CN115575583A - Artificial intelligence-based microbial fertilizer production quality evaluation system - Google Patents
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Abstract
The invention belongs to the field of microbial fertilizers, relates to a quality detection technology, and is used for solving the problems of low quality monitoring efficiency and complex data statistics process of the existing microbial fertilizer production quality evaluation system, in particular to a microbial fertilizer production quality evaluation system based on artificial intelligence, which comprises a pre-detection module, a cultivation analysis module, a quality monitoring module, a cultivation management module and a storage module, wherein the pre-detection module, the cultivation analysis module and the cultivation management module are sequentially connected in a one-way manner; the method can detect and analyze the cultivation environment before the quality detection of the microbial fertilizer, ensure that the cultivation environment of all the microbial fertilizers can meet the requirements before the quality detection, and improve the accuracy of the quality evaluation result.
Description
Technical Field
The invention belongs to the field of microbial fertilizers, relates to a quality detection technology, and particularly relates to a microbial fertilizer production quality evaluation system based on artificial intelligence.
Background
The microbial strain can be artificially bred, continuously purified and rejuvenated to improve the activity of the fertilizer, and particularly, the required strain can be obtained by a genetic engineering method along with the further development of biotechnology.
Due to the fact that microbial fertilizers are various in types and comprehensive in functions, the existing quality evaluation system needs to adopt different detection standards to carry out quality monitoring on different types of microbial fertilizers, and therefore the problems that quality monitoring efficiency is low, data statistics process is complex and the like are caused; meanwhile, the quality detection results obtained by different methods can only be used for quality evaluation, but can not be used for factor investigation when the quality is unqualified, and can not be used for optimized analysis of the cultivation environment when the quality is qualified.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based microbial fertilizer production quality evaluation system, which is used for solving the problems of low quality monitoring efficiency and complex data statistics process of the conventional microbial fertilizer production quality evaluation system;
the technical problems to be solved by the invention are as follows: how to provide a microbial fertilizer production quality evaluation system which can adopt a unified detection standard to carry out quality monitoring on different microbial fertilizers.
The purpose of the invention can be realized by the following technical scheme:
the system comprises a pre-detection module, a cultivation analysis module, a quality monitoring module, a cultivation management module and a storage module, wherein the pre-detection module, the cultivation analysis module and the cultivation management module are sequentially connected in a one-way mode;
the pre-detection module is used for detecting and analyzing the cultivation environment of the microbial fertilizer before the production quality detection of the microbial fertilizer is carried out, and sending a cultivation analysis signal to the cultivation analysis module when the environment pre-detection result is qualified;
the cultivation analysis module carries out cultivation analysis on the microbial fertilizer after receiving the cultivation analysis signal, marks the type of the microbial fertilizer as a label value of the microbial fertilizer, carries out cultivation environment detection analysis in a regular time in the cultivation process and sends the obtained environment coefficient to the cultivation management module, and generates a quality detection signal and sends the quality detection signal to the quality monitoring module after the cultivation of the microbial fertilizer is finished;
the quality monitoring module carries out growth benefit analysis on the detection object after receiving the quality detection signal, marks the detection object as a beneficial object or a non-beneficial object and sends the non-beneficial object or the beneficial object to the cultivation management module;
the cultivation management module analyzes the useless factors and judges the influence factors with unqualified growth benefit when receiving the useless objects; and the cultivation management module performs optimization analysis and obtains environment standard data when receiving the beneficial objects, matches the environment standard data with the tag value and sends the environment standard data to the storage module for storage.
As a preferred embodiment of the invention, the specific process of the pre-detection module for detecting and analyzing the cultivation environment of the microbial fertilizer comprises the following steps: marking the cultivation soil of the microbial fertilizer as a detection object, and acquiring soil temperature data, acid-base data and water holding data of the detection object; the method comprises the steps of obtaining a pre-detection coefficient of a detection object by carrying out numerical calculation on soil temperature data, acid-base data and water holding data of the detection object; obtaining a pre-detection threshold value through a storage module, and comparing a pre-detection coefficient with the pre-detection threshold value: if the pre-detection coefficient is smaller than the pre-detection threshold value, judging that the environment pre-detection result of the detection object is qualified, and sending a cultivation analysis signal to the cultivation analysis module by the pre-detection module; and if the pre-detection coefficient is larger than or equal to the pre-detection threshold, judging that the environment pre-detection result of the detection object is unqualified, and sending an environment adjusting signal to a mobile phone terminal of a manager by the pre-detection module.
