: Figure 1 The method for tracing the entire agricultural industrial chain based on big data according to the present invention is given, including the following steps: S1. Obtain the original data of the entire agricultural industrial chain, including crop growth parameters, environmental parameters, quality inspection parameters, and transportation environment parameters; S2. Divide the crop growth parameters and environmental parameters according to the agronomic growth stages to generate time series data segments for each stage; S3. Dynamically mark the key abnormal events and time windows in the time series data segments based on the key event thresholds in the agronomic rule base; S4. Reconstruct the quality inspection parameters and transportation environment parameters into a dynamic system trajectory through phase space reconstruction, and generate a trajectory similarity index based on the reference trajectory predefined in the agronomic rule base to screen the target growth stage; S5. Conduct a superposition effect analysis on the key abnormal events in the target growth stage to generate the contribution weights of each key abnormal event, and identify the main loss-causing factors and synergy factors in combination with crop stress resistance data; S6. Modify the traceability path according to the contribution weight distribution of the main loss-causing factors, and reversely verify and iteratively update the key event thresholds and contribution weights in the agronomic rule base.

[0019] In step S1, the original data of the entire agricultural industry chain is obtained, including crop growth parameters, environmental parameters, quality inspection parameters, and transportation environment parameters. The crop growth parameters are collected by Internet of Things sensors deployed in the field. The leaf surface temperature is measured by an infrared temperature sensor, which is installed in the range of 20 - 50 cm above the crop canopy, and the data collection interval is 30 minutes. The stem diameter is measured by a macro camera combined with an image analysis algorithm. The macro camera is fixed on the side of the crop stem, and the measurement accuracy is 0.1 mm. The photosynthetic rate is obtained by a chlorophyll fluorometer. The probe of the chlorophyll fluorometer is close to the leaf surface, and the data recording interval is 1 hour.

[0020] Among the environmental parameters, the soil pH value is real-time monitored by a soil probe buried at a depth of 10 - 30 cm in the crop root layer. The soil probe is equipped with an electrochemical sensor, and the data upload interval is 2 hours. The rainfall is recorded by a rain gauge of a weather station. The weather station is deployed at the center of the farmland. The resolution of the rain gauge is 0.1 mm, and the data synchronization interval is 15 minutes. The light intensity is measured by a photosynthetically active radiation sensor, which is installed at the top of the crop canopy, and the data recording interval is 10 minutes.

[0021] The quality inspection parameters include the pesticide residue detection results and microbial indicators in the processing link. The pesticide residue detection results are generated by liquid chromatography. The detected substances include organophosphorus and pyrethroid compounds. The microbial indicators are determined by the microbial culture method. The microbial indicators include the total number of colonies of Escherichia coli and Salmonella. The detection results are generated by the processing plant laboratory and stamped with an electronic seal. The transportation environment parameters include the temperature and humidity data of the cold chain carriage. The temperature and humidity data are recorded by digital temperature sensors and capacitive humidity sensors. The digital temperature sensors are distributed in the front, middle, and rear of the carriage, and the data collection interval is 5 minutes. The measurement error of the capacitive humidity sensor is ±3%. The quality inspection parameters and transportation environment parameters are uploaded through blockchain nodes. The blockchain nodes are deployed in the data center of the processing plant and the in-vehicle terminal of the logistics vehicle. The uploaded data is appended with a timestamp. The timestamp format is UTC time and the accuracy is millisecond level. The timestamp is synchronized and calibrated with the national time service center time source.

[0022] The original data of crop growth parameters, environmental parameters, quality inspection parameters, and transportation environment parameters are stored in JSON fields and time series database tags. The JSON field names correspond to the parameter types. The field name of the leaf surface temperature is "leaf_temperature", and the field name of the stem diameter is "stem_diameter". When storing the data, a hash algorithm is used to generate a data fingerprint, and the data fingerprint and the original data are stored in a distributed database.

[0023] The field Internet of Things sensors are calibrated once every quarter. The infrared temperature sensor is linearly corrected in the range of 25°C - 40°C through a blackbody radiation source. The macro camera is calibrated for pixel resolution through a standard scale. The chlorophyll fluorometer is calibrated for the light quantum flux reading through a standard fluorescence plate. The rain gauge of the weather station is calibrated through the standard water injection method. The soil pH probe is calibrated at three points with pH 4.0, pH 7.0, and pH 10.0 standard buffer solutions. The pesticide residue detection equipment in the processing link is certified by a third-party testing institution every year. The certification items include detection limit, repeatability, and recovery rate. The certification report number is associated and stored with the detection data.

[0024] In the cold chain transportation link, the data of in-vehicle temperature and humidity sensors are uploaded to the blockchain node through a wireless transmission module. The transmission protocol is the MQTT protocol. The data packet includes the vehicle ID, sensor location number, and acquisition timestamp. If the temperature and humidity data exceed the preset range three times in a row, the in-vehicle terminal triggers an alarm and records the abnormal event. The abnormal event is marked as "transport_alert" and associated and stored with the temperature and humidity data.

[0025] Crop growth parameters are continuously collected from the sowing date to the harvesting date. Quality inspection parameters are uploaded within 24 hours after processing is completed. Transportation environment parameters are recorded in real time throughout the logistics process. The growth parameters, quality inspection parameters, and transportation parameters of the same batch of crops are associated through the batch ID. The batch ID format is "regional code_crop type_planting year_sequential number".

