CN116661517B

CN116661517B - Compound microbial fertilizer fermentation temperature intelligent regulation and control system based on thing networking

Info

Publication number: CN116661517B
Application number: CN202310951751.0A
Authority: CN
Inventors: 于文慧; 徐东升; 张新; 徐长青; 张磊; 张海涛
Original assignee: SHANDONG SANFANG CHEMICAL INDUSTRY GROUP CO LTD
Current assignee: SHANDONG SANFANG CHEMICAL INDUSTRY GROUP CO LTD
Priority date: 2023-07-24
Filing date: 2023-07-24
Publication date: 2023-09-29
Anticipated expiration: 2043-07-24
Also published as: CN116661517A

Abstract

The application relates to the technical field of digital data processing, in particular to an intelligent control system for fermentation temperature of a compound microbial fertilizer based on the Internet of things, which comprises the following components: the confirming module is used for confirming a growth stage corresponding to the target detection period according to temperature influence data of the target detection period in the fermentation process; the calculation module is used for confirming a temperature prediction coefficient corresponding to the next detection period based on the growth stage and temperature influence data corresponding to the target detection period; the prediction module is used for carrying out temperature prediction on the next detection period through the temperature prediction model and confirming predicted temperature data corresponding to the next detection period; and the regulation and control module is used for regulating and controlling the fermentation temperature of the next detection period based on the predicted temperature data corresponding to the next detection period. According to the system, the fermentation temperature of the next detection period is obtained through the growth stage and temperature influence data corresponding to the current target detection period, so that the accuracy of temperature regulation and control is improved, and the working cost is reduced.

Description

Compound microbial fertilizer fermentation temperature intelligent regulation and control system based on thing networking

Technical Field

The application relates to the technical field of digital data processing, in particular to an intelligent control system for fermentation temperature of a compound microbial fertilizer based on the Internet of things.

Background

The compound microbial fertilizer is an organic fertilizer which is formed by organically combining inorganic nutrient elements, organic matters and microbial bacteria into a whole, reflecting the comprehensive effects of inorganic chemical fertilizer, organic fertilizer and microbial fertilizer, and is formed by fermenting and mixing a plurality of microbial strains. The compound microbial fertilizer contains various ecological microorganisms and does not contain harmful chemical components, is an environment-friendly, safe and efficient fertilizer, and can promote the balance of a soil ecosystem and improve the soil fertility, the disease resistance of plants and the yield. And can solve the phenomenon of soil hardening, repair and condition soil, improve the utilization rate of chemical fertilizer, reduce the pollution of rivers, reduce the occurrence of plant diseases and insect pests, strengthen the stress resistance of crops and improve the quality and yield of crops fruits.

In the fermentation process of the compound microbial fertilizer, the fermentation temperature is an especially important factor. Too low or too high fermentation temperature affects the immunity of the microorganism, resulting in variation of yeast cells, and too low fermentation temperature affects the metabolism and growth of the microorganism.

The fermentation temperature is regulated and controlled manually according to the empirical value in the fermentation process of the traditional compound microbial fertilizer, and the fermentation temperature is regulated and controlled manually according to the empirical value, so that the fermentation temperature is easy to be higher or lower, and the quality of the final compound microbial fertilizer is influenced, namely, the accuracy of temperature regulation is lower through manual regulation and control in the traditional method, so that the quality of the final compound microbial fertilizer is unstable, and the working cost is increased.

Disclosure of Invention

In view of the above, it is necessary to provide an intelligent control system for fermenting temperature of a compound microbial fertilizer based on the internet of things, which improves accuracy of temperature control and further reduces working cost of fermentation compared with traditional manual control of fermenting temperature.

The application provides an intelligent control system for fermenting temperature of a compound microbial fertilizer based on the Internet of things, which is applied to the field of intelligent temperature control and comprises the following components: the device comprises a confirming module, a detecting module and a detecting module, wherein the confirming module is used for confirming a growth stage corresponding to a target detection period according to temperature influence data of the target detection period in a fermentation process, the growth stage comprises an adjustment period, a growth period, a stabilization period and a decay period which are arranged in time sequence, the target detection period is one detection period after a standard detection period, and the standard detection period is a preset number of detection periods before the adjustment period, the growth period, the stabilization period or the decay period; the calculation module is used for confirming a temperature prediction coefficient corresponding to the next detection period based on the growth stage and the temperature influence data corresponding to the target detection period, wherein the temperature prediction coefficient is a smoothing coefficient of a temperature prediction model; the prediction module is used for predicting the temperature of the next detection period through the temperature prediction model and confirming predicted temperature data corresponding to the next detection period; and the regulation and control module is used for regulating and controlling the fermentation temperature of the next detection period based on the predicted temperature data corresponding to the next detection period.

In one embodiment, the temperature influence data includes first sub data, second sub data and third sub data, and the corresponding confirmation module is configured to confirm a growth phase corresponding to a target detection period according to temperature influence data of the target detection period in a fermentation process, where the growth phase includes an adjustment period, a growth period, a stabilization period and a decay period arranged in time sequence, the target detection period is a detection period after a standard detection period, and the standard detection period is a preset number of detection periods before the adjustment period, the growth period, the stabilization period or the decay period, and specifically includes: the first sub-confirmation module is used for calculating a first influence factor, a second influence factor and a third influence factor corresponding to the first sub-data, the second sub-data and the third sub-data based on the first sub-data, the second sub-data and the third sub-data corresponding to the target detection period in the standard detection period; the second sub-confirmation module is used for taking the time corresponding to the minimum values of the first influence factor, the second influence factor and the third influence factor as a stage time node of the growth stage; and the third sub-confirmation module is used for confirming the growth phase corresponding to the target detection period based on the phase time node.

In one embodiment, the first sub-confirmation module is configured to calculate, based on first sub-data corresponding to the standard detection period and the target detection period, a first influence factor corresponding to the first sub-data, where the standard detection period is a preset number of detection periods before the adjustment period, the growth period, the stationary period, or the decay period, and the target detection period is a detection period after the standard detection period, and specifically includes: a first confirmation unit, configured to combine all data points before the target data point in the target detection period with all data points in the standard detection period, and confirm a first data point sequence; a second confirmation unit, configured to combine all data points after the target data point in the target detection period with all data points in the standard detection period, and confirm a second data point sequence; the third confirmation unit is used for determining a first slope and a first correlation parameter corresponding to the first data point sequence through linear fitting of the first data point sequence and preset correlation calculation; a fourth confirmation unit, configured to determine a second slope and a second correlation parameter corresponding to the second data point sequence through linear fitting of the second data point sequence and preset correlation calculation; a fifth confirming unit, configured to confirm a first initial impact factor corresponding to the target data point based on the first slope, the second slope, the first correlation parameter, and the second correlation parameter; and a sixth confirmation unit, configured to confirm the first initial impact factor greater than the preset impact factor threshold as the first impact factor.

