Bacillus bio-organic fertilizer fermentation condition monitoring and optimizing method

Abstract

The invention relates to the technical field of bio-organic fertilizer fermentation monitoring, in particular to a bacillus bio-organic fertilizer fermentation condition monitoring and optimizing method. The method comprises the steps of obtaining a data change degree characterization value and a related difference characterization value of a first detection point, obtaining a target bacillus acid production characterization value of the first detection point according to the data change degree characterization value and the related difference characterization value of the first detection point, and obtaining a target bacillus acid production characterization value of the second detection point according to the target bacillus acid production characterization value. Screening the first detection point again, obtaining a predicted PH value at a future monitoring moment according to a screening result, and monitoring the fermentation process of the bacillus bio-organic fertilizer according to the predicted PH value at the future monitoring moment. And the timeliness or timeliness of monitoring the pH value of the fermentation liquid in the fermentation tank can be improved by predicting the pH value.

Description

Technical Field

[0001] The invention relates to the technical field of bio-organic fertilizer fermentation monitoring, and in particular to a method for monitoring and optimizing fermentation conditions of bacillus bio-organic fertilizer. Background Art

[0002] In the process of bacillus bio-organic fertilizer fermentation, as in the process of Velez bacillus TA-1 fermentation, in order to ensure fermentation quality, the production quality or production efficiency of bio-organic fertilizer, the pH value of fermented liquid in fermentor tank will usually be monitored; and due to in the process of bacillus bio-organic fertilizer fermentation, lactic acid and acetic acid are easily produced, when the lactic acid and acetic acid produced are too much, the pH value of the fermented liquid in fermentor tank will be caused to decline or the phenomenon of overacidity will be caused in fermentor tank, that is, overacidity refers to that the acidic characteristic of solution in fermentor tank is stronger, and when the pH value of fermented liquid in fermentor tank is too small, fermentation quality, fermentation speed, etc. will be directly affected, and then the production quality or production efficiency of bio-organic fertilizer will also be affected, therefore in the process of bacillus bio-organic fertilizer fermentation, it is crucial to monitor the pH value of the fermented liquid in fermentor tank, that is, it is crucial to monitor the overacidity phenomenon, and described fermentor tank is the container of bacillus bio-organic fertilizer fermentation.

[0003] In the prior art, the real-time monitored pH value is usually monitored based on an empirical value or a fixed threshold value, and the pH value is the pH value in the fermentation tank. However, this monitoring method has the problem of poor monitoring timeliness. For example, in the process of monitoring the pH value in the fermentation tank based on an empirical value or a fixed threshold value, if the pH value in the fermentation tank collected at the current monitoring time is less than the preset threshold value, then the pH value of the solution in the fermentation tank has become too acidic, which has already affected the fermentation process and will also affect the final fermentation result. Therefore, it is necessary to optimize the monitoring process, that is, in the process of Bacillus bio-organic fertilizer fermentation, how to optimize the process of monitoring the pH value of the fermentation liquid in the fermentation tank to ensure the timeliness or timeliness of monitoring has become a problem that needs to be solved urgently. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method for monitoring and optimizing fermentation conditions of Bacillus bio-organic fertilizer, and the technical scheme adopted is as follows:

[0005] An embodiment of the present invention provides a method for monitoring and optimizing fermentation conditions of Bacillus bio-organic fertilizer, comprising the following steps:

[0006] Obtaining pH monitoring time series corresponding to different detection points in a fermentation tank, wherein the fermentation tank is a container for fermenting Bacillus bio-organic fertilizer;

[0007] According to the data in the pH monitoring time series, the detection points are screened to obtain a first detection point, and according to the pH monitoring time series corresponding to the first detection point, a target sequence corresponding to the first detection point is obtained, and according to the difference between adjacent pH values ​​in the target sequence, a data change degree characterization value of the first detection point is obtained; according to the length of the target sequence, a set of sequences to be analyzed for the first detection point is obtained, and according to the correlation between the target sequence corresponding to the first detection point and each sequence to be analyzed in the set of sequences to be analyzed corresponding to the first detection point, a correlation difference characterization value of the first detection point is obtained; according to the data change degree characterization value and the correlation difference characterization value of the first detection point, a target Bacillus acid production characterization value of the first detection point is obtained;

[0008] According to the target bacillus acid production characterization value, the first detection point is screened again, and the predicted pH value at the future monitoring time is obtained according to the screening result, and the bacillus bio-organic fertilizer fermentation process is monitored according to the predicted pH value at the future monitoring time.

[0009] Beneficial effects: The present invention first obtains the pH monitoring time series corresponding to different detection points in the fermentation tank; then, according to the data in the pH monitoring time series, the detection points are screened to obtain the first detection point, and according to the pH monitoring time series corresponding to the first detection point, the target sequence corresponding to the first detection point is obtained, according to the difference between adjacent pH values ​​in the target sequence, the data change degree characterization value of the first detection point is obtained, according to the length of the target sequence, the sequence set to be analyzed of the first detection point is obtained, and according to the correlation between the target sequence corresponding to the first detection point and each sequence to be analyzed in the sequence set to be analyzed corresponding to the first detection point, the correlation difference characterization value of the first detection point is obtained; then, according to the data change degree characterization value and the correlation difference characterization value of the first detection point, the target Bacillus acid production characterization value of the first detection point is obtained; finally, according to the target Bacillus acid production characterization value, the first detection point is screened again, and the predicted pH value at the future monitoring time is obtained according to the screening result, and the fermentation process of the Bacillus bio-organic fertilizer is monitored according to the predicted pH value at the future monitoring time. Moreover, the present invention can timely detect the overacidity phenomenon when it has not yet occurred but there is a trend or risk of overacidity phenomenon by predicting the pH value, and take corresponding measures to avoid the occurrence of overacidity phenomenon, that is, the present invention can improve the timeliness or effectiveness of monitoring the pH value of the fermentation liquid in the fermentation tank by predicting the pH value. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 The present invention provides a flow chart of a method for monitoring and optimizing fermentation conditions of Bacillus bio-organic fertilizer. DETAILED DESCRIPTION

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the embodiments of the present invention.