[0026] In step S2, the crop growth parameters and environmental parameters are divided according to the agronomic growth stages to generate time series data segments for each stage. The agronomic growth stages are divided into the germination stage, vegetative growth stage, reproductive growth stage, and maturity stage according to the phenological periods of the target crop. The division of phenological periods is based on the biological characteristics of the target crop under natural growth conditions. For example, the germination stage is defined as the period from the seed breaking through the soil to the unfolding of the first true leaf. The vegetative growth stage is defined as the period from the rapid growth of stems and leaves to flower bud differentiation. The reproductive growth stage is defined as the period from flowering to fruit formation. The maturity stage is defined as the period from fruit maturity to harvestable.

[0027] The crop growth parameters and environmental parameters are divided into stages according to the time intervals corresponding to the phenological periods. The start and end times of the time intervals are determined according to the growth cycle of the target crop. For example, for rice crops, the time interval of the germination stage is from the 1st day to the 7th day after sowing, the vegetative growth stage is from the 8th day to the 40th day, the reproductive growth stage is from the 41st day to the 70th day, and the maturity stage is from the 71st day to the 90th day. The crop growth parameters include leaf surface temperature, stem diameter, and photosynthetic rate. The environmental parameters include soil pH value, rainfall, and light intensity. The data of these parameters within the corresponding time intervals are arranged in chronological order to form time series data segments for each stage.

[0028] The time-series data segments are extracted by the sliding time window algorithm. The window length of the sliding time window is dynamically adjusted according to the biological characteristics of each growth stage. The window length in the germination stage is set to a shorter time span to adapt to the rapidly changing growth state, and the window length in the vegetative growth stage is set to a longer time span to cover the stable growth cycle. The adjustment rule for the window length is that the difference between the start time and the end time of the window does not exceed one-third of the total time length of this growth stage. When the sliding time window algorithm extracts the time-series data segments, it slides at a fixed step size in chronological order, and the step size is set to be less than half of the window length.

[0029] Each time-series data segment is bound with a unique stage identifier. The stage identifier includes the crop variety code, the name of the growth stage, and the start date. The crop variety code is associated with the crop growth parameters collected in step S1, the name of the growth stage is consistent with the phenological period division result, and the start date is the timestamp of the first data point of this time-series data segment. The format of the stage identifier is the combination of the crop variety code, the name of the growth stage, and the start date. If the rice variety code is RC001 and the start date of the germination stage is 20231001, then the stage identifier is RC001_Germination stage_20231001.

[0030] To verify the accuracy of the stage division, the data distribution of the crop growth parameters and environmental parameters within the time interval needs to conform to the growth law of the target crop. The stem diameter data in the vegetative growth stage should show a continuous upward trend. If the data trend is abnormal, the boundary of the time interval is adjusted again. The adjustment method for the time interval boundary is to calculate the average duration of each stage according to the phenological period records of the same variety of crops in the historical planting data, and align the change inflection point of the current growth parameters with the average duration. If the growth inflection point of the current stem diameter is earlier than the historical average, the time interval of the vegetative growth stage is shortened accordingly.

[0031] The storage format of the time-series data segments is the same as the structured format of the original data in step S1. A new field named stage_id is added to the JSON field to store the stage identifier, and new tags named the name of the growth stage and the start date are added to the time-series database tags. The stage identifier is associated with the batch ID in step S1. For the time-series data segments of the crop growth parameters in the vegetative growth stage with the same batch ID, the start date in the stage identifier is associated with the sowing date of this batch ID.

[0032] The implementation of the sliding time window algorithm is based on a time series data processing library. When the window slides, the segments with inconsistent data collection intervals are ignored, and the missing data is linearly interpolated and filled. If the leaf surface temperature data is missing within a certain time window, the average value is calculated based on the data at the previous and subsequent time points for filling. The interpolated time-series data segments are marked as completed, and the non-interpolated segments are marked as original data. The marking information is stored in the data_status of the JSON field.

[0033] After the stage division is completed, each time-series data segment is associated with the key abnormal events in step S3 through the stage identifier. If the time-series data segment in the vegetative growth period is marked as having an irrigation absence event, the corresponding stage identifier is referenced in the event record. The stage identifier serves as a key index for data retrieval in subsequent steps. When screening for the target growth stage in step S4, the dynamic system trajectory after phase space reconstruction is matched through the growth stage name in the stage identifier.

[0034] In step S3, based on the key event thresholds in the agronomic rule library, the key abnormal events and time windows in the time-series data segments are dynamically marked. The key event thresholds stored in the agronomic rule library include the extreme high temperature event threshold, the pest and disease outbreak threshold, and the irrigation absence threshold. The key event thresholds are generated by analyzing the historical disaster records of the target crop. The historical disaster records include the historical growth data and disaster event data of the same crop variety in the same planting area. The extreme high temperature event threshold is set as the daily average temperature exceeding the heat tolerance critical value of the target crop for three consecutive days. The heat tolerance critical value is statistically obtained from the temperature-sensitive interval in the historical growth data of this crop variety. The temperature-sensitive interval is the range of daily average temperatures in the historical data that lead to yield reduction. The pest and disease outbreak threshold is set as the leaf lesion area ratio exceeding 5% and the insect population density reaching 10 per plant. This threshold is statistically generated based on the critical conditions for disease spread in historical pest and disease outbreak events. The irrigation absence threshold is set as the soil humidity being lower than the minimum threshold of the crop water requirement for five consecutive days. The minimum threshold is determined based on the transpiration experiment data of this crop variety at different growth stages.