As a preferred embodiment of the present invention, the process of acquiring the soil temperature data includes: acquiring a soil temperature value and a temperature range of a detection object, marking an average value of a maximum value and a minimum value of the temperature range as a soil temperature mean value, and marking an absolute value of a difference value of the soil temperature value and the soil temperature mean value as soil temperature data; the acquisition process of acid-base data comprises the following steps: acquiring the pH value of a detected object, and marking the absolute value of the difference value between the pH value and the numerical value seven as pH data; the water holding data is the water holding amount of the detection object.
As a preferred embodiment of the present invention, the specific process of the quality monitoring module for analyzing the growth benefit of the test object includes: after the microbial fertilizer is cultured, acquiring nitrogen concentration data, phosphorus concentration data and potassium concentration data of a detection object; the method comprises the following steps of obtaining a quality coefficient for microbial cultivation by carrying out numerical calculation on nitrogen concentration data, phosphorus concentration data and potassium concentration data of a detection object, obtaining a quality threshold value through a storage module, and comparing the quality coefficient of the microbial fertilizer with the quality threshold value: if the quality coefficient is smaller than the quality threshold value, judging that the growth benefit of the corresponding detection object is unqualified, and marking the corresponding detection object as a useless object; if the quality coefficient is larger than or equal to the quality threshold, judging that the growth benefit of the corresponding detection object is qualified, and marking the corresponding detection object as a beneficial object; the non-beneficial objects or beneficial objects are sent to the breeding management module.
As a preferred embodiment of the present invention, the acquisition process of the nitrogen concentration data of the detection object includes: extracting volume value L1m in the detected object 3 The soil of (2) is used as a soil sub-object, and a volume value of L2m is extracted above the analysis object 3 The nitrogen content concentration in the soil classification object and the nitrogen content concentration in the air separation object are extracted, and the sum of the nitrogen content concentrations in the soil classification object and the air separation object is marked as nitrogen concentration data; the phosphorus concentration data is a phosphorus element concentration value extracted from the soil classification object; the potassium concentration data is a potassium element concentration value extracted from the soil classification object.
As a preferred embodiment of the present invention, the specific process of the cultivation management module performing the analysis of the non-beneficial factors on the non-beneficial objects includes: establishing an environment set for all received environment coefficients, performing variance calculation on the environment set to obtain a ring wave coefficient, acquiring a preset ring wave threshold, and comparing the ring wave coefficient with the ring wave threshold: if the loop wave coefficient is smaller than the loop wave threshold value, judging that the unqualified growth benefit influence factor of the useless object is unqualified microbial fertilizer quality, and sending an unqualified fertilizer signal to a mobile phone terminal of a manager by the cultivation management module; if the loop wave coefficient is larger than or equal to the loop wave threshold value, judging that the influence factor with unqualified growth benefit of the useless object is environmental fluctuation, and sending an environmental management signal to a mobile phone terminal of a manager by the cultivation management module.
As a preferred embodiment of the present invention, the specific process of the cultivation management module performing the optimization analysis on the beneficial objects includes: classifying all received beneficial objects according to tag values, establishing a beneficial set for the beneficial objects with the same tag value, marking the beneficial objects with the largest mass coefficient in the beneficial set as optimized objects, acquiring a soil temperature value, a pH value and a water holding capacity when the optimized objects are subjected to cultivation environment detection analysis, marking the soil temperature value, the pH value and the water holding capacity as environmental standard data of the beneficial set, matching the environmental standard data of the beneficial set with the tag values, and sending the environmental standard data of the beneficial set to a storage module for storage.
As a preferred embodiment of the invention, the working method of the artificial intelligence based microbial fertilizer production quality evaluation system comprises the following steps:
the method comprises the following steps: before the production quality detection of the microbial fertilizer is carried out, the cultivation environment of the microbial fertilizer is detected and analyzed to obtain a pre-detection coefficient, and whether the environmental pre-detection result of a detection object is qualified or not is judged according to the numerical value of the pre-detection coefficient;
step two: and (3) carrying out cultivation analysis on the microbial fertilizer: marking the type of the microbial fertilizer as a label value of the microbial fertilizer; applying the microbial fertilizer into a detection object for cultivation according to a fertilizing method of a label value, and regularly performing cultivation environment detection analysis in the cultivation process;
step three: after the microbial fertilizer is cultivated, carrying out growth benefit analysis on the detection object to obtain a mass coefficient, and marking the detection object as a beneficial object or a non-beneficial object according to the numerical value of the mass coefficient;
step four: analyzing the useless objects by using useless factors and judging the influence factors of the useless objects with unqualified growth benefit; and performing optimization analysis on the beneficial objects to obtain environment standard data, matching the environment standard data with the tag value, and sending the environment standard data to the storage module for storage.