[0013] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0014] This embodiment provides a method for monitoring and optimizing fermentation conditions of Bacillus bio-organic fertilizer, which is described in detail as follows:

[0015] like Figure 1 As shown, the method for monitoring and optimizing fermentation conditions of Bacillus bio-organic fertilizer comprises the following steps:

[0016] Step S001, obtaining the pH monitoring time sequence corresponding to different detection points in a fermentation tank, wherein the fermentation tank is a container for fermenting Bacillus bio-organic fertilizer.

[0017] Since the problem of poor monitoring timeliness occurs when the fermentation process of bacillus bio-organic fertilizer is currently monitored, and when poor monitoring timeliness occurs, it will affect the fermentation quality, the production quality or production efficiency of the bio-organic fertilizer, etc. Therefore, in order to improve the monitoring timeliness as much as possible, this embodiment will optimize the existing monitoring process, thereby improving the timeliness or timeliness of monitoring, and the existing monitoring process refers to monitoring the pH value monitored in real time based on an empirical value or a fixed threshold value, and the pH value is the pH value of the solution in the fermentation tank; since the bacillus bio-organic fertilizer is usually fermented in a fermentation tank, the fermentation The process is monitored, that is, the pH value of the solution in the fermentation tank is monitored, that is, the fermentation tank is a container for the fermentation of bacillus bio-organic fertilizer, and the fermentation tank in this embodiment is a non-mechanical stirring and ventilated fermentation tank; in addition, due to the large number of types of bacillus, this embodiment will subsequently describe the monitoring process of the fermentation of bacillus bio-organic fertilizer of any type as an example. For example, in this embodiment, the bacillus that appear later are all Bacillus Velez TA-1, which is a bacterium belonging to the genus Bacillus, which can be used as a control agent for plant diseases, especially has important application prospects for the disaster protection of plant pathogenic nematodes.

[0018] In addition, for ease of understanding and analysis, this embodiment only monitors the pH value of the solution in any fermentation tank, that is, the pH values ​​collected subsequently are all the pH values ​​of the solution in the same fermentation tank, and this embodiment mainly monitors the overacidification phenomenon caused by the acidic substances produced by fermentation. The acidic substances produced by fermentation include but are not limited to lactic acid and acetic acid, that is, this embodiment mainly monitors the changes in the pH value in the fermentation tank caused by the acidic substances, and when the overacidification phenomenon is detected or the risk of overacidification is high, the pH of the solution in the fermentation tank is immediately adjusted, such as injecting an alkaline solution.

[0019] First, in order to ensure the accuracy of monitoring, the present embodiment arranges detection points at different positions in the fermenter, and the number and distribution positions of the detection points in the fermenter need to be adaptively set according to the actual conditions such as the volume of the fermenter and the monitoring accuracy. For example, if the volume of the fermenter is small, the number of detection points in the fermenter can be set to 4, and if the volume of the fermenter is large, the number of detection points in the fermenter can be set to 8. The detection points in the fermenter can also be set to be uniformly distributed or randomly distributed. After determining the detection points in the fermenter, the detection instrument is used to obtain the corresponding detection points in the fermenter at each monitoring time in the current monitoring time period. PH value, and then the time series constructed by all PH values ​​corresponding to each detection point obtained in the current monitoring time period is recorded as the PH monitoring time series corresponding to the corresponding detection point, and the current monitoring time period includes the current monitoring moment, then the last PH value in the PH monitoring time series is the PH value collected at the current monitoring moment, and the detection instrument refers to the PH sensor; in addition, in specific applications, the implementer needs to set the time interval between adjacent monitoring moments and the duration corresponding to the current monitoring time period according to actual conditions. For example, in this embodiment, the time interval between adjacent monitoring moments can be set to 1 second, and the duration of the current monitoring time period can be set to 2 minutes.

[0020] Therefore, this embodiment can obtain the pH monitoring time sequence corresponding to each detection point in the fermentation tank during the current monitoring time period through the above process.

[0021] Step S002, according to the data in the pH monitoring time series, the detection points are screened to obtain the first detection point, and according to the pH monitoring time series corresponding to the first detection point, the target sequence corresponding to the first detection point is obtained, and according to the difference between adjacent pH values ​​in the target sequence, the data change degree representation value of the first detection point is obtained; according to the length of the target sequence, the set of sequences to be analyzed for the first detection point is obtained, and according to the correlation between the target sequence corresponding to the first detection point and each sequence to be analyzed in the set of sequences to be analyzed corresponding to the first detection point, the correlation difference representation value of the first detection point is obtained; according to the data change degree representation value and the correlation difference representation value of the first detection point, the target Bacillus acid production representation value of the first detection point is obtained.