[0035] The dynamic marking of the key abnormal events in the time-series data segments is achieved by comparing the real-time data stream with the key event thresholds. The real-time data stream is sourced from the time-series data segments generated in step S2. The time-series data segments extract the crop growth parameters and environmental parameters in chronological order, and the parameter values are compared point by point with the thresholds of the corresponding events in the agronomic rule library. When the time for the parameter value to continuously exceed the threshold reaches the preset duration, a mark is triggered. The preset duration is dynamically adjusted according to the event type. The preset duration for the extreme high temperature event is set as three days, the preset duration for the pest and disease outbreak event is set as two days, and the preset duration for the irrigation absence event is set as five days.

[0036] The time window of the key abnormal event is recorded as the start time and the end time. The start time is the time point when the parameter first exceeds the threshold, and the end time is the time point when the parameter returns to within the threshold range. If the parameter does not return to within the threshold range, the end time is marked as the end time point of the time-series data segment. The range of the time window is associated with the stage division result of the time-series data segment in step S2, and the start time of the time window shall not be earlier than the start date of the time-series data segment.

[0037] Each critical abnormal event is bound to a corresponding stage identifier. The stage identifier is derived from the division result of the time series data segments in step S2. The binding process of the stage identifier includes matching the stage identifiers of all time series data segments in step S2 according to the time window range of the critical abnormal event, screening out the events whose time window is completely included in a certain time series data segment, and writing the stage identifier of this segment into the event record. For example, if the time window of the extreme high temperature event is included in the time series data segment with the stage identifier RC001_Vegetative Growth Stage_20231008, then this stage identifier is associated in the event record.

[0038] The critical abnormal events and their time windows are stored in structured fields. The structured fields include event type, time window range, and the associated stage identifier. The event type field records extreme high temperature, pest and disease outbreak, or irrigation absence. The time window range field records the timestamps of the start time and end time. The associated stage identifier field directly references the identifier in step S2. The storage format of the structured fields is consistent with the original data formats of step S1 and step S2. In the JSON field, a new event_type field is added to store the event type, a new time_window field is added to store the time window range, and a new stage_id field is added to store the stage identifier. In the time series database tags, new event type and stage identifier tags are added.

[0039] To verify the accuracy of the critical event marking, the marking result is cross-checked with the field on-site monitoring records. If an event marked as irrigation absence during a certain period but the field irrigation log shows normal irrigation, then the threshold recalibration process is triggered. The calibration method is to recalculate the irrigation absence threshold according to the soil moisture distribution during the normal irrigation period in the historical data. The calibrated threshold is updated to the agronomic rule library, and the version number and effective time are recorded.

[0040] The marking result of the critical abnormal event is used for the superposition effect analysis in the subsequent steps. If there are multiple critical abnormal events under the same stage identifier, then the overlapping degree and intensity of the time windows of the events will affect the calculation of the contribution weight. The marking result is also associated with the phase space reconstruction in step S4. If the time window of a certain transportation environment parameter abnormal event overlaps with the time window of the critical abnormal event, then it is associated to the same target growth stage through the stage identifier.

[0041] In step S4, the quality inspection parameters and transportation environment parameters are reconstructed into a dynamic system trajectory through phase space reconstruction. A trajectory similarity index is generated based on the reference trajectory predefined in the agronomic rule library to screen the target growth stage. The quality inspection parameters and transportation environment parameters are derived from the pesticide residue detection results, microbial indicators, and cold chain carriage temperature and humidity data collected in step S1. The quality inspection parameters and transportation environment parameters are arranged in chronological order to form a parameter sequence. The phase space reconstruction method uses the delay coordinate method. The embedding dimension and time delay of the delay coordinate method are determined according to the temporal characteristics of the time series data segment in step S2. The temporal characteristics include the data sampling frequency and the parameter fluctuation period. The data sampling frequency is the interval time of data collection by the sensor in step S1, and the parameter fluctuation period is obtained by extracting the main frequency component of the parameter sequence through Fourier transform. For example, if the sampling frequency of the quality inspection parameter is once a day and the main frequency component is a fluctuation once a week, the time delay is set to 7 days and the embedding dimension is set to 3.

[0042] The reference trajectory predefined in the agronomic rule library is generated based on the time series data segment of the historical normal growth stage. The historical normal growth stage is screened by the stage identifier without marked key abnormal events in step S3. For example, the time series data segment with the stage identifier RC001_Vegetative Growth Stage_20231008 and not associated with any key abnormal events is screened, and its quality inspection parameters and transportation environment parameter sequences are extracted. The reference trajectory is generated through the same phase space reconstruction method. The time length of the reference trajectory is the same as that of the time series data segment of the corresponding growth stage in step S2. The fluctuation amplitude threshold is set as the standard deviation of the data points of the reference trajectory, and the phase shift tolerance is set as the maximum allowable deviation range of the peak and valley positions of the reference trajectory.

