Intelligent tracing method for full life cycle quality of agricultural microbial inoculants

Abstract

The present application relates to the technical field of agricultural input quality traceability management, and specifically discloses a full-life-cycle quality intelligent traceability method for agricultural microbial inoculants, which collects fermentation whole-process parameters and environmental temperature and humidity accumulated temperature values after tanking to generate an initial batch traceability file; associates the file to a pending state mark, constructs a space-time anchor association according to the tanking timestamp and the filling timestamp to generate a temporary file; obtains a specific growth rate measured value, constructs a deviation correction mapping to correct the specific growth rate with the environmental temperature and humidity accumulated temperature value as a covariant, and backfills the temporary file to generate a history decay gene fingerprint; stores the fingerprint for notarization by irreversible hashing and binds it to the file for solidification; in response to a code scanning query, extracts the fingerprint and associates a circulation link temperature cumulative fluctuation sequence to generate current effective viable state information and an application window prompt; and the present application realizes data association and dynamic quality expression for the whole chain from production to application.

Description

Technical Field

[0001] This invention relates to the field of agricultural input quality traceability management technology, specifically to an intelligent traceability method for the entire life cycle quality of agricultural microbial agents. Background Technology

[0002] Currently, the quality traceability of agricultural microbial agents generally adopts a batch-based information management system. The basic approach involves binding static information such as strain number, fermentation batch number, filling batch number, and factory inspection report using QR codes or barcodes. Farmers or distributors use scanning terminals to read the fixed data stored within the code to obtain the production information and factory qualification determination for that batch of products; this management method, to a certain extent, achieves the transmission of basic information from production to sales.

[0003] Existing traceability systems for agricultural microbial agents only record static quality inspection data upon leaving the factory. They lack the technical means to asynchronously correlate the measured specific growth rate obtained by plate counting lag after fermentation and tank release during the production stage with the traceability files generated before bottling. This results in the initial specific growth parameters in the traceability files always being unverified theoretical design values. At the same time, the cumulative decay of live bacteria due to cold chain temperature fluctuations during distribution and the disturbance effect of ambient temperature and humidity accumulation on bacterial metabolism during the production stage are two independent information recording paths in the existing system. They cannot be correlated and integrated into a dynamic activity status assessment basis for the same batch of products. As a result, the information obtained by end users through scanning the code is only the factory nominal value rather than the current actual live bacteria status. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent traceability method for the entire life cycle quality of agricultural microbial agents, in order to solve the problems mentioned above.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A smart traceability method for the entire lifecycle quality of agricultural microbial agents includes the following steps: S1, collect the exhaust gas concentration, dissolved oxygen decay slope, feeding accumulation rate and ambient temperature and humidity accumulated temperature values ​​during the entire fermentation process after the tank is opened, generate an initial batch traceability file, bind a unique identifier to the batch traceability file and reserve the specific growth rate field as null. S2, associate the unique identifier and batch traceability file with the pending status marker, construct a spatiotemporal anchoring association based on the can release time stamp and filling time stamp, and generate a temporary file waiting for quality inspection backfilling; S3. Obtain the measured value of specific growth rate after tank loading by plate counting. With ambient temperature, humidity and accumulated temperature as covariates, construct a deviation correction mapping. Use the deviation correction mapping to correct the measured value of specific growth rate. Feed the corrected measured value of specific growth rate back into the temporary file and simultaneously correct all fermentation time process parameters to generate a unique process decay gene fingerprint for the corresponding batch. S4. The process decay gene fingerprint is irreversibly hashed and stored, and then bound to the batch traceability file to generate a solidified batch traceability file. S5, in response to the terminal's QR code scanning query request, extracts the process decay gene fingerprint from the batch traceability file solidified under the current query timestamp, associates it with the temperature accumulation fluctuation sequence in the circulation process, and generates the current effective live bacteria status information and application window prompts.

[0007] As a further aspect of the present invention: the generation of temporary files awaiting quality inspection backfilling specifically includes: Logically bind the unique identifier to the storage address of the batch traceability file and write it to the pending status marker; The can release timestamp and filling timestamp are concatenated in chronological order to form a time axis anchoring interval. The filling production line number is then used as the spatial dimension anchoring coordinate and combined with the time axis anchoring interval to form a spatiotemporal anchoring association. Using a unique identifier as an index, all reserved null fields in the batch traceability archive are encapsulated together with the spatiotemporal anchoring association into a temporary archive awaiting quality inspection backfilling.

[0008] As a further aspect of the present invention: the combination process of the spatiotemporal anchoring association is as follows: The can placement timestamp and the filling timestamp are concatenated in chronological order to generate a time axis anchoring interval; Map the filling production line number to spatial dimension anchor coordinates; By combining the time axis anchoring interval as the first component and the spatial dimension anchoring coordinates as the second component, a spatiotemporal anchoring association is generated.