The invention has the following beneficial effects:
1. the cultivation environment can be detected and analyzed before the quality of the microbial fertilizer is detected through the pre-detection module, and the cultivation environment of all the microbial fertilizers can meet the requirements before the quality is detected, so that the influence of the cultivation environment on the cultivation of the microbes is reduced, the accuracy of quality evaluation results is improved, and meanwhile, the cultivation environment can be timely adjusted when the cultivation environment is unqualified;
2. the cultivation analysis module can be used for cultivating and analyzing the microbial fertilizer, recording the type of the microbial fertilizer and the environmental coefficient regularly acquired in the cultivation process, and providing data support for factor analysis of the useless objects and cultivation optimization analysis of the beneficial objects;
3. the quality monitoring module can analyze the growth benefit of the detection object, the quality monitoring process is to compare the beneficial influence exerted by the microbial fertilizer in the detection object with the processing quality of the microbial fertilizer, the concentration of elements beneficial to and growing by organisms in the cultivated soil is extracted and analyzed by utilizing the growth rule of the organisms, the growth benefit of the soil is taken as a guide, the production quality of the microbial fertilizer is detected and evaluated, and the quality detection efficiency is improved;
4. the cultivation management module can analyze the useless factors when receiving the useless objects, mark the reasons causing the unqualified growth benefit, and feed back the actual production quality of the microbial fertilizer; and the beneficial objects can be optimized and analyzed when being received, so that the optimal cultivation environment of each variety of microbial fertilizer is obtained, and the subsequent use effect of the microbial fertilizer is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in figure 1, the microbial fertilizer production quality evaluation system based on artificial intelligence comprises a pre-detection module, a cultivation analysis module, a quality monitoring module, a cultivation management module and a storage module, wherein the pre-detection module, the cultivation analysis module and the cultivation management module are sequentially connected in a one-way mode, the quality monitoring module is connected with the cultivation analysis module and the cultivation management module in a one-way mode, and the storage module is connected with the pre-detection module and the quality monitoring module in a one-way mode.
The pre-detection module is used for detecting and analyzing the cultivation environment of the microbial fertilizer before the production quality detection of the microbial fertilizer: marking the cultivation soil of the microbial fertilizer as a detection object, acquiring soil temperature data TW, acid and alkali data SJ and water retention data CS of the detection object, and acquiring the soil temperature data TWThe process comprises the following steps: acquiring a soil temperature value and a temperature range of a detection object, marking an average value of a maximum value and a minimum value of the temperature range as a soil temperature average value, and marking an absolute value of a difference value of the soil temperature value and the soil temperature average value as soil temperature data TW; the process for acquiring the acid-base data SJ comprises the following steps: acquiring the pH value of a detected object, and marking the absolute value of the difference value between the pH value and the seven value as acid-base data SJ; water holding data CS is the water holding amount of the detection object; by the formula
Obtaining a pre-detection coefficient YJ of the detection object, wherein the pre-detection coefficient is a numerical value reflecting the degree of microbial fertilizer cultivation suitable for the detection object, and the smaller the numerical value of the pre-detection coefficient is, the higher the degree of microbial fertilizer cultivation suitable for the detection object is, and the smaller the influence of the cultivation environment on the quality evaluation result is; wherein alpha 1, alpha 2 and alpha 3 are proportionality coefficients, alpha 3 is more than alpha 2 and more than alpha 1 and more than 1, e is a natural constant, and the value of e is 2.78; obtaining a pre-detection threshold YJmax through a storage module, and comparing the pre-detection coefficient YJ with the pre-detection threshold YJmax: if the pre-detection coefficient YJ is smaller than the pre-detection threshold YJmax, judging that the environment pre-detection result of the detection object is qualified, and sending a cultivation analysis signal to the cultivation analysis module by the pre-detection module; if the pre-detection coefficient YJ is larger than or equal to the pre-detection threshold YJmax, judging that the environment pre-detection result of the detection object is unqualified, and sending an environment adjusting signal to a mobile phone terminal of a manager by a pre-detection module; the cultivation environment is detected and analyzed before the quality of the microbial fertilizer is detected, and the cultivation environment of all the microbial fertilizers before the quality detection can meet the requirements, so that the influence of the cultivation environment on the microbial cultivation is reduced, the accuracy of a quality evaluation result is improved, and the microbial fertilizer can be timely adjusted when the cultivation environment is unqualified.
The cultivation analysis module is used for carrying out cultivation analysis on the microbial fertilizer after receiving the cultivation analysis signal: marking the type of the microbial fertilizer as a label value of the microbial fertilizer; applying the microbial fertilizer into a detection object for cultivation according to a fertilizing method of a label value, regularly performing cultivation environment detection analysis in the cultivation process, sending the obtained environment coefficient to a cultivation management module, and generating a quality detection signal and sending the quality detection signal to a quality monitoring module after the microbial fertilizer is cultivated; the types of the microbial fertilizer and the environmental coefficients acquired regularly in the cultivation process are recorded, and data support is provided for factor analysis of useless objects and cultivation optimization analysis of beneficial objects.