[0043] The trajectory similarity index of the dynamic system trajectory is generated by comparing the morphological differences between the current trajectory and the reference trajectory. The morphological differences include the degree of differences in trajectory shape, fluctuation amplitude, and phase shift. The calculation of the morphological differences uses the dynamic time warping algorithm. The dynamic time warping algorithm non-linearly aligns the time axes of the two trajectories and then calculates the minimum cumulative distance. The minimum cumulative distance is normalized to a value between 0 and 1 as the trajectory similarity index. The smaller the value, the higher the similarity. The calculation of the trajectory similarity index integrates the time window overlap ratio of the key abnormal events in step S3. The time window overlap ratio is the ratio of the overlapping duration of the time range of the current trajectory and the time window of the key abnormal events under the same stage identifier in step S3. For example, if the time range of the current trajectory is 10 days and the overlapping part of the time window of the key abnormal event is 4 days, the overlap ratio is 40%.

[0044] The screening of the target growth stage is achieved through a comprehensive determination of the difference between the trajectory similarity index and the preset similarity threshold and the overlapping ratio of the time window. The preset similarity threshold is set according to the statistical distribution of the similarity indexes of historical normal trajectories. For example, the 10% quantile of the similarity indexes of historical normal trajectories is taken as the threshold. The preset overlapping ratio threshold is set according to the statistical analysis of the loss-causing cases of key abnormal events in step S3. For example, the minimum value of the overlapping ratio in the loss-causing cases is taken as the threshold. If the trajectory similarity index is lower than the preset similarity threshold and the overlapping ratio of the time window is higher than the preset overlapping ratio threshold, it is marked as the target growth stage. For example, when the trajectory similarity index is 0.3 and the overlapping ratio is 50%, the preset similarity threshold is 0.4, and the preset overlapping ratio threshold is 40%, it is determined as the target growth stage.

[0045] The target growth stage is associated with the stage identifier in step S2, and the screening result is stored as structured data including the stage identifier, the trajectory similarity index, and the overlapping ratio of the time window. The storage format of the structured data is the same as that of the data in steps S1 to S3. A new field similarity_score is added to the JSON field to store the trajectory similarity index, and a new field overlap_ratio is added to store the overlapping ratio of the time window. A target growth stage mark is added to the tags of the time series database. After the stage identifier is associated with the screening result in step S4, it is used as the input data for the superposition effect analysis in step S5. For example, if there are multiple target growth stage marks under the same stage identifier, the data segment corresponding to the mark with the highest overlapping ratio is preferentially analyzed.

[0046] To verify the accuracy of the screening result, the target growth stage mark is compared with the abnormal growth stage recorded in the field investigation. If the marked result is inconsistent with the field record, the preset similarity threshold and the preset overlapping ratio threshold are adjusted. The adjustment method is to reset the threshold according to the statistical distribution of historical correct marking cases. For example, the preset similarity threshold is adjusted from 0.4 to 0.35, and the preset overlapping ratio threshold is adjusted from 40% to 45%. The adjusted thresholds are updated to the agronomic rule library, and the version number and the effective time are recorded.

[0047] The screening result is also associated with the traceability path correction in step S6. For example, the stage identifier in the target growth stage mark is used to reverse-locate the time series data segment in step S2, and the loss-causing factors are analyzed in combination with the key abnormal events in step S3. The trajectory similarity index and the overlapping ratio of the time window participate in the calculation of the contribution weight in step S5 as weight coefficients. For example, the lower the trajectory similarity index and the higher the overlapping ratio, the greater the contribution weight of the corresponding key abnormal event.

[0048] Step S4 solves the problem of the disconnection between the data form and the dynamic law of biological growth in traditional agricultural traceability by reconstructing the quality inspection parameters and transportation environment parameters into a dynamic system trajectory and performing morphological difference analysis with the reference trajectory in the agronomic rule base; by integrating the time window overlap ratio of the key abnormal events in Step S3, screening the target growth stage by combining the trajectory similarity index and the dual judgment conditions of the preset threshold; phase space reconstruction maps the discrete quality inspection parameters and transportation parameters into a continuous system trajectory to restore the dynamic characteristics of the growth process; the time window overlap ratio introduces the correlation analysis of key abnormal events to avoid isolated judgment errors; through the integration of multi-dimensional data morphology (trajectory shape, fluctuation amplitude) and event spatio-temporal correlation (window overlap), accurately locate the damaged stage, making the traceability conclusion more in line with the irreversible law of crop growth, significantly improving the accuracy of screening abnormal growth stages, solving the problem of traceability path deviation caused by traditional methods ignoring biological timing characteristics, and providing reliable input for superposition effect analysis and damage factor identification.

[0049] In Step S5, perform superposition effect analysis on the key abnormal events in the target growth stage to generate the contribution weights of each key abnormal event, and identify the main damage-causing factors and synergistic factors in combination with the crop stress resistance data. The key abnormal events in the target growth stage come from the screening results of Step S4. The key abnormal events include event type, time window range, and associated stage identifier. The event type is the extreme high temperature event, pest and disease outbreak event, or irrigation shortage event marked in Step S3. The superposition effect analysis generates the contribution weights by integrating the trajectory similarity index and the time window overlap ratio in Step S4. The trajectory similarity index is the normalized value calculated in Step S4, and the time window overlap ratio is the proportion of the overlap duration between the time window of the key abnormal event in Step S4 and the time range of the current target growth stage. The contribution weight is calculated using a linear combination of the trajectory similarity index and the time window overlap ratio. The weight coefficients of the trajectory similarity index and the time window overlap ratio are preset according to the statistical analysis results of historical damage cases. For example, by analyzing the damage influence degree of different parameters in historical data, the weight coefficient of the trajectory similarity index is set to 0.6, and the weight coefficient of the time window overlap ratio is set to 0.4.