[0009] As a further aspect of the present invention: the construction of the deviation correction mapping specifically includes: The accumulated temperature and humidity values ​​of the environment are divided into multiple accumulated temperature segments according to the daily time period of the natural days after the tank is placed, and the accumulated temperature accumulation amount corresponding to each accumulated temperature segment is calculated. Using the accumulated temperature on the first day after the tank is opened as the baseline value, the accumulated temperature of each subsequent temperature segment is compared with the baseline value to generate the accumulated temperature deviation ratio corresponding to each segment. The accumulated temperature deviation ratios are arranged in chronological order to form a deviation ratio sequence, and this deviation ratio sequence is used as a deviation correction mapping.

[0010] As a further aspect of the present invention: the generation process of the process decay gene fingerprint is as follows: The measured specific growth rate is multiplied one by one with each deviation ratio in the deviation ratio sequence to obtain the corrected rate value for each time segment. The corrected rate values ​​are backfilled into the ratio growth field of the corresponding time point in the temporary file in time segment order to form the corrected ratio growth time series. Based on the growth time series, the induction timing marker and feeding execution time at each time point in the temporary file are adjusted one by one to make the two synchronously correlated with the rise and fall trend of the growth time series. All adjusted process parameters were combined in chronological order to form a process decay gene fingerprint.

[0011] As a further aspect of the present invention: the generation of the solidified batch traceability file specifically includes: The process decay gene fingerprint is segmented and compressed to obtain a compressed fixed-length summary value; A fixed-length digest value is used as the evidence identifier, and it is associated with the storage address of the batch traceability file to generate an associated file with the evidence identifier. The associated files, along with the solidified evidence timestamp, are written into the immutable evidence node to generate the solidified batch traceability file.

[0012] As a further aspect of the present invention: obtaining the compressed fixed-length digest value specifically includes: The chronological decay gene fingerprint is divided into multiple data blocks of equal length in chronological order; Calculate the weighted sum of all feature values ​​within each data block to obtain the block representative value corresponding to each data block; The representative values ​​of each block are combined according to the original order of the data blocks to obtain the combined intermediate value; The merged intermediate values ​​are cyclically shifted and mixed by a fixed number of bits to obtain a fixed-length summary value.

[0013] As a further aspect of the present invention: the generation of associated files with evidence storage identifiers specifically includes: The fixed-length digest value is used as the starting segment and concatenated with the first and last characters of the storage address to obtain the concatenated evidence string. Using the total length of the concatenated evidence string as the span base, the position sequence is extracted from it at fixed intervals to generate the interlaced position sequence; Based on the interleaving position sequence, the fixed-length digest value is interleaved bit by bit between the corresponding bits in the storage address to obtain the fused evidence string; The first part of the merged evidence string is appended with an evidence identifier to generate an associated file.

[0014] As a further aspect of the present invention: the generation of current effective viable bacteria status information and application window prompts specifically includes: Extract the process decay gene fingerprint from the solidified batch traceability file, and accumulate the temperature deviation values ​​of each cold chain node recorded in the circulation process in chronological order to form a temperature cumulative fluctuation sequence. The retention rate after decay of each segment is obtained by subtracting the correction rate value of each time segment in the process decay gene fingerprint from the cumulative fluctuation value of the corresponding time segment in the temperature cumulative fluctuation sequence. The retention rates of each segment after decay are concatenated in chronological order to form a decay trajectory. Based on the comparison between the decay trajectory and the preset activity threshold, the current effective viable bacteria status information and application window prompts are generated.

[0015] As a further aspect of the present invention: the current effective live bacteria status information and application window specifically include: The decayed retention rate corresponding to the current query time point in the decay trajectory is compared step by step with the preset first activity threshold and second activity threshold. The first activity threshold is greater than the second activity threshold. When the retention rate after decay is greater than the first activity threshold, an activity surplus status information and a window prompt indicating that normal application is allowed are generated. When the retention rate after decay is between the first activity threshold and the second activity threshold, information on the critical state of activity and a prompt for a limited application window are generated. When the retention rate after decay is less than the second activity threshold, an insufficient activity status message and a prompt to postpone application are generated.

[0016] The beneficial effects of this invention are: This invention achieves identity anchoring of batch traceability files when the production timeline conflict between quality inspection results and filling scanning. By cascading the can release timestamp and filling timestamp into a time axis anchoring interval and mapping the filling production line number into a spatial dimension anchoring coordinate, this invention enables the identification of batch traceability files in the event of a production timeline conflict where quality inspection results lag behind filling and scanning. In conjunction with an asynchronous backfilling mechanism, the measured specific growth rate value can be accurately located to the corresponding temporary file under the spatiotemporal identity after quality inspection is completed and then fed back into the file. This avoids data attribution confusion or human intervention mismatch caused by the quality inspection result entry time being later than the filling completion time, thus improving the accuracy of timeline management of production data and quality inspection data in the agricultural input quality traceability system.