In one embodiment, the fifth confirming unit is configured to confirm the first initial impact factor corresponding to the target data point based on the first slope, the second slope, the first correlation parameter and the second correlation parameter, and specifically includes:

wherein ,target data pointA corresponding first initial impact factor is used to determine,for a first slope corresponding to a first sequence of data points,for a second slope corresponding to a second sequence of data points,for a first correlation parameter corresponding to a first sequence of data points,for a second correlation parameter corresponding to a second sequence of data points,is a long correlation threshold.

In one embodiment, the calculating module is configured to determine, based on growth phase and temperature influence data corresponding to a target detection period, a temperature prediction coefficient corresponding to a next detection period, where the temperature prediction coefficient is a smoothing coefficient of a temperature prediction model, and specifically includes: the first sub-calculation module is used for confirming the association degree corresponding to the target detection period according to a corresponding relation table of the growth stage corresponding to the target detection period and a preset association degree coefficient; the second sub-calculation module is used for confirming a temperature influence data change rate sequence corresponding to the target detection period based on the temperature influence data corresponding to the target detection period; the third sub-calculation module is used for calculating the fluctuation degree and the fluctuation compactness corresponding to the target detection period according to the extreme points of the temperature-affected data change rate sequence; a fourth sub-calculation module, configured to calculate an interference level corresponding to the target detection period based on the association degree, the fluctuation level, and the fluctuation compactness corresponding to the target detection period, where the interference level includes a first interference level, a second interference level, and a third interference level corresponding to the first sub-data, the second sub-data, and the third sub-data; and the fifth sub-calculation module is used for inputting the first interference degree, the second interference degree and the third interference degree into a preset temperature prediction coefficient calculation model and calculating a temperature prediction coefficient corresponding to the next detection period.

In one embodiment, the temperature-affected data rate sequence includes a first sub data rate sequence, a second sub data rate sequence, and a third sub data rate sequence corresponding to the first sub data, the second sub data, and the third sub data, the fluctuation degree includes a first fluctuation degree, a second fluctuation degree, and a third fluctuation degree corresponding to the first sub data, the second sub data, and the third sub calculation module is configured to calculate, according to an extremum point of the temperature-affected data rate sequence, a fluctuation degree corresponding to the target detection period, and specifically includes:

according to the extreme point of the first sub-data change rate sequence, calculating a first fluctuation degree corresponding to the target detection period, specifically:

wherein ,for a first degree of fluctuation corresponding to the target detection period,for the data point mean value corresponding to all maximum points in the first sub-data rate of change sequence,for the first sub-data rate of change sequenceThe data point mean value corresponding to all the minimum value points in the column,for the number of all the extreme points,all data points are counted for the first sub-data rate of change sequence.

In one embodiment, the third sub-calculating module is configured to calculate, according to an extreme point of the temperature-affected data rate sequence, a fluctuation compactness corresponding to the target detection period, and specifically includes:

according to the extreme point of the first sub-data change rate sequence, calculating a first fluctuation compactness corresponding to the target detection period, specifically:

wherein ,for a first closeness of fluctuation corresponding to the target detection period,for the first sub-data trend item data sequenceThe number of extreme points is chosen,for the number of extreme points of the first sub-data trend item data sequence,is the firstThe predicted time difference value of each extreme point,is the firstThe prediction time of the individual extreme points,is the firstThe actual time of the individual extreme points,is a predicted time difference threshold.

In one embodiment, the association degree includes a first association degree, a second association degree, and a third association degree corresponding to the first sub-data, the second sub-data, and the third sub-data, and the fourth sub-calculation module is configured to calculate, based on the association degree, the fluctuation degree, and the fluctuation compactness corresponding to the target detection period, an interference degree corresponding to the target detection period, where the interference degree includes a first interference degree, a second interference degree, and a third interference degree corresponding to the first sub-data, the second sub-data, and the third sub-data, and specifically includes:

Based on the first association degree, the first fluctuation degree and the first fluctuation compactness corresponding to the target detection period, the first interference degree corresponding to the target detection period is calculated, specifically:

wherein ,for a first interference level corresponding to the target detection period,for a first degree of association corresponding to the target detection period,for a first degree of fluctuation corresponding to the target detection period,the first closeness of the fluctuation corresponding to the target detection period.

In one embodiment, the fifth sub-calculating module is configured to input the first interference level, the second interference level, and the third interference level into a preset temperature prediction coefficient calculating model, and calculate a temperature prediction coefficient corresponding to a next detection period, and specifically includes:

wherein ,the temperature prediction coefficient corresponding to the next detection period,for the first degree of interference to be the first degree,for the second degree of interference to be the second degree of interference,for a third degree of interference, the first and second interference levels,for the first calculation constant to be used,for the second calculation constant, a second calculation constant is used,is a normalization function.

According to the embodiment of the application, a growth stage corresponding to a target detection period is confirmed by a confirmation module according to temperature influence data of the target detection period in a fermentation process, wherein the growth stage comprises an adjustment period, a growth period, a stationary period and a decay period which are arranged in time sequence; then, according to the growth stage and temperature influence data corresponding to the target detection period, a temperature prediction coefficient corresponding to the next detection period is confirmed by a calculation module, wherein the temperature prediction coefficient is a smoothing coefficient of a temperature prediction model; then, temperature prediction is carried out on the next detection period through the temperature prediction model by using a prediction module, and predicted temperature data corresponding to the next detection period is confirmed; and finally, regulating and controlling the fermentation temperature of the next detection period according to the predicted temperature data corresponding to the next detection period through a regulating and controlling module. According to the system, the smooth coefficient of the temperature prediction model corresponding to the next growth stage is obtained through the growth stage and temperature influence data corresponding to the current target detection period, so that the fermentation temperature of the next detection period is obtained, the accuracy of temperature regulation is improved, and further the working cost of fermentation is reduced.

Drawings

Fig. 1 is a first block diagram of an intelligent control system for fermentation temperature of a compound microbial fertilizer based on the internet of things in an embodiment of the application.

Fig. 2 is a second block schematic diagram of the intelligent control system for fermenting temperature of the compound microbial fertilizer based on the internet of things in the embodiment of the application.

Fig. 3 is a third frame schematic diagram of an intelligent regulation system for fermentation temperature of a compound microbial fertilizer based on the internet of things in an embodiment of the application.

Fig. 4 is a fourth block diagram of an intelligent control system for fermentation temperature of a compound microbial fertilizer based on the internet of things in an embodiment of the application.