The quality monitoring module is used for carrying out growth benefit analysis on the detection object after receiving the quality detection signal: after the microbial fertilizer is cultured, acquiring nitrogen concentration data DN, phosphorus concentration data LN and potassium concentration data JN of a detection object, wherein the acquisition process of the nitrogen concentration data DN of the detection object comprises the following steps: extracting a volume value of L1m from the detection object 3 The soil of (2) is used as a soil sub-object, and a volume value of L2m is extracted above the analysis object 3 The air is taken as an air separation object, the nitrogen content concentrations in the soil separation object and the air separation object are extracted, and the sum of the nitrogen content concentrations in the soil separation object and the air separation object is marked as nitrogen concentration data DN; the phosphorus concentration data LN is a phosphorus element concentration value extracted from the soil classification object; the potassium concentration data JN is a potassium concentration value extracted from the soil classification object; obtaining a mass coefficient ZL for culturing the microorganisms through a formula ZL = beta 1 + DN + beta 2 + LN + beta 3 + JN, wherein the mass coefficient is a numerical value reflecting the degree of the beneficial organisms growing on the detection object, and the larger the numerical value of the mass coefficient is, the more beneficial the organisms growing on the detection object is, the higher the quality of the microbial fertilizer is; obtaining a quality threshold value ZLmin through a storage module, and comparing a quality coefficient ZL of the microbial fertilizer with the quality threshold value ZLmin: if the quality coefficient ZL is smaller than the quality threshold ZLmin, judging that the growth benefit of the corresponding detection object is unqualified, and marking the corresponding detection object as an useless object; if the mass coefficient ZL is larger than or equal to the mass threshold ZLmin, judging that the growth benefit of the corresponding detection object is qualified, and marking the corresponding detection object as a beneficial object; sending the useless objects or the beneficial objects to the cultivation management module; the quality monitoring process is that the beneficial influence exerted by the microbial fertilizer in the detection object is analogized to the processing quality of the microbial fertilizer, and the concentration extraction and the separation of the beneficial and biological growth elements in the cultivated soil are carried out by utilizing the biological growth ruleAnd analyzing, wherein the growth benefit of the soil is taken as a guide, the production quality of the microbial fertilizer is detected and evaluated, and the quality detection efficiency is improved.
As will be appreciated, in the field of application of microbial fertilizers, rhizobia can fix nitrogen in the air to supply plant nitrogen nutrients; the nitrogen fixing microbial inoculum can be used as a base fertilizer, an additional fertilizer or root dipping and seed dressing, directly fixes nitrogen in the air so as to improve the nitrogen nutrition of plants, and can be applied to most crops; potassium bacteria can decompose potassium-containing silicate in soil, and the application of potassium bactericides can improve the effective contents of nitrogen, phosphorus and potassium in the soil, improve plant nutrition and increase plant yield; therefore, the beneficial element concentrations of different varieties of microbial fertilizers are almost improved in a mode of generating benign influence on soil and crops, and therefore under the condition of eliminating the influence caused by the cultivation environment, the analysis result is carried out by combining the beneficial element concentrations on the growth benefits of the soil, and the quality monitoring analysis result of the microbial fertilizer can be converted.
The cultivation management module is used for carrying out cultivation management analysis when receiving the useless objects or the beneficial objects: and (3) performing useless factor analysis when a useless object is received: establishing an environment set for all received environment coefficients, performing variance calculation on the environment set to obtain a ring wave coefficient, acquiring a preset ring wave threshold, and comparing the ring wave coefficient with the ring wave threshold: if the ring wave coefficient is smaller than the ring wave threshold value, judging that the growth benefit disqualification influence factor of the useless object is disqualified microbial fertilizer quality, and sending a fertilizer disqualification signal to a mobile phone terminal of a manager by the cultivation management module; if the loop wave coefficient is larger than or equal to the loop wave threshold value, judging that the influence factor of unqualified growth benefit of the useless object is environmental fluctuation, and sending an environmental management signal to a mobile phone terminal of a manager by the cultivation management module; and (3) carrying out optimization analysis when the beneficial objects are received: classifying all received beneficial objects according to tag values, establishing a beneficial set of the beneficial objects with the same tag value, marking the beneficial object with the largest mass coefficient in the beneficial set as an optimized object, acquiring a soil temperature value, a pH value and a water holding capacity when the optimized object is subjected to cultivation environment detection and analysis, marking the soil temperature value, the pH value and the water holding capacity as environmental standard data of the beneficial set, matching the environmental standard data of the beneficial set with the tag values, and sending the environmental standard data of the beneficial set to a storage module for storage; when receiving the useless objects, analyzing the useless objects by using useless factors, and marking the reasons causing the unqualified growth benefit, thereby feeding back the actual production quality of the microbial fertilizer; and the beneficial objects can be optimized and analyzed when being received, so that the optimal cultivation environment of each variety of microbial fertilizer is obtained, and the subsequent use effect of the microbial fertilizer is improved.