[0017] This invention constructs a deviation correction mapping using ambient temperature, humidity, and accumulated temperature as covariates. It then performs daily segmented correction on the measured specific growth rate determined by plate counting. The corrected specific growth time series is synchronously associated with fermentation timing parameters such as induction timing markers and feeding execution times to form a process decay gene fingerprint. This fingerprint becomes a batch-specific data carrier that covers the impact of ambient temperature fluctuations. During subsequent queries, the decay correction is performed by combining the temperature cumulative fluctuation sequence in the distribution process and generating the current viable cell status. This achieves a dynamic expression of quality status with data association throughout the entire process from the fermentation environment to the distribution environment, overcoming the limitation of traditional traceability systems that can only provide static data at the factory. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the process for generating temporary files awaiting quality inspection and backfilling in this invention; Figure 3 This is a flowchart of the process for constructing the deviation correction mapping in this invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 As shown, this invention provides an intelligent traceability method for the entire lifecycle quality of agricultural microbial agents, comprising the following steps: S1, collect the exhaust gas concentration, dissolved oxygen decay slope, feeding accumulation rate and ambient temperature and humidity accumulated temperature values ​​during the entire fermentation process after the tank is opened, generate an initial batch traceability file, bind a unique identifier to the batch traceability file and reserve the specific growth rate field as null. S2, associate the unique identifier and batch traceability file with the pending status marker, construct a spatiotemporal anchoring association based on the can release time stamp and filling time stamp, and generate a temporary file waiting for quality inspection backfilling; S3. Obtain the measured value of specific growth rate after tank loading by plate counting. With ambient temperature, humidity and accumulated temperature as covariates, construct a deviation correction mapping. Use the deviation correction mapping to correct the measured value of specific growth rate. Feed the corrected measured value of specific growth rate back into the temporary file and simultaneously correct all fermentation time process parameters to generate a unique process decay gene fingerprint for the corresponding batch. S4. The process decay gene fingerprint is irreversibly hashed and stored, and then bound to the batch traceability file to generate a solidified batch traceability file. S5, in response to the terminal's QR code scanning query request, extracts the process decay gene fingerprint from the batch traceability file solidified under the current query timestamp, associates it with the temperature accumulation fluctuation sequence in the circulation process, and generates the current effective live bacteria status information and application window prompts.

[0022] In S1, the exhaust gas concentration, dissolved oxygen decay slope, feed accumulation rate, and ambient temperature, humidity, and accumulated temperature values ​​during the entire fermentation process after tank discharge are collected. An initial batch traceability file is generated, and a unique identifier is bound to the batch traceability file, with the specific growth rate field reserved as null. Specifically, this includes: Simultaneously with the start of the fermentation process, an infrared gas analyzer connected to the fermenter's exhaust pipe is activated. This analyzer continuously collects the carbon dioxide and oxygen volume concentration values ​​in the exhaust gas throughout the entire fermentation period after the fermentation begins, sampling at a frequency of once per minute. The carbon dioxide volume concentration value collected is subtracted from the initial carbon dioxide volume concentration value to obtain the carbon dioxide concentration increment. The oxygen volume concentration value collected is subtracted from the initial oxygen volume concentration value to obtain the oxygen consumption. The carbon dioxide concentration increment and oxygen consumption are added together and divided by the total fermentation time to obtain the average exhaust gas respiration quotient, which is used as the exhaust gas concentration data.

[0023] Simultaneously with the collection of exhaust gas concentration, the current signal output terminal of the polarographic dissolved oxygen electrode installed in the fermenter is connected to a data logger. The data logger records the dissolved oxygen percentage reading every 10 seconds. After fermentation enters the logarithmic growth phase, starting from the beginning time of the logarithmic phase, the dissolved oxygen percentage reading is read every 30 minutes. The current reading is subtracted from the previous reading and divided by 30 minutes to obtain the dissolved oxygen change rate for the current period. The arithmetic mean of the three smallest dissolved oxygen change rates in all periods of the logarithmic phase is taken as the dissolved oxygen decay slope.

[0024] The cumulative feeding rate is obtained by a mass flow meter at the outlet of the feeding tank. The mass flow meter records the cumulative mass of the feeding liquid every five minutes. Starting from the time of discharge from the fermentation tank, the cumulative mass of the feeding liquid recorded at the time of discharge is subtracted from the cumulative mass of the feeding liquid recorded at the start of fermentation to obtain the total mass of the feeding liquid. The total mass of the feeding liquid is divided by the total fermentation time to obtain the average feeding rate, which is used as the cumulative feeding rate.

[0025] The accumulated temperature and humidity values ​​of the environment are collected by multiple temperature and humidity recorders distributed along the material transfer channel between the fermentation tank and the filling workshop. Each temperature and humidity recorder records the temperature in Celsius and the relative humidity percentage every 10 minutes from the time the tank is unloaded until the filling operation is completed. For each recording time, the temperature in Celsius and the relative humidity percentage are multiplied and then divided by 100 to obtain the weighted temperature and humidity value at that time. The weighted temperature and humidity values ​​of all recording times are summed to obtain the accumulated temperature and humidity value of the environment from unloading to filling.

[0026] The average exhaust gas breathing quotient, dissolved oxygen decay slope, average feeding rate, and ambient temperature and humidity accumulated temperature values ​​collected above are summarized into a batch process record using the tank discharge timestamp as the index. Create a new batch traceability file in the database, using a unique string consisting of the batch number, product code, and production date as the unique identifier. This file includes fields for exhaust gas breathing quotient, dissolved oxygen decay slope, average feed rate, ambient temperature and humidity accumulated temperature, and specific growth rate. The specific growth rate field is left empty, while the other fields are filled with the corresponding collected data. This generates the initial batch traceability file.