[0050] Crop stress resistance data is extracted from a predefined stress resistance database, which stores drought tolerance scores and disease resistance scores of different crop varieties. The drought tolerance score is generated based on the survival rate of the target crop variety in a drought stress experiment. The experimental method is to simulate different drought intensities in a controlled environment and record the plant survival rate. The survival rate is converted into a score from 0 to 1. For example, a survival rate of 80% corresponds to a score of 0.8. The disease resistance score is generated based on the lesion inhibition rate of the target crop variety in a pathogen inoculation experiment. The experimental method is to measure the proportion of the lesion area after inoculating a specified concentration of pathogens and convert it into a score from 0 to 1. For example, an inhibition rate of 60% corresponds to a score of 0.6.

[0051] The determination of the main loss-causing factor is achieved by multiplying the contribution weight by the reciprocal of the stress resistance score. The calculation formula for the product value is the contribution weight multiplied by the reciprocal of the stress resistance score. For example, if the contribution weight is 0.5 and the disease resistance score is 0.4, then the product value is 0.5×(1 / 0.4)=1.25. The preset product threshold is set according to the distribution of the product values of the main loss-causing factors in historical loss cases. For example, the 90th percentile of the product values in historical cases is taken as the threshold. If the current product value exceeds this threshold, it is marked as the main loss-causing factor.

[0052] The identification of the synergistic factor is achieved by calculating the time window overlap density of key abnormal events under the same stage identifier. The time window overlap density is the proportion of the number of time window overlaps between events to the total number of events. For example, if there are 3 key abnormal events under the same stage identifier, and the number of time window overlaps between every two events is 2 times, then the overlap density is the number of 2 overlaps divided by the total number of pairs of 3 events (3 pairs), and the calculation result is 2 / 3. The preset density threshold is set according to the statistical results of the overlap density of historical synergistic loss cases. For example, the 75th percentile of the overlap density in historical cases is taken as the threshold. If the current overlap density exceeds this threshold, it is marked as the synergistic factor.

[0053] The main loss-causing factor and the synergistic factor are associated with the key abnormal events in step S3. The output data includes the event type, contribution weight, and associated stage identifier. The event type field records extreme high temperature, pest and disease outbreak, or irrigation shortage. The contribution weight field records the calculation result of the linear combination. The associated stage identifier field references the stage identifier in step S2. The storage format of the output data is consistent with the structured data in steps S1 to S4. New fields leading_factor and synergistic_factor are added in the JSON fields to mark the main loss-causing factor and the synergistic factor respectively. An associated label of the event type and the stage identifier is added to the tags of the time series database.

[0054] To verify the accuracy of the primary loss-causing factors and co-factors, the output data is compared with the records of the loss-causing reasons in the field investigation. If the marked result is inconsistent with the field record, the preset product threshold and preset density threshold are adjusted. The adjustment method is to reset the threshold according to the statistical distribution of historical correct marking cases. For example, the preset product threshold is adjusted from 1.2 to 1.1, and the preset density threshold is adjusted from 0.6 to 0.65. The adjusted thresholds are updated to the agronomic rule base and the version number and effective time are recorded.

[0055] The output data of the primary loss-causing factors and co-factors is used for the traceability path correction in step S6. For example, the stage identifier corresponding to the primary loss-causing factor is used to reversely locate the time series data segment in step S2, and the spatio-temporal correlation of the loss-causing event is analyzed by combining the time window of the key abnormal event in step S3. The marked result of the co-factor is used to supplement and correct the secondary loss-causing factors in the traceability path. For example, the event type of the co-factor within the same stage may indicate the occurrence of compound disasters.

[0056] Step S5 solves the problem of deviation in identifying loss-causing factors caused by single-parameter analysis in traditional agricultural traceability by integrating the trajectory similarity index, the overlapping ratio of time windows, and crop stress resistance data. Compared with the prior art that only relies on the frequency or intensity of event occurrence, the data form difference is quantified by the trajectory similarity index, the spatio-temporal correlation of events is reflected by the overlapping ratio of time windows, and the variety resistance difference is reflected by the stress resistance score; the event influence and spatio-temporal coupling effect are dynamically balanced through linear combination (such as trajectory weight 0.6 + overlapping ratio weight 0.4), and the loss-causing sensitivity of low-resistance varieties is reversely enhanced by combining the reciprocal product of the stress resistance score (such as contribution weight × 1 / resistance to disease score); the threshold is preset based on the historical statistical distribution (such as the product threshold taking the 90th percentile and the overlapping density threshold taking the 75th percentile), so that the judgment standard conforms to the actual loss-causing law and avoids subjective assumption errors. Compared with the prior art, through multi-dimensional data collaborative analysis and dynamic threshold calibration, the accuracy of identifying primary loss-causing factors is significantly improved, and the problem of deviation in traceability conclusions caused by ignoring variety resistance and event correlation in traditional methods is solved.