[0022] Next, this embodiment will obtain the predicted pH value in the fermentation tank at the future monitoring time by analyzing the acid production degree of Bacillus at the detection point, that is, the target Bacillus acid production characterization value, and finally monitor the fermentation process of Bacillus bio-organic fertilizer based on the predicted pH value, and the timeliness or timeliness of monitoring the fermentation process of Bacillus bio-organic fertilizer based on the predicted pH value can be improved; however, before obtaining the acid production degree of Bacillus, it is necessary to first screen all the detection points according to the data in the pH monitoring time series corresponding to each detection point to obtain the first detection point, and then obtain the target sequence corresponding to the first detection point according to the pH monitoring time series sequence corresponding to the first detection point, and obtain the data change degree characterization value of the first detection point according to the difference between adjacent pH values ​​in the target sequence, and the The data change degree characterization value can reflect the pH decrease phenomenon caused by the presence of acidic substances at the corresponding detection point; then, according to the length of the target sequence, the set of sequences to be analyzed for the first detection point is obtained, and according to the correlation between the target sequence corresponding to the first detection point and each sequence to be analyzed in the set of sequences to be analyzed corresponding to the first detection point, the correlation difference characterization value of the first detection point is obtained, and the correlation difference characterization value can further reflect whether there is a pH decrease phenomenon caused by acidic substances at the corresponding detection point; then, according to the obtained data change degree characterization value and the correlation difference characterization value of the first detection point, the target Bacillus acid production characterization value of the first detection point is obtained. The acidic substances mentioned in this embodiment all refer to acidic substances produced as the Bacillus fermentation process proceeds.

[0023] Therefore, based on the above analysis, it can be known that in this embodiment, all the detection points need to be screened according to the data in the pH monitoring time series corresponding to each detection point to obtain the first detection point, and the purpose of screening is to obtain the detection point of the pH decrease phenomenon caused by the suspected presence of acidic substances at the current monitoring moment. Then, in this embodiment, all the detection points are screened according to the data in the pH monitoring time series corresponding to each detection point to obtain the specific process of the first detection point as follows:

[0024] First, according to the data in the pH monitoring time series corresponding to each detection point, the suspected fermentation acid production characterization value corresponding to each detection point is obtained, and the suspected fermentation acid production characterization value can reflect the possibility of a decrease in pH value caused by the suspected presence of acidic products at the corresponding detection point position during the fermentation of the Bacillus bio-organic fertilizer; and in this embodiment, the specific acquisition process of the suspected fermentation acid production characterization value corresponding to each detection point is as follows: for any detection point: first, the second to last pH value and the last pH value in the pH monitoring time series corresponding to the detection point are recorded as the first data value and the second data value, respectively, and The result obtained by subtracting the second data value from the first data value is recorded as the first difference, and then a continuous preset number of data before the last PH value in the PH monitoring time series sequence corresponding to the detection point is obtained, and the new sequence consisting of the obtained continuous preset number of data is recorded as a characteristic subsequence, and then the absolute value of the difference between the mean of the characteristic subsequence and the second data value is obtained, and recorded as the second difference, the reciprocal of the result obtained by adding the second difference and the preset first constant is obtained, and recorded as the first ratio; finally, the product of the first difference and the first ratio is obtained, and recorded as the suspected fermentation acid production characterization value corresponding to the detection point.

[0025] Therefore, this embodiment can obtain the suspected fermentation acid production characterization value corresponding to each detection point through the above process, and the larger the suspected fermentation acid production characterization value corresponding to the detection point, the more likely it is that the pH value change at the detection point position at the current monitoring time is caused by acidic substances. Therefore, after obtaining the suspected fermentation acid production characterization value corresponding to each detection point, the detection point is initially screened, and the specific process of the initial screening is as follows: for any detection point, first determine whether the suspected fermentation acid production characterization value corresponding to the detection point is a positive number. If so, continue to determine whether the normalized value of the suspected fermentation acid production characterization value corresponding to the detection point is greater than or equal to the preset suspected judgment threshold. If so, it is determined that the pH value change at the detection point position at the current monitoring time is more likely to be caused by acidic substances. Decrease in pH, and further analysis is required. Therefore, this detection point is recorded as the first detection point at this time. In addition, if the suspected fermentation acid production characterization value corresponding to a detection point is positive, then the normalized value of the suspected fermentation acid production characterization value corresponding to the detection point is greater than or equal to the preset suspected judgment threshold. If so, it is determined that the pH value change at the detection point position at the current monitoring time is more likely to be caused by acidic substances. Decrease in pH, and further analysis is required. Therefore, at this time, the detection point is recorded as the first detection point. If the acid production characterization value is a negative number, it indicates that the second to last pH value in the pH monitoring time series corresponding to the detection point is less than the last pH value in the pH monitoring time series corresponding to the detection point, which indicates that there is no pH decrease caused by the presence of acidic substances at the detection point at the current detection time. Moreover, if the suspected fermentation acid production characterization values ​​corresponding to the detection points are all negative numbers, it is directly determined that the fermentation process at the current moment may not have acidic substances. Therefore, there will be no over-acidification in the fermentation tank for a long period of time in the future. Therefore, there is no need to adjust the acidity and alkalinity of the solution in the fermentation tank at the current monitoring moment. That is, if the suspected fermentation acid production characterization values ​​corresponding to the detection points are all negative numbers, it is determined that there is no need to inject alkaline solution into the fermentation tank at the current monitoring moment. The normalized value of the above-mentioned suspected fermentation acid production characterization value refers to the value obtained by normalizing the corresponding suspected fermentation acid production characterization value using the normalization function Norm().