Fig. 5 is a characteristic schematic diagram of an intelligent control system for fermentation temperature of a compound microbial fertilizer based on the internet of things in an embodiment of the application.

Detailed Description

In describing embodiments of the present application, words such as "exemplary," "or," "such as," and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," "such as," and the like are intended to present related concepts in a concrete fashion.

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 application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It is to be understood that, unless otherwise indicated, a "/" means or. For example, A/B may represent A or B. The "and/or" in the present application is merely one association relationship describing the association object, indicating that three relationships may exist. For example, a and/or B may represent: a exists alone, A and B exist simultaneously, and B exists alone. "at least one" means one or more. "plurality" means two or more than two. For example, at least one of a, b or c may represent: seven cases of a, b, c, a and b, a and c, b and c, a, b and c.

It should be further noted that the terms "first" and "second" in the description and claims of the present application and the accompanying drawings are used for respectively similar objects, and are not used for describing a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flowchart, including one or more steps for implementing the method, may be performed in an order that the steps may be interchanged with one another, and some steps may be deleted without departing from the scope of the claims.

The embodiment of the application firstly provides an intelligent regulation and control system for fermentation temperature of a compound microbial fertilizer based on the Internet of things, which is applied to the field of intelligent regulation and control of temperature, and referring to the attached figure 1, the system comprises the following steps:

the confirming module 1 is configured to confirm a growth phase corresponding to a target detection period according to temperature influence data of the target detection period in a fermentation process, where the growth phase includes an adjustment period, a growth period, a stabilization period, and a decay period that are arranged in time sequence, the target detection period is a detection period after a standard detection period, and the standard detection period is a preset number of detection periods before the adjustment period, the growth period, the stabilization period, or the decay period.

The fermentation process of the compound microbial fertilizer can be divided into the following steps: 1. selecting a fermentation tank with proper capacity for cleaning and sterilizing; 2. mixing and primarily treating production raw materials according to a formula; 3. screening and culturing proper strains according to the requirements of the compound microbial fertilizer; 4. and (3) placing the treated raw materials and strains into a fermentation tank, adding auxiliary fermentation materials, and starting fermentation.

The application takes a microorganism compound bacterial fertilizer as an example to carry out the regulation and control explanation of the fermentation temperature of the follow-up compound bacterial fertilizer, wherein the compound microorganism used for the fermentation of the compound bacterial fertilizer comprises bacillus megaterium, streptomyces jingyang, pseudomonas fluorescens and bacillus thuringiensis, and the fermentation culture medium of the compound bacterial fertilizer comprises the following components: the concentration of glucose is 10g/L, 6 g/L，3 g/L, 1.5 g/L urea, 1 g/L yeast powder,0.41 g/L, the initial pH of the fermentation medium was 7.0.

Specifically, the temperature influence data refers to data parameters capable of influencing the fermentation temperature, such as the pH of the fermentation environment, the substrate concentration, the dissolved oxygen concentration, the pressure, and the like. The degree of influence of different temperature influence data on the fermentation temperature is different, and is also related to the stage where fermentation is located, for example, the fermentation speed in the initial stage of fermentation is lower, the influence of temperature influence data on the fermentation temperature is lower, and when the fermentation speed is higher in the middle stage of fermentation, the influence of temperature influence data on the fermentation temperature is higher. The temperature influence data are collected by installing a temperature sensor, a PH electrode, a matrix concentration sensor, a dissolved oxygen sensor and the like in the fermentation tank, and the specific collection mode is not further limited. It should be noted that the temperature influence data is periodic, the acquisition time interval is preset, the data amount adopted in one acquisition period is preset, the specific data setting can be defined according to actual requirements, and the scheme is not further limited.

The standard detection period is a preset number detection period before the adjustment period, the growth period, the stationary period or the decay period, for example, the preset number detection period is set to be the first three detection periods, the starting time of the adjustment period is a, the starting time of the growth period is b, the starting time of the stationary period is c, and the starting time of the decay period is d, and the standard detection period of the corresponding adjustment period, the growth period, the stationary period or the decay period is the first three detection periods from a, the first three detection periods from b, the first three detection periods from c and the first three detection periods from d. The target detection period is one detection period after the standard detection period, and aims to predict the fermentation temperature of the next detection period by combining the data of the previous standard detection period so as to improve the prediction accuracy. The standard detection period may be a fixed pre-set number of detection periods of the adjustment period, the growth period, the stationary period, or the decay period, or may be all detection periods before the target detection period of the adjustment period, the growth period, the stationary period, or the decay period, for example, taking the standard detection period of the adjustment period as an example, assuming that the 15 th detection period is followed by the growth period, the pre-set number of detection periods is the first 3 detection periods, and when the target detection period is the 12 th detection period, the standard detection period may be a fixed 1-3 detection periods, or may be 1-11 detection periods. It is to be resolved that when the growth phase corresponding to the target detection period is confirmed to be the decay phase, the growth phase of each target detection period is not required to be judged, and the subsequent steps can be directly performed to realize the fermentation temperature regulation because the last growth phase of the decay phase does not change.

It should be noted that, in the fermentation process of the compound microbial fertilizer, the growth and propagation process of the microorganism is the core part of the fermentation process, and the fermentation temperature is an important factor affecting the growth and metabolism of the microorganism, so that it is particularly important to regulate and control the fermentation temperature of the fermentation process in real time. The optimal temperatures of the microorganisms needed in different growth stages are different, the growth speed of the microorganisms is fastest in the optimal temperature range, beyond the temperature range, the microorganisms do not grow any more, and the temperature in the fermentation tank is adversely affected due to the growth and metabolism of the microorganisms in the fermentation tank, heat generated by the microorganisms in the metabolism process can accumulate in the fermentation tank, the spontaneous temperature rise in the fermentation tank is caused, and the fermentation tank is regulated to the optimal temperature in real time, so that the optimal step of improving the fermentation quality is realized.

Further, referring to fig. 5, the growth phase includes a chronologically-ordered conditioning phase, a growing phase, a stationary phase, and a decay phase. In the regulation period, the growth rate is close to 0, and the metabolic system of the microorganism needs to adapt to new environment just after being inoculated to the fermentation tank, and meanwhile, the number of cells in the period is not increased because of synthesizing enzymes, coenzymes, other metabolic intermediate metabolites and the like. The growth rate of the microbial strain in the growing period is fastest, the metabolism is vigorous and the enzyme system is active, and the preparation of the microbial strain in the adjusting period provides enough material basis for the microbial growth in the growing period, and the external environment is also in the optimal state. In the stationary phase, the growth rate is stable, and the total number reaches the highest point because the accumulation of metabolites reaches the highest peak. During the decay period, the bacterial death rate is greater than the new growth rate, and the bacterial death rate is in a negative growth state, so that the continuous growth is more and more unfavorable due to the external environment, the catabolism of cells is greater than the anabolism, and then a large number of bacteria die.