It should be noted that when the growth benefit disqualification influencing factor of the useless object is the microorganism fertilizer quality disqualification, the traditional microorganism fertilizer quality detection method (such as the detection of the effective viable count and the content of the mixed bacteria) can be adopted to carry out the quality detection on the microorganism fertilizer, and if the analysis result of the traditional detection method is consistent with that of the quality monitoring module, the microorganism fertilizer quality disqualification is judged; if the microbial fertilizers are inconsistent, deleting the label value corresponding to the microbial fertilizer from the storage module, and only carrying out quality detection on the microbial fertilizer containing the label value in the storage module in the subsequent quality evaluation; therefore, the application range of the quality evaluation system is optimized and limited, and the accuracy of the quality detection result is further improved.
Example two
As shown in fig. 2, the artificial intelligence based method for evaluating the production quality of microbial fertilizer comprises the following steps:
the method comprises the following steps: before the production quality detection of the microbial fertilizer is carried out, the cultivation environment of the microbial fertilizer is detected and analyzed to obtain a pre-detection coefficient, whether the environment pre-detection result of a detection object is qualified or not is judged according to the numerical value of the pre-detection coefficient, the accuracy of the quality evaluation result is improved, and meanwhile, the cultivation environment can be timely adjusted when the cultivation environment is unqualified;
step two: and (3) carrying out cultivation analysis on the microbial fertilizer: marking the type of the microbial fertilizer as a label value of the microbial fertilizer; applying the microbial fertilizer into a detection object for cultivation according to a fertilizing method of a label value, and regularly performing cultivation environment detection analysis in the cultivation process to provide data support for factor analysis of useless objects and cultivation optimization analysis of beneficial objects;
step three: after the microbial fertilizer is cultured, carrying out growth benefit analysis on the detection object to obtain a quality coefficient, marking the detection object as a beneficial object or an unprofitable object according to the numerical value of the quality coefficient, and detecting and evaluating the production quality of the microbial fertilizer by taking the growth benefit of soil as guidance;
step four: carrying out non-beneficial factor analysis on the non-beneficial objects, judging the influence factors of the non-beneficial objects with unqualified growth benefit, and feeding back the actual production quality of the microbial fertilizer; and the beneficial objects are optimized and analyzed to obtain environment standard data, and the environment standard data and the tag value are matched and sent to the storage module for storage, so that the use effect of the subsequent microbial fertilizer is improved.
When the system works, the cultivation environment of the microbial fertilizer is detected and analyzed before the production quality of the microbial fertilizer is detected, a pre-detection coefficient is obtained, whether the environmental pre-detection result of a detection object is qualified or not is judged according to the numerical value of the pre-detection coefficient, the accuracy of the quality evaluation result is improved, and meanwhile, the system can be timely adjusted when the cultivation environment is unqualified; and (3) carrying out cultivation analysis on the microbial fertilizer: marking the type of the microbial fertilizer as a label value of the microbial fertilizer; applying the microbial fertilizer into a detection object for cultivation according to a fertilizing method of a label value, and performing cultivation environment detection analysis regularly in the cultivation process to provide data support for factor analysis of useless objects and cultivation optimization analysis of beneficial objects; and after the cultivation of the microbial fertilizer is finished, analyzing the growth benefit of the detection object to obtain a quality coefficient, marking the detection object as a beneficial object or a non-beneficial object according to the numerical value of the quality coefficient, and detecting and evaluating the production quality of the microbial fertilizer by taking the growth benefit of soil as a guide.
The foregoing is merely illustrative and explanatory of the present invention and various modifications, additions or substitutions may be made to the specific embodiments described by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: formula (II)
(ii) a Collecting multiple groups of sample data and setting corresponding pre-detection coefficients for each group of sample data by a person skilled in the art; substituting the set pre-detection coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are respectively 2.65, 3.84 and 8.14;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding harmful coefficient preliminarily set by a person skilled in the art for each group of sample data; the method is only required to be carried out without influencing the proportional relation between the parameters and the quantized numerical value, such as the harmful coefficient is in direct proportion to the numerical value of the ammonia gas content.