[0026] In specific applications, the implementer needs to set the preset suspected judgment threshold, the preset number and the preset first constant according to the actual situation. For example, in this embodiment, the preset suspected judgment threshold can be set to 0.6, the preset number can be set to 100, and the preset first constant can be set to 0.1.

[0027] Therefore, this embodiment completes the initial screening of the detection points through the above process, and obtains all the first detection points in all the detection points. After obtaining the first detection points, the pH monitoring time series corresponding to each first detection point is analyzed to obtain the target sequence corresponding to the first detection point. The subsequent analysis of the target sequence can more accurately obtain the possibility of pH decrease caused by acidic substances at each first detection point at the current monitoring moment. Therefore, the specific acquisition process of the target sequence corresponding to the first detection point in this embodiment is:

[0028] For any first detection point: first determine whether the sequence formed by the ABth PH value to the Ath PH value in the PH monitoring timing sequence corresponding to the first detection point is a decreasing sequence and whether the sequence formed by the A-(B-1)th PH value to the Ath PH value in the PH monitoring timing sequence corresponding to the first detection point is not a decreasing sequence, and if it is determined that the sequence formed by the ABth PH value to the Ath PH value in the PH monitoring timing sequence corresponding to the first detection point is a decreasing sequence, and the sequence formed by the A-(B-1)th PH value to the Ath PH value in the PH monitoring timing sequence corresponding to the first detection point is not a decreasing sequence, then the sequence formed by the ABth PH value to the Ath PH value in the PH monitoring timing sequence corresponding to the first detection point is recorded as the target sequence corresponding to the first detection point, and when the sequence formed by the ABth PH value to the Ath PH value in the PH monitoring timing sequence corresponding to the first detection point is a decreasing sequence and the first When the sequence formed by the A-(B-1)th PH value to the Ath PH value in the PH monitoring time series sequence corresponding to the detection point is not a decreasing sequence, it indicates that starting from the ABth PH value in the PH monitoring time series sequence corresponding to the first detection point, a decrease in PH caused by acidic substances may have occurred at the position of the first detection point, and as the fermentation time becomes longer and longer, the production of acidic substances will gradually increase, that is, as the fermentation time increases, the PH value of the solution in the fermentation tank will gradually decrease. Based on this, it can be seen that only when the PH at the detection point shows a decreasing or decreasing feature, the overacid phenomenon may occur, and it is worth monitoring. Therefore, this embodiment needs to extract the data collected at the current monitoring time and the sequence showing a decrease, that is, the target sequence obtained above. The target sequence will be analyzed later to further analyze whether the decreasing feature presented by the target sequence is caused by acidic substances; in addition, A is the total number of data in the PH monitoring time series sequence corresponding to the first detection point, and B is less than A and is a positive integer.

[0029] Therefore, the present embodiment can obtain the target sequence corresponding to each first detection point through the above process. After obtaining the target sequence corresponding to each first detection point, the data change degree representation value of each first detection point is obtained according to the difference between adjacent pH values ​​in the target sequence. The change degree representation value can reflect the possibility that the target sequence is caused by acidic substances. Therefore, in the present embodiment, the specific process of obtaining the data change degree representation value of the first detection point is as follows:

[0030] For any first detection point: first, obtain the change feature value sequence of the target sequence corresponding to the first detection point, and the bth change feature value in the change feature value sequence is the result of subtracting the b+1th change feature value in the change feature value sequence from the bth change feature value in the target sequence corresponding to the first detection point; then obtain the reverse sequence of the change feature value sequence, and record it as the reverse sequence to be analyzed corresponding to the first detection point. For example, if the change feature value sequence is {u1, u2, u3}, then the reverse sequence to be analyzed is {u3, u2, u1}; then obtain the feature difference value sequence of the reverse sequence to be analyzed corresponding to the first detection point and the weight coefficients of each feature difference value in the feature difference sequence, and the dth feature difference value in the feature difference sequence is the dth change feature value in the reverse sequence to be analyzed minus the weight coefficients of the reverse sequence to be analyzed. The result of the d+1th changed characteristic value in the sequence, and the weight coefficient of the dth feature difference value in the feature difference value sequence is the normalized value of the position characterization value of the dth feature difference value, and the position characterization value of the dth feature difference value is the reciprocal of d; in addition, the normalized value of the position characterization value of the dth feature difference value refers to the ratio of the position characterization value of the dth feature difference value to the comprehensive position characterization value, and the comprehensive position characterization value is the cumulative sum of the position characterization values ​​of all feature difference values ​​in the feature difference value sequence; then, the weighted feature difference value sequence of the feature difference value sequence is obtained, and the normalized value of the cumulative sum of all weighted feature differences in the weighted feature difference value sequence is recorded as the data change degree characterization value of the first detection point, and the product of the dth feature difference value and the weight coefficient of the dth feature difference value is the dth weighted feature difference value in the weighted feature difference value sequence.

[0031] In addition, the specific expression for calculating the data change degree representation value of the first detection point is:

[0032]

[0033] Wherein, F is the data change degree characterization value of the first detection point, D is the total number of characteristic difference values ​​in the characteristic difference value sequence of the reverse sequence to be analyzed corresponding to the first detection point, δ d is the normalized value of the position representation value of the dth feature difference value in the feature difference sequence, G d is the dth feature difference in the feature difference sequence, Norm() is the normalization function, and the above position representation value can reflect the reference value of the feature difference, and the closer to the current monitoring time, that is, G d The larger it is, the greater the reference value of the corresponding feature difference.