[0027] Please see Figure 2 As shown, in S2, the unique identifier and batch traceability file are associated with the pending status marker. A spatiotemporal anchoring association is constructed based on the can release timestamp and filling timestamp, and a temporary file awaiting quality inspection backfilling is generated, specifically including: The specific process of logically binding the unique identifier to the storage address of the batch traceability file and writing it to the pending status flag is as follows: In the file index table of the database, using the unique identifier as the primary key, fill in the starting sector number and offset of the batch traceability file in the physical storage device in the corresponding file storage address field, and write a binary value 0 in the status flag field of the file. This value 0 represents the pending status where the growth rate has not yet been recorded, thus completing the logical binding and writing of the pending status flag.

[0028] The specific process of concatenating the can release timestamp and the filling timestamp in chronological order to form a time axis anchoring interval is as follows: Read the date, time, minute, and second values ​​recorded by the workshop timer when the fermentation tank discharge operation is completed, and use them as the tank discharge timestamp; Read the year, month, day, hour, minute, and second values ​​recorded by the timer of the filling production line when the filling operation is started, and use them as the filling timestamp; Arrange all the numbers of the year, month, day, hour, minute, and second in the can-laying timestamp in their original chronological order as the starting time, and arrange all the numbers of the year, month, day, hour, minute, and second in the filling timestamp in their original chronological order as the ending time. Define the time period range between the starting time and the ending time to obtain the time axis anchoring interval.

[0029] The specific process of mapping the filling production line number to spatial dimension anchor coordinates is as follows: Read the two-dimensional coordinate values ​​of the filling production line that the batch of products actually passed through in the factory layout plan. The two-dimensional coordinate values ​​are composed of the horizontal column spacing number and the vertical column spacing number corresponding to the fixed installation position of the production line. The horizontal column spacing number is used as the first coordinate value of the spatial dimension anchor coordinate, and the vertical column spacing number is used as the second coordinate value of the spatial dimension anchor coordinate, thus forming the spatial dimension anchor coordinate.

[0030] The specific process of generating a spatiotemporal anchored association by associating and combining the time axis anchoring interval as the first component and the spatial dimension anchoring coordinates as the second component is as follows: The start and end times in the time axis anchoring interval are arranged in their original order as the content of the first component. The first and second coordinate values ​​in the spatial dimension anchoring coordinates are arranged in their original order as the content of the second component. The contents of the first component and the contents of the second component are concatenated into a whole data string in a fixed order of time first and space later. This whole data string is the spatiotemporal anchoring association.

[0031] The specific process of encapsulating all reserved null fields of the batch traceability archive with spatiotemporal anchoring associations, using the unique identifier as an index, into a temporary archive awaiting quality inspection backfilling is as follows: In database transactions, the unique identifier is used as the retrieval condition to locate the initial batch traceability file generated by S1. Add a new anchoring association field to the file and write the entire data string of the aforementioned spatiotemporal anchoring association into this anchoring association field; The previously null-valued relative growth rate field in the archive is combined with the newly added anchoring correlation field and other existing fields to form a complete archive data record; Update the status flag field of the complete archive data record from binary value 0 to binary value 1. This value 1 represents an intermediate state where a spatiotemporal anchoring association has been established and the data is waiting for the quality inspection results to be filled in. The updated complete archive data is stored in the temporary archive storage area of ​​the database, thus obtaining a temporary archive awaiting quality inspection and backfilling.

[0032] This temporary file retains all fields of the original collected data and includes spatiotemporal anchoring information, allowing the complete spatiotemporal identity information of the batch of products to be obtained based on this spatiotemporal anchoring information when subsequent quality inspection results are entered.

[0033] Please see Figure 3 As shown, in S3, the measured specific growth rate is obtained after unloading and determined by plate counting. A deviation correction mapping is constructed using ambient temperature, humidity, and accumulated temperature as covariates. This mapping is then used to correct the measured specific growth rate, which is then fed back into the temporary archive. Simultaneously, all fermentation timing process parameters are corrected, generating a unique process decay gene fingerprint for each batch. Specifically, this includes: The specific process for obtaining the measured value of the specific growth rate by plate counting after unloading is as follows: 48 hours after fermentation, the first sample was aseptically taken from the fermentation broth after fermentation, serially diluted tenfold, and spread on nutrient agar plates. The plates were placed in a constant temperature incubator at 35 degrees Celsius and incubated for 24 hours. The number of colony-forming units was counted to obtain the actual number of viable bacteria 48 hours after fermentation. The viable count ratio is obtained by dividing the measured viable count at 48 hours after unloading by the initial viable count at the time of unloading. Calculate the natural logarithm of the viable cell count ratio; use the 48-hour period from the time the fermentation tank is removed to the 48th hour as the time interval. The specific growth rate of this batch is calculated by subtracting the natural logarithm of the initial live bacteria count at the time of discharge from the natural logarithm of the measured live bacteria count 48 hours after discharge, and then dividing the difference by the time interval of 48 hours.