It should be noted that, the optimal temperatures of the adjustment period, the growth period, the stabilization period and the decay period are all different, and then the sectional regulation and control are required based on different periods of the growth period, so that the microorganisms are in the optimal temperature intervals in the adjustment period, the growth period, the stabilization period and the decay period, thereby improving the fermentation quality.

And the calculating module 2 is used for confirming a temperature prediction coefficient corresponding to the next detection period based on the growth stage corresponding to the target detection period and the temperature influence data, wherein the temperature prediction coefficient is a smoothing coefficient of the temperature prediction model.

It should be noted that, when the temperature regulation and control are performed on the fermentation process, a certain reaction time is required for regulating and controlling the temperature, and the current temperature is difficult to be realized according to the current temperature influence data, so that the temperature corresponding to the next detection period is predicted and the temperature regulation and control are realized according to the growth stage and the temperature influence data corresponding to the current target detection period, so that the accurate and real-time temperature regulation and control are realized, and the fermentation quality is improved.

Specifically, according to the growth phase corresponding to the obtained target detection period, which growth phase the target detection period is can be determined, if the target detection period is completely in the same growth phase, the temperature of the next detection period of the target detection period can be predicted, if the target detection period is in the junction of two different growth phases, the next detection period of the target detection period is different from the growth phase in which the target detection period is located, and the current target detection period cannot be predicted. The temperature prediction coefficient is a smoothing coefficient of a temperature prediction model, the temperature prediction model can be an exponential moving average algorithm (EMA), and the exponential moving average algorithm can accurately reflect the trend and periodicity of data and is suitable for data prediction of various time sequences. In other fields in the industry, the smoothing coefficient of the algorithm is generally selected according to experience, which can cause too large or too small smoothing degree, and the scheme calculates the smoothing coefficient according to the growth stage and the temperature influence data, so that the fermentation temperature prediction accuracy of the next detection period can be further improved.

Specifically, the exponential moving average algorithm (EMA) refers to a calculation of an average value that considers not only the current data point but also the moving average value at the previous time point. In this method, each data point has an exponential weight that decreases exponentially with time, expressed as:

a+(1-a)

wherein ,indicating a point in timeIs a moving average of the indices of (c),data representing the point in time t of the day,an exponentially moving average, a, representing the time point t-1 is a smoothing factor, and the weight of the new data point is controlled, typically to a value between 0.1 and 0.3. Besides calculating the smoothing coefficient, the specific index moving average algorithm further obtains the fermentation temperature, and the corresponding parameters are substituted, so that the method is not limited further and can be realized by referring to the prior art.

And the prediction module 3 is used for predicting the temperature of the next detection period through the temperature prediction model and confirming the predicted temperature data corresponding to the next detection period.

After the temperature prediction coefficient corresponding to the temperature prediction model, namely the smoothing coefficient is obtained, the corresponding temperature prediction coefficient is substituted into the temperature prediction model, and corresponding calculation parameters are input to calculate predicted temperature data corresponding to the next detection period. The predicted temperature data refers to the fermentation temperature corresponding to the predicted next detection period.

And the regulation and control module 4 is used for regulating and controlling the fermentation temperature of the next detection period based on the predicted temperature data corresponding to the next detection period.

The predicted temperature data corresponding to the next detection period may be a predicted fermentation temperature corresponding to the next detection period, where one predicted temperature data includes a plurality of fermentation temperatures, and an average value of fermentation temperatures in the predicted temperature data of the next detection period is calculated, and the average value is used as an optimum temperature of the next detection period, that is, the predicted fermentation temperature of the next detection period. After the predicted fermentation temperature of the next detection period is obtained, and when the temperature of the next detection period is regulated and controlled, the current temperature of the next detection period and the predicted fermentation temperature are subjected to difference calculation to obtain a temperature difference value, and the temperature difference value is converted into a corresponding electric signal through a temperature control system so as to control the current temperature to the predicted fermentation temperature. The temperature control system may be a PLC temperature control system, and the cold air valve is controlled to regulate the temperature in the fermenter.

In an embodiment of the present application, referring to fig. 2, the temperature influence data includes first sub-data, second sub-data, and third sub-data, and the corresponding confirmation module 1 is configured to confirm a growth phase corresponding to a target detection period according to the temperature influence data of the target detection period in a fermentation process, where the growth phase includes an adjustment period, a growth period, a stabilization period, and a decay period that are arranged in time sequence, and the target detection period is a detection period after a standard detection period, and the standard detection period is a preset number of detection periods before the adjustment period, the growth period, the stabilization period, or the decay period, and specifically includes:

a first sub-confirmation module 11, configured to calculate a first influence factor, a second influence factor, and a third influence factor corresponding to the first sub-data, the second sub-data, and the third sub-data based on the first sub-data, the second sub-data, and the third sub-data corresponding to the standard detection period and the target detection period, where the standard detection period is a preset number of detection periods before the adjustment period, the growth period, the stationary period, or the decay period, and the target detection period is one detection period after the standard detection period;

Wherein the first, second and third sub-data may be matrix concentration data, PH data, dissolved oxygen content data. Each sub-data corresponds to an influence factor, namely, the first sub-data, the second sub-data and the third sub-data correspond to the first influence factor, the second influence factor and the third influence factor respectively. The first influence factor, the second influence factor, and the third influence factor are parameters for characterizing characteristics between different growth phases, for example, characteristics that a data change rate of an adjustment phase-a growth phase is gradually increased from small, a data change rate of a stabilization phase-a decay phase is gradually decreased from large, and the like. The data points between different periods are processed, so that the characteristic can be characterized by parameters, and the first influence factor, the second influence factor and the third influence factor are parameters of the type.

A second sub-confirmation module 12, configured to use a time corresponding to the minimum values of the first influence factor, the second influence factor, and the third influence factor as a stage time node of the growth stage;

the phase time nodes of the growth phases are turning moments among different growth phases, and the turning moments refer to four moments of an adjustment phase-a growth phase, a growth phase-a stabilization phase and a stabilization phase-a decay phase. And taking the time corresponding to the minimum values of the first influence factor, the second influence factor and the third influence factor as a stage time node of the growth stage, and realizing temperature regulation and control of the next stage in a maximum range.

And the third sub-confirmation module 13 is configured to confirm the growth phase corresponding to the target detection period based on the phase time node.