[0034] In addition, in the actual Bacillus fermentation process, due to the long fermentation time, the substances inside the pH sensor probe electrode will change with the increase of time or the influence of high-temperature steam sterilization, which will cause the monitored data to drift. This drift phenomenon is more likely to occur during the long-term fermentation of Bacillus. However, the drift phenomenon caused by the sensor will also cause a continuously declining data segment in the monitored data sequence, that is, the target sequence obtained above may also be caused by the drift phenomenon, but the continuously declining data segment caused by the drift phenomenon is different from the continuously declining data segment caused by the acidic substance. There are differences in the data distribution in the data segment, that is, the drift phenomenon is affected by the long-term environment. Therefore, compared with the data segment that continues to decline due to acidic substances, the data in the data segment that continues to decline due to drift phenomenon changes slowly and is close to linear change. The number of Bacillus strains will gradually increase during the fermentation process, and the acidic substances produced will also gradually increase. Therefore, compared with the data segment that continues to decline due to drift phenomenon, the data in the data segment that continues to decline due to acidic substances changes faster, and the change amplitude will become more and more drastic. Based on the data change degree representation value calculated by the target sequence, it can be seen that when δ d ×G d The larger the value of F is, the larger the value of F is, and the larger the value of F is, the more drastic the data change in the target sequence corresponding to the first detection point is, and the greater the probability that the data change in the target sequence corresponding to the first detection point is caused by the acidic substances produced by fermentation, that is, the greater the probability that the decreasing feature presented by the target sequence corresponding to the first detection point is caused by the acidic substances produced by fermentation.

[0035] Therefore, this embodiment can obtain the data change degree characterization value of each first detection point through the above process. After obtaining the data change degree characterization value of the first detection point, in order to further determine whether the data change in the target sequence corresponding to the first detection point is caused by the acidic substance produced by fermentation, this embodiment will obtain the relevant difference characterization value of the first detection point, and the relevant difference characterization value is also an important parameter reflecting whether the data change in the target sequence corresponding to the first detection point is caused by the acidic substance produced by fermentation. However, before obtaining the relevant difference characterization value, it is necessary to first obtain the sequence set to be analyzed for each first detection point according to the length of the target sequence corresponding to each first detection point, and the sequence set to be analyzed is the basis for subsequent analysis to obtain the relevant difference characterization value. Then, in this embodiment, the specific process of the sequence set to be analyzed for each first detection point is as follows:

[0036] For any first detection point: first, all detection points in the fermentation tank except the first detection point are obtained, and the set consisting of all the detection points in the fermentation tank except the first detection point is recorded as the first set, and then the total number of data in the target sequence corresponding to the first detection point is obtained, and recorded as the characteristic quantity representation value, and then the sequence to be analyzed corresponding to each detection point in the first set is obtained according to the characteristic quantity representation value, and the set constructed by the sequences to be analyzed corresponding to all detection points in the first set is recorded as the sequence set to be analyzed for the first detection point; and for the g-th detection point in the first set, the sequence constructed by the last characteristic quantity representation value PH value in the PH monitoring time series sequence corresponding to the g-th detection point is recorded as the sequence to be analyzed corresponding to the g-th detection point, that is, the length of the sequence to be analyzed corresponding to the g-th detection point is consistent with the length of the target sequence corresponding to the first detection point.

[0037] Therefore, this embodiment can obtain the set of sequences to be analyzed for each first detection point through the above process, and the specific reason for obtaining the set of sequences to be analyzed for the first detection point is: in the fermentation tank, the pH value directly monitored by the pH sensor is mainly affected by the strains around the sensor probe, that is, the degree of pH change caused by the acidic substances produced by the Bacillus will vary with the strain density, and because the strain growth process during the fermentation process is an entropy increase process, the strain density is randomly distributed, that is, at different detection points, the correlation between the pH changes caused by acidic substances is small. Therefore, for a certain first detection point, if the data change in the target sequence of the detection point is caused by the acidic substances produced by the fermentation, then the target sequence of the detection point and each of the sequences to be analyzed in the set of sequences to be analyzed at the first detection point will be different. The correlation between the analysis sequences is small, but if the data change in the target sequence of the first detection point is not caused by the acidic substances produced by fermentation, but by other reasons, such as the data change in the target sequence of the first detection point is caused by high-temperature steam sterilization, then at this time, the correlation between the target sequence of the first detection point and each sequence to be analyzed in the set of sequences to be analyzed of the first detection point is large. Based on the above analysis, after obtaining the set of sequences to be analyzed of the first detection point, this embodiment obtains the correlation difference characterization value of each first detection point according to the correlation between the target sequence corresponding to each first detection point and each sequence to be analyzed in the set of sequences to be analyzed corresponding to the first detection point, and the specific acquisition process of the correlation difference characterization value of each first detection point is:

[0038] For any first detection point: first obtain the normalized value of the Pearson correlation coefficient between the target sequence corresponding to the first detection point and each sequence to be analyzed in the set of sequences to be analyzed at the first detection point, and record them as correlation characterization values, then record the sequence constructed by all the obtained correlation characterization values ​​as the correlation characterization value sequence corresponding to the first detection point, and the h-th correlation characterization value in the correlation characterization value sequence corresponding to the first detection point is the normalized value of the Pearson correlation coefficient between the target sequence corresponding to the first detection point and the h-th sequence to be analyzed, and the h-th sequence to be analyzed is the h-th sequence to be analyzed in the set of sequences to be analyzed at the first detection point, that is, the correlation characterization value sequence corresponding to the first detection point The number of data in is consistent with the number of sequences to be analyzed in the set of sequences to be analyzed at the first detection point; then the mean of the correlation characterization value sequence corresponding to the first detection point is obtained and recorded as the correlation mean, and then the correlation mean is negatively correlated and mapped, and the mapping result is recorded as the correlation difference characterization value of the first detection point. The smaller the correlation mean, the larger the correlation difference characterization value of the first detection point, and the larger the correlation difference characterization value of the first detection point, the greater the probability that the data change in the target sequence corresponding to the first detection point is caused by the acidic substance produced by fermentation, that is, the greater the probability that the decreasing feature presented by the target sequence corresponding to the first detection point is caused by the acidic substance produced by fermentation.

[0039] In addition, the normalized value of the Pearson correlation coefficient between the target sequence corresponding to the first detection point and the hth sequence to be analyzed is R h is the Pearson correlation coefficient between the target sequence corresponding to the first detection point and the hth sequence to be analyzed, and the normalization here is to control the correlation representation value between 0 and 1; the correlation difference representation value of the first detection point is exp(-N0), exp() is an exponential function with a constant e as the base, and N0 is the correlation mean.

[0040] Therefore, this embodiment can obtain the relevant difference characterization value of each first detection point through the above process, and then obtain the target Bacillus acid production characterization value of each first detection point according to the data change degree characterization value and the relevant difference characterization value of each first detection point, that is, the specific acquisition process of the target Bacillus acid production characterization value of the first detection point is: for any first detection point, the average of the data change degree characterization value of the first detection point and the relevant difference characterization value of the first detection point is used as the target Bacillus acid production characterization value of the first detection point, and the larger the target Bacillus acid production characterization value of the first detection point, the greater the probability that the decreasing feature presented by the target sequence corresponding to the first detection point is caused by the acidic substance produced by fermentation.

[0041] Therefore, in this embodiment, the target Bacillus acid production characteristic value of each first detection point can be obtained through the above process.

[0042] Step S003, according to the target bacillus acid production characterization value, the first detection point is screened again, and the predicted pH value at the future monitoring time is obtained according to the screening result, and the fermentation process of the bacillus bio-organic fertilizer is monitored according to the predicted pH value at the future monitoring time.

[0043] After obtaining the target Bacillus acid production characterization value of each first detection point, this embodiment screens all the first detection points again according to the target Bacillus acid production characterization value of each first detection point, and obtains the predicted pH value at the future monitoring time according to the screening result, and the specific acquisition process is:

[0044] First, among all the first detection points, all the first detection points whose target Bacillus acid production characterization value is not less than the preset Bacillus acid production characterization value threshold are obtained, and all of them are recorded as second detection points; and in the present embodiment, it is determined that the decreasing characteristic presented by the target sequence corresponding to the first detection point whose target Bacillus acid production characterization value is not less than the preset Bacillus acid production characterization value threshold is caused by the acidic substances produced by fermentation; in addition, in specific applications, the implementer needs to set the preset Bacillus acid production characterization value threshold according to the actual situation. For example, in the present embodiment, the preset Bacillus acid production characterization value threshold can be set to 0.4.

[0045] After obtaining the second detection point, the least squares method is used to perform linear fitting on the target sequence corresponding to each second detection point, and the straight line obtained by fitting is recorded as the fitting straight line corresponding to the second detection point, that is, a fitting straight line can be obtained by linear fitting a second detection point; then the fitting straight lines of all the second detection points are averaged to obtain an average fitting straight line, which is recorded as the target fitting straight line, and the horizontal coordinate of the fitting straight line is time, and the vertical coordinate is pH value, and the slope of the average fitting straight line is the mean of the slopes of the fitting lines of all the second detection points.

[0046] And because the straight line obtained by linear fitting can be used to predict future data, after obtaining the target fitting straight line, this embodiment obtains the predicted PH value of the future monitoring moment according to the obtained target fitting straight line, and the predicted PH value of the future monitoring moment in this embodiment refers to the predicted PH value corresponding to the next monitoring moment of the current monitoring moment, and the predicted PH value corresponding to the next monitoring moment of the current monitoring moment refers to the ordinate value of the data point located on the target fitting straight line and having the same abscissa value as the time corresponding to the next monitoring moment of the current monitoring moment, that is, if the time corresponding to the next monitoring moment of the current monitoring moment is T0, then the ordinate value of the data point on the target fitting straight line with the same abscissa value as T0 is the predicted PH value corresponding to the next monitoring moment of the current monitoring moment, and usually the current monitoring moment and the next monitoring moment of the current monitoring moment are the same. The time interval between the moments is consistent with the time interval between any adjacent monitoring moments in the above-mentioned current monitoring time period; in addition, as another real-time method, the predicted PH value at the future monitoring moment can also be the predicted PH value at each moment in the future monitoring time period, and it is required that the time length of the future monitoring time period is consistent with the time length of the current monitoring time period, the future monitoring time period is adjacent to and continuous with the current monitoring time period, and the future monitoring time period is located behind the current monitoring time period in time, and the predicted PH value at any moment t1 in the future monitoring time period refers to the ordinate value of the data point which is located on the target fitting straight line and whose abscissa value is the same as the time corresponding to the moment t1, that is, if the time corresponding to any moment t1 in the future monitoring time period is T1, then the ordinate value of the data point on the target fitting straight line whose abscissa value is equal to T1 is the predicted PH value at the moment t1.