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. The system is characterized by comprising a pre-detection module, a cultivation analysis module, a quality monitoring module, a cultivation management module and a storage module, wherein the pre-detection module, the cultivation analysis module and the cultivation management module are sequentially connected in a one-way manner; the pre-detection module is used for detecting and analyzing the cultivation environment of the microbial fertilizer before the production quality detection of the microbial fertilizer is carried out, and sending a cultivation analysis signal to the cultivation analysis module when the environment pre-detection result is qualified; the cultivation analysis module carries out cultivation analysis on the microbial fertilizer after receiving the cultivation analysis signal, marks the type of the microbial fertilizer as a label value of the microbial fertilizer, carries out cultivation environment detection analysis regularly in the cultivation process and sends the obtained environment coefficient to the cultivation management module, and generates a quality detection signal and sends the quality detection signal to the quality monitoring module after the cultivation of the microbial fertilizer is finished; the quality monitoring module carries out growth benefit analysis on the detection object after receiving the quality detection signal, marks the detection object as a beneficial object or a non-beneficial object and sends the non-beneficial object or the beneficial object to the cultivation management module; the cultivation management module analyzes the useless factors and judges the influence factors with unqualified growth benefit when receiving the useless objects; and the cultivation management module performs optimization analysis and obtains environment standard data when receiving the beneficial objects, matches the environment standard data with the tag value and sends the environment standard data to the storage module for storage.
2. The artificial intelligence based microbial fertilizer production quality evaluation system of claim 1, wherein the specific process of the pre-detection module for performing detection analysis on the cultivation environment of the microbial fertilizer comprises the following steps: marking the cultivation soil of the microbial fertilizer as a detection object, and acquiring soil temperature data, acid-base data and water holding data of the detection object; the method comprises the steps of obtaining a pre-detection coefficient of a detection object by carrying out numerical calculation on soil temperature data, acid-base data and water holding data of the detection object; obtaining a pre-detection threshold value through a storage module, and comparing a pre-detection coefficient with the pre-detection threshold value: if the pre-detection coefficient is smaller than the pre-detection threshold value, judging that the environmental pre-detection result of the detection object is qualified, and sending a cultivation analysis signal to the cultivation analysis module by the pre-detection module; and if the pre-detection coefficient is larger than or equal to the pre-detection threshold, judging that the environment pre-detection result of the detection object is unqualified, and sending an environment adjusting signal to a mobile phone terminal of a manager by the pre-detection module.
3. The artificial intelligence based microbial fertilizer production quality evaluation system of claim 1, wherein the process of obtaining soil temperature data comprises: acquiring a soil temperature value and a temperature range of a detection object, marking an average value of a maximum value and a minimum value of the temperature range as a soil temperature mean value, and marking an absolute value of a difference value of the soil temperature value and the soil temperature mean value as soil temperature data; the acquisition process of the acid-base data comprises the following steps: acquiring the pH value of a detection object, and marking the absolute value of the difference value between the pH value and the numerical value seven as pH data; the water holding data is the water holding amount of the detection object.
4. The artificial intelligence based microbial fertilizer production quality evaluation system of claim 1, wherein the specific process of the quality monitoring module for analyzing the growth benefit of the detection object comprises the following steps: after the microbial fertilizer is cultured, acquiring nitrogen concentration data, phosphorus concentration data and potassium concentration data of a detection object; the method comprises the following steps of obtaining a quality coefficient of microbial cultivation by carrying out numerical calculation on nitrogen concentration data, phosphorus concentration data and potassium concentration data of a detection object, obtaining a quality threshold value through a storage module, and comparing the quality coefficient of the microbial fertilizer with the quality threshold value: if the quality coefficient is smaller than the quality threshold value, judging that the growth benefit of the corresponding detection object is unqualified, and marking the corresponding detection object as a useless object; if the quality coefficient is larger than or equal to the quality threshold, judging that the growth benefit of the corresponding detection object is qualified, and marking the corresponding detection object as a beneficial object; the non-beneficial objects or beneficial objects are sent to the breeding management module.
5. The artificial intelligence based microbial fertilizer production quality evaluation system of claim 1, wherein the acquisition process of the nitrogen concentration data of the detection object comprises: extracting volume value L1m in the detected object 3 The soil of (2) is used as a soil sub-object, and a volume value of L2m is extracted above the analysis object 3 The air is taken as an air separation object, the nitrogen content concentrations in the soil separation object and the air separation object are extracted, and the sum of the nitrogen content concentrations in the soil separation object and the air separation object is marked as nitrogen concentration data; the phosphorus concentration data is a phosphorus element concentration value extracted from the soil classification object; the potassium concentration data is a potassium element concentration value extracted from the soil classification object.