It should be noted that, according to the first sub-data, the second sub-data, and the third sub-data, the first influence factor, the second influence factor, and the third influence factor are calculated according to the same logic, and the embodiment takes calculating the first influence factor as an example to perform analysis and explanation, and referring to fig. 3, the first sub-confirmation module 11 is configured to calculate, based on the first sub-data corresponding to the target detection period in the standard detection period, the first influence factor corresponding to the first sub-data, and specifically includes:

a first confirmation unit 111, configured to combine all data points before the target data point in the target detection period with all data points in the standard detection period, and confirm a first data point sequence;

a second confirmation unit 112, configured to combine all data points after the target data point in the target detection period with all data points in the standard detection period, and confirm a second data point sequence;

a third confirmation unit 113, configured to determine a first slope and a first correlation parameter corresponding to the first data point sequence through linear fitting of the first data point sequence and preset correlation calculation;

A fourth confirmation unit 114, configured to determine a second slope and a second correlation parameter corresponding to the second data point sequence through linear fitting of the second data point sequence and preset correlation calculation;

a fifth confirming unit 115, configured to confirm a first initial impact factor corresponding to the target data point based on the first slope, the second slope, the first correlation parameter and the second correlation parameter;

a sixth confirming unit 116, configured to confirm the first initial impact factor greater than the preset impact factor threshold as the first impact factor.

Specifically, the linear fitting process is performed on the first data point sequence and the second data point sequence, so that a slope after linear fitting can be obtained, the slope can represent the characteristic of data change between different growth stages, and similarly, the preset correlation calculation is performed on the first data point sequence and the second data point sequence, wherein the preset correlation calculation can be DFA trending analysis, and the corresponding first correlation parameter and second correlation parameter can be Hurst indexes, and are also a characteristic parameter of the characteristic of data change between different growth stages.

It should be noted that there are three cases of Hurst index correlation:

(1) When 0.5< h <1, the time series has long-range correlation, and the time series shows a state that the trend is continuously enhanced, namely, the time series is in an increasing (decreasing) trend in a certain time period, the next time series is in an increasing (decreasing) trend, and the closer h is to 1, the stronger the correlation is.

(2) When h=0.5, the time series is uncorrelated, which is an independent random process, i.e. the current state does not affect the future state.

(3) When 0< h <0.5, it indicates that the radial flow time series has negative correlation, and presents a state of inverse persistence, that is, the time series is in a trend of increasing (decreasing) in a certain time period, and in a next time period, is in a trend of decreasing (increasing).

Correspondingly, based on the stage time node, confirming the growth stage corresponding to the target detection period may specifically be:

it should be noted that, according to the characteristics of different growth phases, the characteristics can be represented by the Hurst index, that is, the value of the Hurst index is close to 0.5 in the case of the data sequences in the adjustment phase and the stabilization phase, and is close to 1 and 0 in the case of the data sequences in the growth phase and the decay phase, respectively.

Further, the fifth confirming unit 115 is configured to confirm the first initial impact factor corresponding to the target data point based on the first slope, the second slope, the first correlation parameter and the second correlation parameter, and specifically includes:

wherein ,for the target data pointA corresponding first initial impact factor is used to determine,for a first slope corresponding to a first sequence of data points,for a second slope corresponding to a second sequence of data points,for a first correlation parameter corresponding to a first sequence of data points,for a second correlation parameter corresponding to a second sequence of data points,is a long correlation threshold. It should be noted that the number of the substrates,the value can be set to 0.5,the larger the value of (a) represents the data pointThe more suspected the corresponding moment is the growth phase transition moment.

In the invention, the intelligent regulation and control object is the temperature of the compound bacterial manure in the fermentation process, but because the compound microorganism has multiple growth stages in the fermentation process of the compound bacterial manure and the time sequence characteristics of different time lengths of each growth stage, the possibility that each data point is taken as the turning time node of the adjacent growth stage is evaluated by utilizing the characteristic that the growth rate difference of two adjacent growth stages of the compound microorganism in the fermentation process of the compound bacterial manure is larger at the turning time node and utilizing the influence degree of the metabolic state of the microorganism in the compound bacterial manure at the corresponding moment of different data points on the change of the substrate concentration of the turning time node. Therefore, the corresponding relation between each target detection period and the specific growth stage of the compound bacterial manure can be determined through the first initial influence factor, so that the optimal temperature of each target detection period is determined through the specific growth stage, and the subsequent realization of stepwise temperature regulation and control by using the PLC is facilitated.

The four times of the adjustment phase-growth phase, the growth phase-stabilization phase and the stabilization phase-decay phase are calculated in the same manner.

In one embodiment of the present application, referring to fig. 4, the step calculating module 2 is configured to determine a temperature prediction coefficient corresponding to a next detection period based on the growth stage and the temperature influence data corresponding to the target detection period, where the temperature prediction coefficient is a smoothing coefficient of a temperature prediction model, and specifically includes:

a first sub-calculation module 21, configured to confirm the association degree corresponding to the target detection period according to a relationship table corresponding to the growth stage corresponding to the target detection period and a preset association degree coefficient;

the correlation degree corresponding to the target detection period may include a first correlation degree, a second correlation degree, and a third correlation degree corresponding to the first sub-data, the second sub-data, and the third sub-data, that is, correlation degrees of PH value data, substrate concentration data, and dissolved oxygen content data to temperature data. The association degree can be obtained by analyzing GRA through gray association through historical data sequences of each monitoring data of the composite microbial fertilizer, and the weight corresponding to the growth stage corresponding to the current target detection period is searched through an association degree coefficient corresponding relation table so as to determine the final association degree. For example, in the growth period through the growth stage corresponding to the target detection period, the weight corresponding to the growth of the preset association coefficient corresponding relation table is a, the initial association degree obtained by gray association analysis GRA is X, and the association degree corresponding to the target detection period is a×x.

It should be noted that, the GRA algorithm essentially provides a method for measuring the distance between two vectors, and for a time factor, the vector can be regarded as a time curve, and the GRA algorithm measures whether the shapes and the trends of the two curves are similar. To avoid other disturbances, the effect of the salient morphology features, GRA is normalized first, all vectors are corrected to the same scale and position, and then the distance of each point is calculated. Finally, the final output result falls between 0 and 1 through correction of min min and max max, thereby conforming to the general definition. rho adjusts for differences between different correlation coefficients, in other words, the distribution of the outputs, so that it may become more sparse or tight. Mathematically speaking, the algorithm measures the sum of the inverse of the l1-norm distance for each dimension of the normalized subvectors and parent vectors and maps it to the 0-1 interval as a strategy for the measure of the relevance of the subvectors.