[0047] Therefore, the present embodiment obtains the predicted pH value at the future monitoring time through the above process, and after obtaining the predicted pH value at the future monitoring time, the present embodiment monitors the fermentation process of the Bacillus bio-organic fertilizer according to the predicted pH value at the future monitoring time, specifically:

[0048] First, if it is determined whether the predicted pH value is less than the predicted pH value of the preset pH threshold, if so, it indicates that the probability of overacidification at a future monitoring time closer to the current monitoring time or within a future monitoring time period closer to the current monitoring time is very high. Therefore, in order to minimize the impact of overacidification on fermentation, it is determined that an alkaline solution is injected into the fermentation tank at the current monitoring time to reduce the probability of overacidification. Otherwise, it indicates that the probability of overacidification at a future monitoring time period closer to the current monitoring time or within a future monitoring time closer to the current monitoring time is extremely low. , then there is no need to inject alkaline solution into the fermentation tank at this time; and this embodiment can monitor when the overacid phenomenon has not occurred but there is a trend of overacid phenomenon by predicting the pH value, and then take corresponding measures to avoid the occurrence of overacid phenomenon; and the amount of the injected alkaline solution needs to be set by the implementer according to the actual situation, such as the amount of the injected alkaline solution can be determined according to the difference between the pH value actually collected at the current monitoring time and the preset pH threshold, and the smaller the result of the pH value actually collected at the current monitoring time minus the preset pH threshold, the more alkaline solution needs to be injected.

[0049] In addition, in a specific application, the implementer needs to set a preset PH threshold according to the actual situation, and the preset PH threshold in this embodiment is the prescribed minimum PH threshold, for example, the preset PH threshold can be set to 5.5.

[0050] In summary, the present embodiment first obtains the pH monitoring time series corresponding to different detection points in the fermentation tank; then, according to the data in the pH monitoring time series, the detection points are screened to obtain the first detection point, and according to the pH monitoring time series corresponding to the first detection point, the target sequence corresponding to the first detection point is obtained, and according to the difference between adjacent pH values ​​in the target sequence, the data change degree characterization value of the first detection point is obtained, and according to the length of the target sequence, the sequence set to be analyzed of the first detection point is obtained, and according to the correlation between the target sequence corresponding to the first detection point and each sequence to be analyzed in the sequence set to be analyzed corresponding to the first detection point, the correlation difference characterization value of the first detection point is obtained; then, according to the data change degree characterization value and the correlation difference characterization value of the first detection point, the target Bacillus acid production characterization value of the first detection point is obtained; finally, according to the target Bacillus acid production characterization value, the first detection point is screened again, and the predicted pH value at the future monitoring time is obtained according to the screening result, and the fermentation process of the Bacillus bio-organic fertilizer is monitored according to the predicted pH value at the future monitoring time. Moreover, by predicting the pH value, this embodiment can timely detect when the overacidification phenomenon has not yet occurred but there is a trend or risk of the overacidification phenomenon, and take corresponding measures to avoid the occurrence of the overacidification phenomenon. That is, by predicting the pH value, this embodiment can improve the timeliness or effectiveness of monitoring the pH value of the fermentation liquid in the fermentation tank.

[0051] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for monitoring and optimizing fermentation conditions of Bacillus bio-organic fertilizer, characterized in that: The method comprises the following steps: Obtaining pH monitoring time series corresponding to different detection points in a fermentation tank, wherein the fermentation tank is a container for fermenting Bacillus bio-organic fertilizer; According to the data in the PH monitoring time series, the detection points are screened to obtain a first detection point, and according to the PH monitoring time series corresponding to the first detection point, a target sequence corresponding to the first detection point is obtained, and according to the difference between adjacent PH values ​​in the target sequence, a data change degree representation value of the first detection point is obtained; Obtaining a set of sequences to be analyzed at the first detection point according to the length of the target sequence, and obtaining a correlation difference representation value of the first detection point according to the correlation between the target sequence corresponding to the first detection point and each sequence to be analyzed in the set of sequences to be analyzed corresponding to the first detection point; Obtaining a target Bacillus acid production characterization value at the first detection point according to the data change degree characterization value and the related difference characterization value at the first detection point; According to the target bacillus acid production characterization value, the first detection point is screened again, and the predicted pH value at the future monitoring time is obtained according to the screening result, and the bacillus bio-organic fertilizer fermentation process is monitored according to the predicted pH value at the future monitoring time.

2. A kind of Bacillus bio-organic fertilizer fermentation condition monitoring and optimization method as claimed in claim 1, characterized in that, The method of screening the detection points according to the data in the pH monitoring time series to obtain the first detection point includes: According to the data in the pH monitoring time series corresponding to each detection point, the suspected fermentation acid production characterization value corresponding to each detection point is obtained; For any detection point, if the suspected fermentation acid production characterization value corresponding to the detection point is judged to be a positive number, then continue to judge whether the normalized value of the suspected fermentation acid production characterization value corresponding to the detection point is not less than the preset suspected judgment threshold. If so, the detection point is recorded as the first detection point.