6. The artificial intelligence based microbial fertilizer production quality evaluation system of claim 1, wherein the specific process of the cultivation management module performing the analysis of the non-beneficial factors on the non-beneficial objects comprises: establishing an environment set for all received environment coefficients, performing variance calculation on the environment set to obtain a ring wave coefficient, acquiring a preset ring wave threshold, and comparing the ring wave coefficient with the ring wave threshold: if the ring wave coefficient is smaller than the ring wave threshold value, judging that the growth benefit disqualification influence factor of the useless object is disqualified microbial fertilizer quality, and sending a fertilizer disqualification signal to a mobile phone terminal of a manager by the cultivation management module; if the ring wave coefficient is larger than or equal to the ring wave threshold value, judging that the influence factor of the useless object with unqualified growth benefit is environmental fluctuation, and sending an environmental management signal to a mobile phone terminal of a manager by the cultivation management module.
7. An artificial intelligence based microbial fertilizer production quality assessment system according to claim 1, wherein the specific process of the cultivation management module to perform optimization analysis on beneficial objects comprises: classifying all received beneficial objects according to the label values, establishing a beneficial set of the beneficial objects with the same label value, marking the beneficial object with the largest mass coefficient in the beneficial set as an optimized object, acquiring a soil temperature value, a pH value and a water holding capacity when the optimized object is subjected to cultivation environment detection and analysis, marking the soil temperature value, the pH value and the water holding capacity as environmental standard data of the beneficial set, matching the environmental standard data of the beneficial set with the label values, and sending the environmental standard data of the beneficial set to a storage module for storage.
8. The artificial intelligence based microbial fertilizer production quality evaluation system of claim 1, wherein the working method of the artificial intelligence based microbial fertilizer production quality evaluation system comprises the following steps: the method comprises the following steps: before the production quality detection of the microbial fertilizer is carried out, the cultivation environment of the microbial fertilizer is detected and analyzed to obtain a pre-detection coefficient, and whether the environmental pre-detection result of a detection object is qualified or not is judged according to the numerical value of the pre-detection coefficient; step two: and (3) carrying out cultivation analysis on the microbial fertilizer: marking the type of the microbial fertilizer as a label value of the microbial fertilizer; applying the microbial fertilizer into a detection object for cultivation according to a fertilizing method of a label value, and regularly performing cultivation environment detection analysis in the cultivation process; step three: after the microbial fertilizer is cultivated, carrying out growth benefit analysis on the detection object to obtain a mass coefficient, and marking the detection object as a beneficial object or a non-beneficial object according to the numerical value of the mass coefficient; step four: analyzing the useless objects by using useless factors and judging the influence factors of the useless objects with unqualified growth benefit; and performing optimization analysis on the beneficial objects to obtain environment standard data, matching the environment standard data with the tag value, and sending the environment standard data to the storage module for storage.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1325440A (en) * | 1998-11-02 | 2001-12-05 | 詹姆斯·J·麦考奈里 | Method and apparatus for controlled composting and bioremediating |
| JP2002345331A (en) * | 2001-05-28 | 2002-12-03 | Katakura Chikkarin Co Ltd | Field crop production support system |
| CN201254535Y (en) * | 2008-05-11 | 2009-06-10 | 郭伟 | System for producing microorganism water retention compound organic fertilizer |
| CN105557280A (en) * | 2016-01-15 | 2016-05-11 | 沃华农业科技（江苏）股份有限公司 | Method for managing environmental factors during Chinese onion seedling culture |
| CN106116728A (en) * | 2016-06-27 | 2016-11-16 | 东北农业大学 | A kind of compost method of difficult cultivating microorganism Bacterial community regulation and control |