[0057] In step S6, the traceability path is corrected according to the contribution weight distribution of the main loss-causing factors, and the key event thresholds and contribution weights of the agronomic rule base are reversely verified and iteratively updated. The correction of the traceability path is achieved by adjusting the priority order of the stage identifiers corresponding to the main loss-causing factors in the traceability path. The priority order is arranged from high to low according to the contribution weights generated in step S5. The higher the contribution weight, the higher the priority of the stage identifier in the traceability path. For example, if the contribution weight of the stage identifier RC001_Vegetative Growth Stage_20231008 is 0.9 and the contribution weight of the stage identifier RC002_Reproductive Growth Stage_20231015 is 0.7, the system will first arrange RC001_Vegetative Growth Stage_20231008 at the forefront of the traceability path.

[0058] The reverse verification is completed by comparing the consistency of the damaged stages between the corrected traceability path and the historical actual loss-causing cases. The historical actual loss-causing cases are the cases where the main loss-causing factors verified in steps S3 to S5 match the field investigation records. The consistency judgment criterion is the coincidence ratio of the first N stage identifiers in the corrected path and the actual damaged stages in the historical cases. The value of N is determined according to the average number of damaged stages in the historical cases. For example, if the historical cases on average involve 3 damaged stages, then N is set to 3. The preset verification threshold is set according to the statistical distribution of the matching accuracy of the historical cases. For example, the matching ratio at the 95th percentile of the historical cases is taken as the threshold. If the coincidence ratio of the first 3 stage identifiers in the current corrected path is lower than 70% compared with the historical cases, it is judged as inconsistent.

[0059] The iterative update of the key event thresholds in the agronomic rule base is based on the reverse verification results. If the consistency ratio is lower than the preset verification threshold, the thresholds are recalculated according to the key abnormal event parameters of the actual damaged stages in the historical cases. For example, for the extreme high temperature event threshold, the temperature data of the actual damaged stages in the historical cases are extracted and its 95th percentile value is calculated as the new threshold; for the irrigation shortage threshold, the lowest 5th percentile value of the soil humidity data in the historical cases is taken as the new threshold. After the threshold is updated, the version number is recorded. The version number format is a combination of rule type, date and serial number, such as "High Temperature Threshold_20231001_001".

[0060] The iterative update of the contribution weight is achieved by statistically analyzing the distribution frequency of the main loss-causing factors in each growth stage of the corrected traceability path. The distribution frequency is the proportion of the number of times the main loss-causing factor is marked in the historical corrected path in the same growth stage to the total number of times. For example, if the vegetative growth stage appears 60 times in 100 corrected paths, the distribution frequency is 60%. The preset frequency threshold is set based on the distribution statistics of the historical high-frequency loss-causing stages. For example, the 80th percentile of the historical distribution frequency is taken as the threshold. If the current distribution frequency exceeds the threshold, the contribution weight is increased according to the exceeding proportion. For example, when the frequency is 85%, the weight increase proportion is (85% - 80%) / 80% = 6.25%.

[0061] The updated critical event threshold and contribution weight are synchronized to the agronomic rule library and the version number is recorded. The version number is associated with the stage identifier in step S2 and the main loss-causing factor in step S5. For example, the version number "high temperature threshold_20231001_001" is associated with the stage identifier RC001 - vegetative growth stage_20231008 and the main loss-causing factor "extreme high temperature". The updated data storage format is the same as that in steps S1 to S5. The threshold_version and weight_version fields are added to the JSON fields to record the threshold and weight version numbers, and the association label between the version number and the stage identifier is added to the time series database tags.

[0062] To verify the effectiveness of the updated rule library, the new threshold and weight are applied to the historical cases that did not participate in the training for cross-validation. For example, the data of the previous year is used to test the recognition accuracy of the new threshold for the loss-causing stage. If the accuracy improvement exceeds the preset optimization threshold (such as 5%), it is determined that the update is effective; otherwise, roll back to the previous version and readjust the parameters.

[0063] The updated agronomic rule library is used for the next round of data traceability analysis in steps S1 to S5. For example, the new threshold is directly called for marking key abnormal events in the next round of data collection to achieve closed-loop iterative optimization. The version number is also used to trace the update history of the rule library. For example, through the version number "high temperature threshold_20231001_001", the stage identifier and the contribution weight distribution of the corresponding historical cases can be traced back.

[0064] It should be noted that the core feature of the agronomic rule library is to dynamically integrate multi-source data and iteratively optimize based on a closed-loop feedback mechanism. The construction process and data sources specifically include: Generation of critical event thresholds: Generated by statistically analyzing historical disaster records (such as temperature, humidity, pest and disease outbreak events). For example, the extreme high temperature threshold is determined by calculating the quantile value based on the temperature-sensitive interval in historical yield reduction cases (such as the number of consecutive high-temperature days and temperature range that cause yield reduction), rather than directly using the theoretical values of crop growth or fixed empirical values; Reference trajectory construction: Generated based on the time-series data segments of the historical normal growth stages in step S2. The normal growth stages are screened by the stage identifiers without marked key abnormal events in step S3. The fluctuation amplitude threshold and phase shift tolerance of the reference trajectory are dynamically set according to the statistical distribution of the normal stage data (such as standard deviation, deviation of peak and trough positions). Sources of stress resistance data: Quantify the resistance levels of crop varieties through controlled environment experiments (such as drought stress, pathogen inoculation). The experimental data is converted into standardized scores (such as survival rate → drought tolerance score, lesion inhibition rate → disease resistance score), and the scores are stored as structured fields and associated with the stage identifiers. Closed-loop iterative mechanism: Dynamically optimize the rule base through the reverse verification (historical case consistency comparison) and parameter update (threshold recalculation, weight improvement) in step S6. For example, the consistency ratio triggers threshold update, and the distribution frequency triggers weight adjustment. The version number management ensures the traceability of the update process.