3. A kind of bacillus bio-organic fertilizer fermentation condition monitoring and optimization method as claimed in claim 2, characterized in that, The method for obtaining the suspected fermentation acid production characterization value corresponding to each detection point includes: For any detection point, the second to last PH value and the last PH value in the PH monitoring time series sequence corresponding to the detection point are recorded as the first data value and the second data value respectively, the result of subtracting the second data value from the first data value is recorded as the first difference, a new sequence consisting of a preset number of consecutive data in front of the last PH value in the PH monitoring time series sequence corresponding to the detection point is recorded as a characteristic subsequence, the absolute value of the difference between the mean of the characteristic subsequence and the second data value is recorded as the second difference, the reciprocal of the result obtained by adding the second difference and the preset first constant is recorded as the first ratio; the product of the first difference and the first ratio is recorded as the suspected fermentation acid production characterization value corresponding to the detection point.

4. A kind of bacillus bio-organic fertilizer fermentation condition monitoring and optimization method as claimed in claim 1, characterized in that, The method for acquiring the target sequence corresponding to the first detection point includes: For any first detection point, if the sequence formed by the ABth PH value in the PH monitoring timing sequence corresponding to the first detection point to the Ath PH value in the PH monitoring timing sequence corresponding to the first detection point is a decreasing sequence, and the sequence formed by the A-(B-1)th PH value in the PH monitoring timing sequence corresponding to the first detection point to the Ath PH value is not a decreasing sequence, then the sequence formed by the ABth PH value to the Ath PH value is recorded as the target sequence corresponding to the first detection point, A is the total number of data in the PH monitoring timing sequence corresponding to the first detection point, and B is less than A.

5. A method for monitoring and optimizing fermentation conditions of bacillus bio-organic fertilizer as claimed in claim 1, characterized in that, The method for obtaining the data change degree representation value of the first detection point includes: For any first detection point: obtain the change feature value sequence of the target sequence corresponding to the first detection point, the bth change feature value in the change feature value sequence is the result of subtracting the b+1th change feature value in the change feature value sequence from the bth change feature value in the target sequence corresponding to the first detection point, record the reverse sequence of the change feature value sequence as the reverse sequence to be analyzed, obtain the feature difference value sequence of the reverse sequence to be analyzed and the weight coefficients of each feature difference value in the feature difference sequence, the dth feature difference value in the feature difference sequence is the dth change feature value in the reverse sequence to be analyzed The weight coefficient of the dth feature difference value in the feature difference value sequence is the normalized value of the position characterization value of the dth feature difference value, and the position characterization value of the dth feature difference value is the reciprocal of d; a weighted feature difference value sequence of the feature difference value sequence is obtained, and the normalized value of the cumulative sum of all weighted feature difference values ​​in the weighted feature difference value sequence is recorded as the data change degree characterization value of the first detection point, and the product of the dth feature difference value and the weight coefficient of the dth feature difference value is the dth weighted feature difference value in the weighted feature difference value sequence.

6. A method for monitoring and optimizing fermentation conditions of bacillus bio-organic fertilizer as claimed in claim 1, characterized in that, The method for acquiring the set of sequences to be analyzed at the first detection point includes: For any first detection point: the set formed by all detection points in the fermentation tank except the first detection point is recorded as the first set, and the total number of data in the target sequence corresponding to the first detection point is recorded as the feature quantity representation value; for the g-th detection point in the first set, the sequence constructed by the last feature quantity representation value PH values ​​in the PH monitoring time series sequence corresponding to the g-th detection point is recorded as the sequence to be analyzed corresponding to the g-th detection point; the set constructed by the sequences to be analyzed corresponding to all detection points in the first set is recorded as the set of sequences to be analyzed for the first detection point.

7. A method for monitoring and optimizing fermentation conditions of bacillus bio-organic fertilizer as claimed in claim 1, characterized in that, The method for obtaining the relevant difference characterization value of the first detection point includes: For any first detection point, a correlation characterization value sequence corresponding to the first detection point is obtained, and the mean of the correlation characterization value sequence is recorded as the correlation mean, and the negative correlation mapping value of the correlation mean is recorded as the correlation difference characterization value of the first detection point, the hth correlation characterization value in the correlation characterization value sequence is the normalized value of the Pearson correlation coefficient between the target sequence corresponding to the first detection point and the hth sequence to be analyzed, and the hth sequence to be analyzed is the hth sequence to be analyzed in the set of sequences to be analyzed of the first detection point.

8. A method for monitoring and optimizing fermentation conditions of bacillus bio-organic fertilizer as claimed in claim 1, characterized in that, The method for obtaining the acid production characterization value of the target Bacillus at the first detection point comprises: For any first detection point, the average of the data change degree characterization value of the first detection point and the related difference characterization value of the first detection point is used as the target Bacillus acid production characterization value of the first detection point.

9. A method for monitoring and optimizing fermentation conditions of bacillus bio-organic fertilizer as claimed in claim 1, characterized in that, The method for obtaining the predicted pH value at the future monitoring time includes: Among all the first detection points, all the first detection points whose target Bacillus acid production characterization value is not less than the preset Bacillus acid production characterization value threshold are recorded as second detection points; linear fitting is performed on the target sequences corresponding to the second detection points, and the straight lines obtained by fitting are recorded as the fitting straight lines corresponding to the second detection points, and the average fitting straight line of all the fitting straight lines is recorded as the target fitting straight line, the horizontal coordinate of the fitting straight line is time, and the vertical coordinate is pH value; according to the target fitting straight line, the predicted pH value at the future monitoring time is obtained.

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