| CN106867941A (en) * | 2017-03-16 | 2017-06-20 | 新疆肥力沃生物工程有限公司 | A kind of microorganism-decomposing agent for livestock and poultry feces discarded object and its preparation method and application |
| CN108949589A (en) * | 2018-08-24 | 2018-12-07 | 泸州众康农业检测有限公司 | The effective detection method of bacterium in a kind of liquid microbe bacterial manure |
| CN109769615A (en) * | 2019-01-25 | 2019-05-21 | 广西壮族自治区农业科学院 | A method of promoting peanut nodule fixed nitrogen |
| CN109924064A (en) * | 2019-04-13 | 2019-06-25 | 杨和金 | A kind of edible fungus culturing optimization system |
| CN209508068U (en) * | 2019-01-03 | 2019-10-18 | 中国农业科学院农业环境与可持续发展研究所 | A kind of microbial-bacterial fertilizer production system |
| CN110862280A (en) * | 2019-12-25 | 2020-03-06 | 赵莉莉 | Organic fertilizer fermentation production method and system |
| JP2022047305A (en) * | 2020-09-11 | 2022-03-24 | 株式会社クボタ | Information management system |
| CN114280276A (en) * | 2021-12-23 | 2022-04-05 | 青岛农业大学 | Agricultural monitoring system and method |
| CN114324334A (en) * | 2021-12-30 | 2022-04-12 | 中国热带农业科学院热带作物品种资源研究所 | Evaluation system of mango germplasm resources nutritional quality |
| CN114766333A (en) * | 2022-05-09 | 2022-07-22 | 邢台市农业科学研究院 | Fruit tree plant networking regulation and control system |
| CN114993387A (en) * | 2022-07-18 | 2022-09-02 | 深圳市联智通达智能有限公司 | Mainboard production and processing supervisory systems based on artificial intelligence |
-
2022
- 2022-11-17 CN CN202211437062.XA patent/CN115575583B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1325440A (en) * | 1998-11-02 | 2001-12-05 | 詹姆斯·J·麦考奈里 | Method and apparatus for controlled composting and bioremediating |
| JP2002345331A (en) * | 2001-05-28 | 2002-12-03 | Katakura Chikkarin Co Ltd | Field crop production support system |
| CN201254535Y (en) * | 2008-05-11 | 2009-06-10 | 郭伟 | System for producing microorganism water retention compound organic fertilizer |
| CN105557280A (en) * | 2016-01-15 | 2016-05-11 | 沃华农业科技（江苏）股份有限公司 | Method for managing environmental factors during Chinese onion seedling culture |
| CN106116728A (en) * | 2016-06-27 | 2016-11-16 | 东北农业大学 | A kind of compost method of difficult cultivating microorganism Bacterial community regulation and control |
| CN106867941A (en) * | 2017-03-16 | 2017-06-20 | 新疆肥力沃生物工程有限公司 | A kind of microorganism-decomposing agent for livestock and poultry feces discarded object and its preparation method and application |
| CN108949589A (en) * | 2018-08-24 | 2018-12-07 | 泸州众康农业检测有限公司 | The effective detection method of bacterium in a kind of liquid microbe bacterial manure |
| CN209508068U (en) * | 2019-01-03 | 2019-10-18 | 中国农业科学院农业环境与可持续发展研究所 | A kind of microbial-bacterial fertilizer production system |
| CN109769615A (en) * | 2019-01-25 | 2019-05-21 | 广西壮族自治区农业科学院 | A method of promoting peanut nodule fixed nitrogen |
| CN109924064A (en) * | 2019-04-13 | 2019-06-25 | 杨和金 | A kind of edible fungus culturing optimization system |
| CN110862280A (en) * | 2019-12-25 | 2020-03-06 | 赵莉莉 | Organic fertilizer fermentation production method and system |
| JP2022047305A (en) * | 2020-09-11 | 2022-03-24 | 株式会社クボタ | Information management system |
| CN114280276A (en) * | 2021-12-23 | 2022-04-05 | 青岛农业大学 | Agricultural monitoring system and method |
| CN114324334A (en) * | 2021-12-30 | 2022-04-12 | 中国热带农业科学院热带作物品种资源研究所 | Evaluation system of mango germplasm resources nutritional quality |
| CN114766333A (en) * | 2022-05-09 | 2022-07-22 | 邢台市农业科学研究院 | Fruit tree plant networking regulation and control system |
| CN114993387A (en) * | 2022-07-18 | 2022-09-02 | 深圳市联智通达智能有限公司 | Mainboard production and processing supervisory systems based on artificial intelligence |
Non-Patent Citations (5)
| Title |
|---|
| YAJUN GENG: "Decreased nitrous oxide emissions associated with functional microbial genes under bio-organic fertilizer application in vegetable fields", 《PEDOSPHERE》 * |
| 张秀平: "基于自动化设备技术的土壤微生物培育技术温室调控研究", 《自动化应用》 * |
| 朱莹;田浩;王芸;杨柳青;张晓君;: "环境因子对土壤微生物产生和还原N_2O过程的影响" * |
| 李武等: "分子生态学方法在微生物肥料质量监测中的应用", 《微生物学通报》 * |
| 许景钢等: "我国微生物肥料的研发及其在农业生产中的应用", 《作物杂志》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117871823A (en) * | 2024-01-12 | 2024-04-12 | 北京中化联合认证有限公司 | Green intelligent design evaluation authentication method and system for fertilizer product |
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Denomination of invention: A Quality Evaluation System for Microbial Fertilizer Production Based on Artificial Intelligence Effective date of registration: 20230713 Granted publication date: 20230331 Pledgee: China Postal Savings Bank Limited by Share Ltd. Wenshang County sub branch Pledgor: SHANDONG TUMUQI BIOTECHNOLOGY Co.,Ltd. Registration number: Y2023980048249 |