[0034] The specific process of constructing a bias correction mapping using ambient temperature, humidity, and accumulated temperature as covariates is as follows: From the accumulated temperature and humidity values ​​of the environment during the period from canning to filling collected in step S1, the first segment starts at midnight on the first day after canning, the first segment ends at midnight on the second day after canning and also serves as the starting point of the second segment, and so on, dividing each complete natural day in the natural number of days after canning into an accumulated temperature segment. For each accumulated temperature segment, all weighted temperature and humidity values ​​recorded in step S1 within that segment are summed to obtain the accumulated temperature accumulation for that segment; the accumulated temperature accumulation corresponding to the first natural day after the tank is unloaded is selected as the baseline value. Divide the accumulated temperature of each subsequent natural day starting from the second natural day by the baseline value to obtain the accumulated temperature deviation ratio for each subsequent natural day. The accumulated temperature deviation ratios are arranged sequentially according to the time of day to form a deviation ratio sequence, which is then used as a deviation correction mapping.

[0035] When the accumulated temperature of subsequent natural days is greater than the baseline value, the corresponding accumulated temperature deviation ratio is greater than 1, indicating that the environmental accumulated temperature of that day is higher than that of the first day. When the accumulated temperature of subsequent natural days is less than the baseline value, the corresponding accumulated temperature deviation ratio is less than 1, indicating that the environmental accumulated temperature of that day is lower than that of the first day.

[0036] The measured specific growth rate is multiplied one by one with each deviation ratio in the deviation ratio sequence to obtain the corrected rate value for each time segment. The corrected rate value is calculated according to the following formula: ;in, Indicates the first The correction rate value corresponding to each natural day. This represents the measured value of the specific growth rate obtained from the aforementioned calculations. Indicating the first in the deviation ratio sequence The accumulated temperature deviation ratio corresponding to each natural day.

[0037] When the deviation ratio is greater than 1, the correction rate value is greater than the measured growth rate value, indicating that the decay of live bacteria is accelerated due to the high accumulated temperature and humidity of the environment on that natural day. When the deviation ratio is less than 1, the correction rate value is less than the measured growth rate value, indicating that the degradation of live bacteria is slowed down due to the low accumulated temperature and humidity of the environment on that natural day. When the deviation ratio is equal to 1, the correction rate value is equal to the measured value of the specific growth rate, indicating that the environmental conditions of that day are consistent with those of the first day, and no correction is required. Thus, the correction rate values ​​corresponding to each day are obtained.

[0038] The corrected rate values ​​are backfilled into the ratio growth field of the corresponding natural day in the temporary file according to the chronological order of the natural day, forming the corrected ratio growth time series; The value of the first day in the specific growth time series is equal to the measured specific growth rate, and the values ​​of each day from the second day onwards are equal to the corrected rate values ​​corresponding to each day.

[0039] The specific process of adjusting the induction timing markers based on the growth time series is as follows: Read the induction timing flag at each time point recorded in the temporary file. When the induction timing flag is 1, it means that the induction operation was performed at that time point, and when it is 0, it means that it was not performed. Starting from the second calendar day, subtract the correction rate value of the current day from the correction rate value of the previous calendar day. If the difference is positive and greater than 0.01, then the induction timing mark corresponding to that day is adjusted to 1. If the difference is negative or not greater than 0.01, the original induction timing mark for that day remains unchanged.

[0040] The specific process for adjusting the timing of material replenishment is as follows: read the cumulative material replenishment quality value recorded in the temporary file for each material replenishment execution; for each natural day starting from the second natural day, divide the correction rate value of that day by the correction rate value of the first day to obtain the material replenishment adjustment ratio; Multiply the original recorded feed execution time (in minutes) by the feed adjustment ratio to obtain the adjusted feed execution time (in minutes). If the adjusted feed execution time (in minutes) is greater than 60, subtract 60 from the minute value and increase the hour value by 1 to obtain the adjusted feed execution time. Combine the adjusted induction timing markers and feed execution times at all time points, along with the original exhaust gas respiration quotient average, dissolved oxygen decay slope, average feed rate, and corrected specific growth time series in the temporary file, in chronological order to form a complete process parameter record. This process parameter record is the unique process decay fingerprint for this batch.

[0041] This process decay gene fingerprint encompasses the daily metabolic adjustment information of this batch from the first day after unloading to the day of filling completion, allowing subsequent queries to obtain all adaptive changes in process parameters caused by environmental temperature fluctuations.