A second sub-calculation module 22, configured to confirm a temperature influence data change rate sequence corresponding to the target detection period based on the temperature influence data corresponding to the target detection period;

The matrix concentration change rate sequence corresponding to the target detection period is obtained by subtracting the matrix concentration difference value at the previous moment from the matrix concentration at the current moment of the data sequence corresponding to the matrix concentration data to obtain the matrix concentration change rate sequence corresponding to the target detection period, and subtracting the matrix concentration change value at the previous moment from the matrix concentration change value at the current moment to obtain the matrix concentration change rate sequence corresponding to the target detection period. It should be noted that, the STL algorithm processing needs to be performed on the matrix concentration change rate sequence, so as to obtain a trend item data sequence of the sequence, and calculate the subsequent extreme points.

And the third sub-calculation module 23 is configured to calculate the fluctuation degree and the fluctuation compactness corresponding to the target detection period according to the extreme points of the temperature-affected data change rate sequence.

Specifically, the temperature-affected data change rate sequence includes a first sub data change rate sequence, a second sub data change rate sequence, and a third sub data change rate sequence corresponding to the first sub data, the second sub data, and the third sub data, the fluctuation degree includes a first fluctuation degree, a second fluctuation degree, and a third fluctuation degree corresponding to the first sub data, the second sub data, and the third sub calculation module 23 is configured to calculate, according to an extremum point of the temperature-affected data change rate sequence, a fluctuation degree corresponding to the target detection period, and specifically includes:

wherein ,for a first degree of fluctuation corresponding to the target detection period,for the data point mean value corresponding to all maximum points in the first sub-data rate of change sequence,for the data point mean value corresponding to all minimum value points in the first sub-data change rate sequence,for all extreme pointsThe number of the product is the number,all data points are counted for the first sub-data rate of change sequence.

In the invention, the first sub data, the second sub data and the third sub data are respectively the substrate concentration, the PH value and the dissolved oxygen content of the compound bacterial manure. Taking the first sub data, namely the substrate concentration of the compound bacterial fertilizer as an example, the change of the substrate concentration of the compound bacterial fertilizer reflects the fluctuation degree of nutrition or energy required by metabolism of compound microorganisms in the fermentation process of the compound bacterial fertilizer, the more the energy required by growth of the compound microorganisms, the more heat generated by bacillus megaterium, streptomyces jingyang, pseudomonas fluorescens and bacillus thuringiensis in the compound bacterial fertilizer in the metabolism process, the higher the fermentation temperature, the more the growth rate of the microorganisms is influenced, the temperature in a fermentation tank is required to be cooled, otherwise, the less the energy required by the growth of the compound bacterial fertilizer, the less the heat generated by the microorganisms, and the lower the fermentation temperature. Therefore, the invention considers that the fluctuation degree of nutrition or energy required by metabolism in the fermentation process of the compound microorganism is reflected by the fluctuation degree of data in the data sequence corresponding to the matrix concentration of the compound bacterial fertilizer, the more irregular the distribution of extreme points in the data sequence is, the larger the difference between the extreme points is, The larger the value of (c), the greater the degree of fluctuation of the data sequence,the larger the value of (c) the more unstable the metabolic response in the fermentation process of the complex microorganism is reflected in the fermenter. I.e.The larger the value of the target detection period is, the more the growth and metabolism states of the compound microorganism are likely to change in the fermentation process, the larger the influence degree of the target detection period on the subsequent fermentation temperature is, the smaller the contribution to the temperature prediction result in the subsequent stage is, and the corresponding target detection period is when the temperature prediction result is obtained by using a prediction algorithm in the subsequent temperature regulation processThe smaller the smoothing factor of the data should be during the target detection period.

It should be noted that, if only the maximum point or the minimum point exists at the extreme point corresponding to the first sub-data change rate sequence, the average value of all the data point values in the first sub-data change rate sequence is taken asOr (b)Another value is taken as 0.The larger the difference between them, the larger the data point fluctuation amplitude of the first sub-data change rate sequence is proved. Number of extreme pointsThe larger the data point of the first sub-data rate of change sequence is, the more frequent the data point fluctuations are proved. The fluctuation amplitude and the frequency are both equal to the first fluctuation degreeAnd shows positive correlation. The first fluctuation degree corresponding to the target detection period can be obtained based on the fluctuation amplitude, the frequency and the fluctuation degree of the data points of the first sub-data change rate sequence . In addition, a first degree of fluctuationDegree of second fluctuationThird degree of fluctuationThe calculation method of the second fluctuation degree and the third fluctuation degree are the same, and the scheme is not repeated.

Specifically, referring to the above-mentioned first sub-data change rate sequence for example for analysis, the calculating the fluctuation compactness corresponding to the target detection period according to the extreme point of the temperature-affected data change rate sequence, the third sub-calculating module 23 is configured to calculate the fluctuation compactness corresponding to the target detection period according to the extreme point of the temperature-affected data change rate sequence, and specifically includes:

It should be noted that the target detection period refers to a current data acquisition period, for the fermentation process of the compound microbial fertilizer, the growth rates of the microbes in different growth stages are different, the metabolic rates are different, and the influence on the temperature is different.

The first fluctuation compactness reflects how tightly the fluctuation data points are distributed between the moment when the fluctuation data points are located and the predicted temperature moment. Predicted temperature time and first sub-data trend item data sequenceThe larger the time interval between the extreme points,the larger the value of (c) is,the larger the value of the (j) th extreme point is, the more likely the jth extreme point and the predicted temperature are in different growth and metabolism states; i.e.The smaller the value of the (a) is, the fewer the number of extreme points in the data sequence of the first sub-data trend item is and the larger the time interval between the extreme points and the moment of the predicted temperature is, the first sub-data sequence is for the subsequent fermentation temperatureThe less the effect of the degree. The longer the time interval between the time of growth and metabolism instability of bacillus megaterium, streptomyces jingyang, pseudomonas fluorescens and bacillus thuringiensis in the compound bacterial fertilizer and the time of predicting the temperature is reflected in the fermentation process of the compound bacterial fertilizer, the smaller the influence on the subsequent fermentation temperature is, and the more approximate the growth and metabolism state of the compound microorganisms are between the predicted time and the time close to the predicted time. Therefore, in the temperature regulation process of the fermentation tank, the smoothing factor corresponding to the target detection period should be larger, so that the temperature predicted value obtained by using the data with less interference on the temperature predicted value is more consistent with the actual growth and metabolism conditions of various microorganisms in the compound bacterial manure in the fermentation tank, and in the temperature regulation process, the substrate concentration of the compound bacterial manure corresponding to the ideal temperature and the temperature predicted value is more similar, and the accuracy of the temperature regulation is higher.