[0065] Traditional agricultural rule bases are usually statically set based on fixed agronomic theories or expert experiences, while this agronomic rule base realizes dynamicization and self-adaptability: Data-driven threshold generation: Thresholds are derived from the statistical distribution of historical loss-causing cases (such as quantile calculation), rather than fixed thresholds. Cross-step data association: The rule base is dynamically bound to multi-step data such as stage identifiers, key abnormal events, and stress resistance scores. For example, the reference trajectory depends on the time-series segments in S2, and the threshold update depends on the reverse verification in S6. Closed-loop feedback optimization: Through the version number to associate the iterative process, the rule base parameters are dynamically adjusted according to the traceability results. For example, the version number "high temperature threshold_20231001_001" records the threshold update history and associated loss-causing factors.

[0066] This agronomic rule base solves the problem of the disconnection between traditional static rule bases and the dynamic laws of agricultural organisms by integrating historical data statistics, experimental verification, and closed-loop feedback mechanisms.

[0067] Example 2: Figure 2 The structural schematic diagram of the big data-based agricultural whole industry chain traceability system of the present invention is given. The big data-based agricultural whole industry chain traceability system includes the following modules: Data acquisition module: Used to acquire the original data of the agricultural whole industry chain, including crop growth parameters, environmental parameters, quality inspection parameters, and transportation environment parameters. Data segmentation module: Used to divide the crop growth parameters and environmental parameters according to the agronomic growth stages, and generate time-series data segments for each stage. Event marking module: Used to dynamically mark the key abnormal events and time windows in the time-series data segments based on the key event thresholds of the agronomic rule base. Trajectory analysis module: used to reconstruct the quality inspection parameters and transportation environment parameters into a dynamic system trajectory through phase space, and generate trajectory similarity indicators based on the reference trajectories predefined in the agronomic rule library to screen the target growth stage; Factor identification module: used to perform superposition effect analysis on the key abnormal events in the target growth stage to generate the contribution weights of each key abnormal event, and combine the crop stress resistance data to identify the main loss-causing factors and synergetic factors; Path optimization module: used to correct the traceability path according to the contribution weight distribution of the main loss-causing factors, and reversely verify and iteratively update the key event thresholds and contribution weights of the agronomic rule library.

[0068] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can be run on a PC or other terminals with a user interface, so as to meet various hardware environments and usage requirements.

[0069] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0070] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices and modules can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0071] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or modules can be in electrical, mechanical, or other forms.

[0072] The modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] In addition, in each embodiment of this application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0074] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0075] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0076] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for tracing the entire agricultural industry chain based on big data, characterized in that It includes the following steps: S1. Obtain the original data of the entire agricultural industry chain, including crop growth parameters, environmental parameters, quality inspection parameters, and transportation environment parameters; S2. Divide the crop growth parameters and environmental parameters according to the agronomic growth stages to generate time series data segments for each stage; S3. Dynamically mark the key abnormal events and time windows in the time series data segments based on the key event thresholds in the agronomic rule library; S4. Reconstruct the quality inspection parameters and transportation environment parameters into a dynamic system trajectory through phase space reconstruction, and generate a trajectory similarity index based on the reference trajectory predefined in the agronomic rule library to screen the target growth stage; S5. Conduct a superposition effect analysis on the key abnormal events in the target growth stage to generate the contribution weights of each key abnormal event, and identify the main loss-causing factors and synergistic factors in combination with the crop stress resistance data; S6. Modify the traceability path according to the contribution weight distribution of the main loss-causing factors, and reverse verify and iteratively update the key event thresholds and contribution weights in the agronomic rule library.

2. The method for tracing the entire agricultural industry chain based on big data according to claim 1, characterized in that The crop growth parameters are collected by Internet of Things sensors deployed in the field, including leaf surface temperature, stem diameter, and photosynthetic rate; the environmental parameters include soil pH value, rainfall, and light intensity, which are synchronously obtained through weather stations and soil probes; the quality inspection parameters include the pesticide residue detection results and microbial indicators in the processing link, and the transportation environment parameters include the temperature and humidity fluctuation data of the cold chain carriage.

3. The method for tracing the entire agricultural industry chain based on big data according to claim 1, characterized in that The agronomic growth stages are divided into the germination stage, vegetative growth stage, reproductive growth stage, and maturity stage according to the phenological period of the target crop; the crop growth parameters and environmental parameters are divided into stages according to the time intervals corresponding to the phenological period to generate time series data segments for each stage; The time series data segments are extracted by the sliding time window algorithm, and the window length of the sliding time window is dynamically adjusted according to the biological characteristics of each growth period.