[0042] In S4, the process decay gene fingerprint is irreversibly hashed and stored, and then bound to the batch traceability file to generate a solidified batch traceability file, which specifically includes: The specific process of obtaining a compressed fixed-length summary value by segmenting and compressing the gene fingerprint of the decay process is as follows: The process decay gene fingerprint generated in step S3 is read into a continuous digital sequence in chronological order. This digital sequence consists of three types of numbers arranged alternately in chronological order: induction timing marker, feeding execution time, and corrected specific growth rate value. Starting from the first digit of the number sequence, the number sequence is divided into multiple data blocks with a fixed length of 128 bits as the dividing standard. If the last data block is less than 128 bits, binary 0s are padded to the end until it reaches 128 bits, thus obtaining multiple data blocks of equal length. For each data block, the product of each digit in the data block and its position is accumulated to obtain the sum of the data block. Add the value of each digit in the data block to its position and multiply it by the value of that digit to get the weighted value of that digit. Sum the weighted values ​​of all digits to get the representative value of the data block. The data blocks are concatenated end to end according to their original order to obtain the concatenated intermediate value. The concatenated intermediate value is cyclically shifted and mixed according to a fixed length of 256 bits. The specific cyclic shifting and mixing method is as follows: the concatenated intermediate value is divided into a first half and a second half of 128 bits each. The second half is moved to the front of the first half. The entire sequence after the shift is divided into 8 groups of 32 bits each. The positions of the first group and the eighth group are swapped, the second group and the seventh group are swapped, the third group and the sixth group are swapped, and the fourth group and the fifth group are swapped. After the swapping is completed, all groups are reassembled in the new order to obtain a fixed-length digest value. The length of the fixed-length digest value is fixed at 256 bits.

[0043] The specific process of associating a fixed-length digest value with the storage address of the batch traceability file to generate an associated file with the storage identifier is as follows: take the aforementioned fixed-length digest value as the starting segment, take the complete path string of the storage address of the batch traceability file in the database as the subsequent segment, and concatenate the first and second parts of the starting segment and the subsequent segment in order, that is, the last part of the fixed-length digest value is followed by the first part of the storage address, thus obtaining the concatenated storage string. Read the total character length of the concatenated evidence string, divide the total character length by 4 and take the integer part to obtain the span base; Using the span base number as a fixed step size, starting from the first character of the concatenated evidence string, extract a character position every span base number of characters, and arrange these extracted character positions in the order of extraction into an interlacing position sequence; Based on the interleaving position sequence, each character in the fixed-length digest value is inserted one by one into the corresponding position in the character sequence of the storage address according to the position indicated by the value of the corresponding position in the interleaving position sequence, thus forming a fused evidence string. A fixed character "#" is appended before the first character of the merged evidence string as an evidence identifier, thereby generating an associated file.

[0044] The specific process of writing the associated files along with the solidified evidence timestamp into the immutable evidence node to generate the solidified batch traceability files is as follows: Read the current time's year, month, day, hour, minute, and second values ​​as a fixed, stored timestamp; The aforementioned associated files and solidified evidence timestamps are concatenated in chronological order to form a single evidence record, i.e., the last digit of the associated file is immediately followed by the first digit of the solidified evidence timestamp. The entire evidence record is written into the storage area of ​​a pre-deployed blockchain evidence node. This blockchain evidence node uses distributed ledger technology. After writing, the entire evidence record is appended with the block height value and the hash value of the previous block, so that the record cannot be modified or deleted once it is written. The storage index number of the overall evidence record in the blockchain evidence record node is backfilled into the evidence record index field of the batch traceability file, thereby generating the solidified batch traceability file. The solidified batch traceability file contains a complete correspondence between the original collected data, the process decay gene fingerprint, the evidence record identifier and the solidified evidence record timestamp.

[0045] In S5, in response to a terminal's barcode scanning query request, the process decay gene fingerprint is extracted from the batch traceability file solidified under the current query timestamp, associated with the temperature accumulation fluctuation sequence in the distribution process, and the current effective viable bacteria status information and application window prompts are generated, specifically including: The specific process of extracting the process attenuation gene fingerprint from the solidified batch traceability file and accumulating the temperature deviation values ​​of each cold chain node recorded chronologically in the circulation process into a temperature cumulative fluctuation sequence is as follows: Read the process decay gene fingerprint in the solidified batch traceability file. The fingerprint contains the correction rate value of each natural day arranged in chronological order. At the same time, the temperature values ​​in Celsius recorded at each cold chain monitoring node in the circulation process of this batch of products are read. The cold chain monitoring nodes include the factory cold storage node, the departure node of the transport vehicle, the midway node of the transport, the arrival node of the transport vehicle, the cold storage node of the distributor, and the terminal retail node, totaling 6 nodes. At each node, the recorded temperature value in Celsius is subtracted from the standard storage and transportation temperature in Celsius specified for that batch of products to obtain the temperature deviation value for that node; The temperature deviation values ​​of the six nodes are accumulated in chronological order. The temperature deviation value of the first node is used as the initial accumulated value. The temperature deviation value of the second node is added to the initial accumulated value to obtain the second accumulated value, and so on, until the temperature deviation value of the sixth node is accumulated. The resulting six accumulated values ​​are then arranged in chronological order to form a temperature cumulative fluctuation sequence.