The prediction time difference threshold valueAn empirical value of 5 may be taken. The fluctuation point in the first sub-data change rate sequence is the extreme pointThe closer the time distance from the predicted time is, the more extreme points are representedThe tighter the distribution between the predicted data points, the first fluctuation compactnessThe greater the value of (2). In addition, the first wave tightnessSecond wave tightnessThird wave tightnessThe same calculation method as in (2), in addition, the second fluctuation compactnessThird wave tightnessThe scheme of the calculation process is not repeated.

And a fourth sub-calculation module 24, configured to calculate an interference level corresponding to the target detection period based on the correlation, the fluctuation level, and the fluctuation compactness corresponding to the target detection period, where the interference level includes a first interference level, a second interference level, and a third interference level corresponding to the first sub-data, the second sub-data, and the third sub-data.

The correlation degree includes a first correlation degree, a second correlation degree, and a third correlation degree corresponding to the first sub-data, the second sub-data, and the third sub-data, and the fourth sub-calculation module 24 is configured to calculate an interference degree corresponding to the target detection period based on the correlation degree, the fluctuation degree, and the fluctuation compactness corresponding to the target detection period, where the interference degree includes a first interference degree, a second interference degree, and a third interference degree corresponding to the first sub-data, the second sub-data, and the third sub-data, and specifically includes:

wherein ,for a first interference level corresponding to the target detection period,for a first degree of association corresponding to the target detection period,for a first degree of fluctuation corresponding to the target detection period,the first closeness of the fluctuation corresponding to the target detection period. The first interference levelThe larger the first sub-data, the greater the degree of interference to the smoothing coefficient, the smaller the value of the corresponding smoothing coefficient should be. In addition, a first interference levelSecond degree of interferenceThird degree of interferenceThe same calculation method, and the second interference degreeThird degree of interferenceThe scheme of the calculation process is not repeated.

And a fifth sub-calculation module 25, configured to input the first interference level, the second interference level, and the third interference level into a preset temperature prediction coefficient calculation model, and calculate a temperature prediction coefficient corresponding to a next detection period.

Specifically, the inputting the first interference level, the second interference level and the third interference level into a preset temperature prediction coefficient calculation model, and calculating a temperature prediction coefficient corresponding to a next detection period specifically includes:

wherein ,for the temperature prediction coefficient corresponding to the next detection period,for the first degree of interference to be the first degree,for the second degree of interference to be the second degree of interference,for a third degree of interference, the first and second interference levels,for the first calculation constant to be used,for the second calculation constant, a second calculation constant is used,is a normalization function. The first calculation constantAnd a second calculation constantThe values can be 0.2 and 0.8.

Further, synthesizeFor the first degree of interference to be the first degree,for the second degree of interference to be the second degree of interference,further explaining that the greater the third interference degree is, the greater the influence degree on the temperature prediction coefficient corresponding to the next detection period is, the greater the state change of the microorganism in the fermentation tank is, the greater the temperature change is along with the greater change, and the lower the corresponding smoothing coefficient (temperature prediction coefficient) is needed to reflect the temperature data sequence more quicklyAnd the change trend is adopted, so that the situation of data lag is avoided. And the closer the time when the data fluctuates is to the predicted time, the smoothing coefficient needs to be reduced in order to reduce the influence of the current fluctuation data on the predicted resultTo reflect the actual trend of the data. Finally, the calculation formula is obtained to calculate the temperature prediction coefficient corresponding to the next detection period.

According to the embodiment of the application, the predicted value of the temperature in the fermentation tank in different growth periods in the fermentation process of the compound microbial fertilizer is obtained, and the temperature is regulated and controlled through the difference between the predicted value of the temperature in the fermentation tank and the optimal temperature. Firstly, determining turning moments of each fermentation stage according to fluctuation degrees of temperature related data at two sides of transition moments among different stages in a fertilizer fermentation process, and determining a growth stage corresponding to the target detection period according to the turning moments, wherein the growth stage comprises an adjustment period, a growth period, a stationary period and a decay period which are arranged in time sequence; secondly, obtaining the fluctuation degree corresponding to each type of influence factors according to the association degree of the different influence factors on the fermentation temperature in each growth stage, wherein the fluctuation degree can reflect the energy required by the metabolism of the compound microorganism in the fermentation process of each influence factor, and the heat generation amount can further reflect the influence degree of the influence factors on the fermentation temperature; and determining the fluctuation compactness of each influence factor on the temperature at the moment to be predicted according to the time interval between the growth stage at which the fermentation temperature to be predicted is located and the moment corresponding to the extreme point in the acquired data, wherein the fluctuation compactness is based on the principle that the influence degree of the fermentation temperature at different moments on the fermentation temperature at the subsequent moment is judged according to the length of the fermentation period of the compound microorganism and the time interval, and then confirming the temperature prediction coefficient corresponding to the next detection period according to the growth stage corresponding to the target detection period and the temperature influence data by the calculation module, wherein the construction principle of the temperature prediction coefficient is based on the smooth parameters in the temperature prediction algorithm which are self-adaptive according to the influence degree of each influence factor on the fermentation temperature at the subsequent moment. Finally, the fermentation temperature of the next detection period is regulated and controlled through the difference between the predicted value of the temperature in the fermentation tank and the optimal temperature.

According to the system, the smooth coefficient in the temperature prediction model is obtained in a self-adaptive mode through different influence degrees of each influence factor on the fermentation temperature in the growth stage corresponding to the current target detection period, so that the fermentation temperature of the next detection period is obtained, the accuracy of temperature regulation is improved, and the working cost of fermentation is further reduced.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The above-described embodiments of the application are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

Claims

1. The utility model provides a compound microbial fertilizer fermentation temperature intelligent regulation and control system based on thing networking, is applied to temperature intelligent regulation and control field, its characterized in that, the system includes:

the device comprises a confirming module, a detecting module and a detecting module, wherein the confirming module is used for confirming a growth stage corresponding to a target detection period according to temperature influence data of the target detection period in a fermentation process, the growth stage comprises an adjustment period, a growth period, a stabilization period and a decay period which are arranged in time sequence, the target detection period is one detection period after a standard detection period, and the standard detection period is a preset number of detection periods before the adjustment period, the growth period, the stabilization period or the decay period;