4. The method for tracing the entire agricultural industry chain based on big data according to claim 1, characterized in that The key event thresholds stored in the agronomic rule library include the extreme high temperature event threshold, the pest and disease outbreak threshold, and the irrigation shortage threshold, and the key event thresholds are generated based on the historical disaster record statistical model; The dynamic marking of the key abnormal events in the time series data segments is realized by comparing the real-time data stream with the key event thresholds. When the crop growth parameters or environmental parameters continuously exceed the key event thresholds for a preset duration, they are marked as key abnormal events; The time window of the key abnormal event is recorded as the start time and the end time; each key abnormal event is bound to the corresponding stage identifier.

5. The method for tracing the entire agricultural industry chain based on big data according to claim 1, characterized in that Convert the quality inspection parameters and transportation environment parameters into a dynamic system trajectory through the phase space reconstruction method; The trajectory similarity index of the dynamic system trajectory is generated by comparing the morphological differences between the current dynamic system trajectory and the reference trajectory predefined in the agronomic rule library; The calculation of the trajectory similarity index integrates the time window overlap ratio of the key abnormal events. The time window overlap ratio is the ratio of the overlapping duration of the time range of the current trajectory and the time window of the key abnormal events under the same stage identifier; The screening of the target growth stage is realized through the comprehensive judgment of the difference between the trajectory similarity index and the preset similarity threshold and the time window overlap ratio. If the trajectory similarity index is lower than the preset similarity threshold and the time window overlap ratio is higher than the preset overlap ratio threshold, it is marked as the target growth stage.

6. The method for tracing the entire agricultural industry chain based on big data according to claim 5, characterized in that The phase space reconstruction method adopts the delayed coordinate method, and the embedding dimension and time delay are determined according to the temporal characteristics of the time series data segment, where the temporal characteristics include the data sampling frequency and the parameter fluctuation period.

7. The method for tracing the entire agricultural industry chain based on big data according to claim 1, characterized in that Perform superposition effect analysis on the key abnormal events in the target growth stage, where the key abnormal events include the event type, time window range, and associated stage identifier; The contribution weight is generated through a linear combination of the trajectory similarity index and the time window overlap ratio, and the weight coefficients of the trajectory similarity index and the time window overlap ratio are preset according to the statistical analysis of historical loss cases; The crop stress resistance data is extracted from a predefined stress resistance database, which stores the drought tolerance score and disease resistance score of crop varieties; The main loss-causing factor is determined by multiplying the contribution weight by the reciprocal of the stress resistance score. If the product value exceeds the preset product threshold, it is marked as the main loss-causing factor; The co-factor is identified by calculating the time window overlap density of key abnormal events under the same stage identifier. The time window overlap density is the ratio of the number of overlaps between events to the total number of events. When the ratio exceeds the preset density threshold, it is marked as the co-factor; The main loss-causing factor and the co-factor are associated with the key abnormal events, and the output data includes the event type, contribution weight, and associated stage identifier.

8. The method for tracing the entire agricultural industry chain based on big data according to claim 1, characterized in that The traceability path correction is achieved by adjusting the priority order of the stage identifier corresponding to the main loss-causing factor in the traceability path. The priority order is arranged from high to low according to the contribution weight; The reverse verification is completed by comparing the consistency of the corrected traceability path with the loss stage of historical actual loss cases; The key event threshold of the agronomic rule base is iteratively updated based on the reverse verification result. If the consistency ratio is lower than the preset verification threshold, the threshold is recalculated according to the parameters of the key abnormal events in the actual loss stage in historical cases; The contribution weight is iteratively updated by statistically analyzing the distribution frequency of the main loss-causing factors in each growth stage in the corrected traceability path, and the weights of the factors with a distribution frequency higher than the preset frequency threshold are increased proportionally.

9. The agricultural whole industry chain traceability method based on big data according to claim 8, characterized in that, The consistency determination criterion is the coincidence ratio of the first N stage identifiers in the corrected path with the actual loss stage in historical cases, and the value of N is determined according to the average number of loss stages in historical cases.

10. An agricultural whole industry chain traceability system based on big data, used to implement the agricultural whole industry chain traceability method based on big data according to any one of claims 1-9, characterized in that, It includes the following modules: Data acquisition module: used to acquire the original data of the entire agricultural industry chain, including crop growth parameters, environmental parameters, quality inspection parameters, and transportation environment parameters; Data segmentation module: used to divide the crop growth parameters and environmental parameters according to the agronomic growth stage to generate time series data segments for each stage; Event marking module: used to dynamically mark the key abnormal events and time windows in the time series data segment based on the key event threshold of the agronomic rule base; Trajectory analysis module: used to reconstruct the quality inspection parameters and transportation environment parameters into a dynamic system trajectory through phase space, and generate a trajectory similarity index based on the reference trajectory predefined in the agronomic rule base to screen the target growth stage; Factor identification module: used to perform superposition effect analysis on the key abnormal events in the target growth stage to generate the contribution weight of each key abnormal event, and identify the main loss-causing factor and the co-factor in combination with the crop stress resistance data; Path optimization module: used to correct the traceability path according to the contribution weight distribution of the main loss-causing factors, and to reverse verify and iteratively update the key event thresholds and contribution weights of the agronomic rule base.

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