[0046] The specific process of subtracting the daily correction rate value in the chronological decay gene fingerprint from the corresponding daily cumulative fluctuation value in the temperature cumulative fluctuation sequence to obtain the retention rate after decay for each day is as follows: The temperature cumulative fluctuation sequence contains 6 cumulative values, which correspond to the 6 cold chain stages from the factory cold storage node to the terminal retail node; The correction rate values ​​of the first 6 natural days in the process decay gene fingerprint are subtracted one by one from the 6 cumulative values ​​in the temperature cumulative fluctuation sequence in the natural day order. That is, the correction rate value of the first natural day is subtracted from the first cumulative value in the temperature cumulative fluctuation sequence to obtain the retention rate after decay of the first natural day. The retention rate after decay on the second natural day is obtained by subtracting the second cumulative value in the temperature cumulative fluctuation sequence from the correction rate value on the second natural day. This process continues until the correction rate value on the sixth natural day is subtracted from the sixth cumulative value in the temperature cumulative fluctuation sequence to obtain the retention rate after decay on the sixth natural day. For days beyond the first 6 days, if there is no corresponding cumulative value in the temperature cumulative fluctuation sequence, the decayed retention rate of subsequent days is calculated by multiplying the decayed retention rate of the previous day by 0.95 to obtain the decayed retention rate of each subsequent day.

[0047] The specific process of concatenating the retention rates after decay on each natural day in chronological order to form a decay trajectory, and generating the current effective viable bacteria status information and application window prompts based on the comparison results of the decay trajectory and the preset activity threshold, is as follows: Arrange the retention rates after decay for each natural day in chronological order to form a decay trajectory that decreases with the number of natural days; read the natural day number corresponding to the current query time point, and extract the retention rate after decay corresponding to that natural day number from the decay trajectory as the current retention rate value. The current retention rate value is compared with a preset first activity threshold and a preset second activity threshold, wherein the first activity threshold is set to 80% of the nominal effective viable count of the batch of products at the time of manufacture, and the second activity threshold is set to 50% of the nominal effective viable count of the batch of products at the time of manufacture.

[0048] If the current retention rate value is greater than the first activity threshold, it is determined that the current activity is in a surplus state, and activity surplus state information is generated. This information includes the specific value of the current retention rate value. At the same time, a window prompt that allows normal application is generated. This prompt includes text guidance suggesting that the product be applied at the regular dosage within the next 3 calendar days.

[0049] If the current retention rate value is less than or equal to the first activity threshold and greater than or equal to the second activity threshold, the current activity is determined to be in a critical state, and activity critical state information is generated. This information includes the current retention rate value and the difference between the value and the first activity threshold. At the same time, a limited application window prompt is generated, which includes text guidance that it is recommended to apply within the next calendar day and that the application amount should be increased by 20% on the basis of the regular dosage.

[0050] If the current retention rate value is less than the second activity threshold, the current activity is determined to be insufficient, and an insufficient activity status information is generated. This information includes the current retention rate value and the difference between the current value and the second activity threshold. At the same time, a postponement application window prompt is generated. This prompt includes text guidance that application is not recommended at present, and additional text suggesting that a viable count test be performed again before application.

[0051] The working principle of this invention is as follows: After the fermentation process begins, the concentration of exhaust gas, dissolved oxygen decay slope, feed accumulation rate, and ambient temperature and humidity accumulated temperature values ​​during the fermentation process are collected. This generates an initial batch traceability file with a unique identifier and a pre-defined null value for the specific growth rate field. The unique identifier is then associated with a pending status marker. A spatiotemporal anchoring association is constructed based on the fermentation timestamp and filling timestamp, generating a temporary file awaiting quality inspection feedback. The measured specific growth rate value from plate counting is obtained. A deviation correction mapping is constructed using ambient temperature and humidity accumulated temperature values ​​as covariates. The measured specific growth rate value is corrected and fed back into the temporary file. Simultaneously, all fermentation time-series process parameters are corrected, generating a process decay gene fingerprint. This process decay gene fingerprint is irreversibly hashed and bound to the batch traceability file, generating a solidified file. In response to terminal scanning and querying, the process decay gene fingerprint in the solidified file is extracted and associated with the temperature accumulation fluctuation sequence in the circulation process, generating current effective viable cell status information and application window prompts.

[0052] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for intelligent traceability of the entire life cycle quality of agricultural microbial agents, characterized in that, Includes the following steps: S1, collect the exhaust gas concentration, dissolved oxygen decay slope, feeding accumulation rate and ambient temperature and humidity accumulated temperature values ​​during the entire fermentation process after the tank is opened, generate an initial batch traceability file, bind a unique identifier to the batch traceability file and reserve the specific growth rate field as null. S2, associate the unique identifier and batch traceability file with the pending status marker, construct a spatiotemporal anchoring association based on the can release time stamp and filling time stamp, and generate a temporary file waiting for quality inspection backfilling; S3. Obtain the measured value of specific growth rate after tank loading by plate counting. With ambient temperature, humidity and accumulated temperature as covariates, construct a deviation correction mapping. Use the deviation correction mapping to correct the measured value of specific growth rate. Feed the corrected measured value of specific growth rate back into the temporary file and simultaneously correct all fermentation time process parameters to generate a unique process decay gene fingerprint for the corresponding batch. S4. The process decay gene fingerprint is irreversibly hashed and stored, and then bound to the batch traceability file to generate a solidified batch traceability file. S5, in response to the terminal's QR code scanning query request, extracts the process decay gene fingerprint from the batch traceability file solidified under the current query timestamp, associates it with the temperature accumulation fluctuation sequence in the circulation process, and generates the current effective live bacteria status information and application window prompts.

2. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 1, characterized in that, The generation of temporary files awaiting quality inspection backfilling specifically includes: Logically bind the unique identifier to the storage address of the batch traceability file and write it to the pending status marker; The can release timestamp and filling timestamp are concatenated in chronological order to form a time axis anchoring interval. The filling production line number is then used as the spatial dimension anchoring coordinate and combined with the time axis anchoring interval to form a spatiotemporal anchoring association. Using a unique identifier as an index, all reserved null fields in the batch traceability archive are encapsulated together with the spatiotemporal anchoring association into a temporary archive awaiting quality inspection backfilling.

3. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 2, characterized in that, The process of combining the spatiotemporal anchoring association is as follows: The can placement timestamp and the filling timestamp are concatenated in chronological order to generate a time axis anchoring interval; Map the filling production line number to spatial dimension anchor coordinates; By combining the time axis anchoring interval as the first component and the spatial dimension anchoring coordinates as the second component, a spatiotemporal anchoring association is generated.

4. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 1, characterized in that, The construction of the deviation correction mapping specifically includes: The accumulated temperature and humidity values ​​of the environment are divided into multiple accumulated temperature segments according to the daily time period of the natural days after the tank is placed, and the accumulated temperature accumulation amount corresponding to each accumulated temperature segment is calculated. Using the accumulated temperature on the first day after the tank is opened as the baseline value, the accumulated temperature of each subsequent temperature segment is compared with the baseline value to generate the accumulated temperature deviation ratio corresponding to each segment. The accumulated temperature deviation ratios are arranged in chronological order to form a deviation ratio sequence, and this deviation ratio sequence is used as a deviation correction mapping.

5. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 1, characterized in that, The process of generating the decaying genetic fingerprint is as follows: The measured specific growth rate is multiplied one by one with each deviation ratio in the deviation ratio sequence to obtain the corrected rate value for each time segment. The corrected rate values ​​are backfilled into the ratio growth field of the corresponding time point in the temporary file in time segment order to form the corrected ratio growth time series. Based on the growth time series, the induction timing marker and feeding execution time at each time point in the temporary file are adjusted one by one to make the two synchronously correlated with the rise and fall trend of the growth time series. All adjusted process parameters were combined in chronological order to form a process decay gene fingerprint.

6. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 1, characterized in that, The generated and solidified batch traceability file specifically includes: The process decay gene fingerprint is segmented and compressed to obtain a compressed fixed-length summary value; A fixed-length digest value is used as the evidence identifier, and it is associated with the storage address of the batch traceability file to generate an associated file with the evidence identifier. The associated files, along with the solidified evidence timestamp, are written into the immutable evidence node to generate the solidified batch traceability file.

7. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 6, characterized in that, The obtained compressed fixed-length digest value specifically includes: The chronological decay gene fingerprint is divided into multiple data blocks of equal length in chronological order; Calculate the weighted sum of all feature values ​​within each data block to obtain the block representative value corresponding to each data block; The representative values ​​of each block are combined according to the original order of the data blocks to obtain the combined intermediate value; The merged intermediate values ​​are cyclically shifted and mixed by a fixed number of bits to obtain a fixed-length summary value.

8. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 6, characterized in that, The generation of associated files with evidence storage identifiers specifically includes: The fixed-length digest value is used as the starting segment and concatenated with the first and last characters of the storage address to obtain the concatenated evidence string. Using the total length of the concatenated evidence string as the span base, the position sequence is extracted from it at fixed intervals to generate the interlaced position sequence; Based on the interleaving position sequence, the fixed-length digest value is interleaved bit by bit between the corresponding bits in the storage address to obtain the fused evidence string; The first part of the merged evidence string is appended with an evidence identifier to generate an associated file.

9. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 1, characterized in that, The generation of current effective viable bacteria status information and application window prompts specifically includes: Extract the process decay gene fingerprint from the solidified batch traceability file, and accumulate the temperature deviation values ​​of each cold chain node recorded in the circulation process in chronological order to form a temperature cumulative fluctuation sequence. The retention rate after decay of each segment is obtained by subtracting the correction rate value of each time segment in the process decay gene fingerprint from the cumulative fluctuation value of the corresponding time segment in the temperature cumulative fluctuation sequence. The retention rates of each segment after decay are concatenated in chronological order to form a decay trajectory. Based on the comparison between the decay trajectory and the preset activity threshold, the current effective viable bacteria status information and application window prompts are generated.

10. The intelligent traceability method for the entire life cycle quality of agricultural microbial agents according to claim 9, characterized in that, The current effective live bacteria status information and application window specifically include: The decayed retention rate corresponding to the current query time point in the decay trajectory is compared step by step with the preset first activity threshold and second activity threshold. The first activity threshold is greater than the second activity threshold. When the retention rate after decay is greater than the first activity threshold, an activity surplus status information and a window prompt indicating that normal application is allowed are generated. When the retention rate after decay is between the first activity threshold and the second activity threshold, information on the critical state of activity and a prompt for a limited application window are generated. When the retention rate after decay is less than the second activity threshold, an insufficient activity status message and a prompt to postpone application are generated.

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