The calculation module is used for confirming a temperature prediction coefficient corresponding to the next detection period based on the growth stage and the temperature influence data corresponding to the target detection period, wherein the temperature prediction coefficient is a smoothing coefficient of a temperature prediction model;

the prediction module is used for predicting the temperature of the next detection period through the temperature prediction model and confirming predicted temperature data corresponding to the next detection period;

the regulation and control module is used for regulating and controlling the fermentation temperature of the next detection period based on the predicted temperature data corresponding to the next detection period;

the temperature influence data comprises first sub data, second sub data and third sub data, and the corresponding confirmation module is used for confirming a growth phase corresponding to a target detection period according to the temperature influence data of the target detection period in a fermentation process, wherein the growth phase comprises an adjustment period, a growth period, a stabilization period and a decay period which are arranged in time sequence, the target detection period is a detection period after a standard detection period, and the standard detection period is a preset number of detection periods before the adjustment period, the growth period, the stabilization period or the decay period, and the method specifically comprises the following steps:

The first sub-confirmation module is used for calculating a first influence factor, a second influence factor and a third influence factor corresponding to the first sub-data, the second sub-data and the third sub-data based on the first sub-data, the second sub-data and the third sub-data corresponding to the target detection period in the standard detection period;

the second sub-confirmation module is used for taking the time corresponding to the minimum values of the first influence factor, the second influence factor and the third influence factor as a stage time node of the growth stage;

a third sub-confirmation module, configured to confirm a growth phase corresponding to the target detection period based on the phase time node;

the first sub-confirmation module is configured to calculate a first influence factor corresponding to first sub-data based on first sub-data corresponding to a standard detection period and a target detection period, where the standard detection period is a preset number of detection periods before an adjustment period, a growth period, a stationary period, or a decay period, and the target detection period is a detection period after the standard detection period, and specifically includes:

a first confirmation unit, configured to combine all data points before the target data point in the target detection period with all data points in the standard detection period, and confirm a first data point sequence;

A second confirmation unit, configured to combine all data points after the target data point in the target detection period with all data points in the standard detection period, and confirm a second data point sequence;

the third confirmation unit is used for determining a first slope and a first correlation parameter corresponding to the first data point sequence through linear fitting of the first data point sequence and preset correlation calculation;

a fourth confirmation unit, configured to determine a second slope and a second correlation parameter corresponding to the second data point sequence through linear fitting of the second data point sequence and preset correlation calculation;

a fifth confirming unit, configured to confirm a first initial impact factor corresponding to the target data point based on the first slope, the second slope, the first correlation parameter, and the second correlation parameter;

a sixth confirming unit, configured to confirm, as the first influence factor, the first initial influence factor that is greater than the preset influence factor threshold;

the fifth confirmation unit is configured to confirm a first initial impact factor corresponding to the target data point based on the first slope, the second slope, the first correlation parameter and the second correlation parameter, and specifically includes:

wherein ,for the target data point +>Corresponding first initial influencing factor, +.>For a first slope corresponding to a first sequence of data points, and (2)>For a second slope corresponding to a second sequence of data points, and (2)>For a first correlation parameter corresponding to a first sequence of data points,/a first correlation parameter corresponding to a second sequence of data points>For a second correlation parameter corresponding to a second sequence of data points,/a second correlation parameter corresponding to a second sequence of data points>Is a long correlation threshold;

the calculation module is configured to determine a temperature prediction coefficient corresponding to a next detection period based on growth stage and temperature influence data corresponding to the target detection period, where the temperature prediction coefficient is a smoothing coefficient of a temperature prediction model, and specifically includes:

the first sub-calculation module is used for confirming the association degree corresponding to the target detection period according to a corresponding relation table of the growth stage corresponding to the target detection period and a preset association degree coefficient;

the second sub-calculation module is used for confirming a temperature influence data change rate sequence corresponding to the target detection period based on the temperature influence data corresponding to the target detection period;

the third sub-calculation module is used for calculating the fluctuation degree and the fluctuation compactness corresponding to the target detection period according to the extreme points of the temperature-affected data change rate sequence;

A fourth sub-calculation module, configured to calculate an interference level corresponding to the target detection period based on the association degree, the fluctuation level, and the fluctuation compactness corresponding to the target detection period, where the interference level includes a first interference level, a second interference level, and a third interference level corresponding to the first sub-data, the second sub-data, and the third sub-data;

a fifth sub-calculation module, configured to input the first interference level, the second interference level, and the third interference level into a preset temperature prediction coefficient calculation model, and calculate a temperature prediction coefficient corresponding to a next detection period;

the temperature-affected data change rate sequence includes a first sub data change rate sequence, a second sub data change rate sequence, and a third sub data change rate sequence corresponding to the first sub data, the second sub data, and the third sub data, the fluctuation degree includes a first fluctuation degree, a second fluctuation degree, and a third fluctuation degree corresponding to the first sub data, the second sub data, and the third sub calculation module is configured to calculate, according to an extremum point of the temperature-affected data change rate sequence, a fluctuation degree corresponding to the target detection period, and specifically includes:

wherein ,for a first degree of fluctuation corresponding to the target detection period, and (2)>For the average value of data points corresponding to all maximum points in the first sub-data change rate sequence, +.>For the average value of data points corresponding to all minimum value points in the first sub-data change rate sequence, +.>For the number of all extreme points +.>All data point numbers for the first sub-data rate of change sequence;

the third sub-calculation module is configured to calculate, according to the extreme point of the temperature-affected data change rate sequence, a fluctuation compactness corresponding to the target detection period, and specifically includes:

wherein ,for a first closeness of fluctuation corresponding to said target detection period,/>A first sub-data rate of change sequence>Extreme points (S)>For the number of extreme points of the first sub-data rate of change sequence,/->Is->Predicted time difference of each extreme point, +.>Is- >Prediction time of each extreme point, +.>Is->Actual time of the extreme points +.>A threshold value for a predicted time difference;

the association degree includes a first association degree, a second association degree, and a third association degree corresponding to the first sub-data, the second sub-data, and the third sub-data, and the fourth sub-calculation module is configured to calculate, based on the association degree, the fluctuation degree, and the fluctuation compactness corresponding to the target detection period, an interference degree corresponding to the target detection period, where the interference degree includes a first interference degree, a second interference degree, and a third interference degree corresponding to the first sub-data, the second sub-data, and the third sub-data, and specifically includes:

wherein ,for a first interference level corresponding to the target detection period, < >>For the first association degree corresponding to the target detection period, < >>For a first degree of fluctuation corresponding to the target detection period, < >>The first fluctuation compactness corresponding to the target detection period;

the fifth sub-calculation module is configured to input the first interference level, the second interference level, and the third interference level into a preset temperature prediction coefficient calculation model, and calculate a temperature prediction coefficient corresponding to a next detection period, and specifically includes:

wherein ,for the temperature prediction coefficient corresponding to the next detection period,/, for>For the first interference level ∈>For the second interference level->For the third interference level->For the first calculation constant, +.>For the second calculation constant, +.>Is a normalization function.