CN117215351A - Intelligent monitoring and early warning system for edible fungus planting environment - Google Patents
Intelligent monitoring and early warning system for edible fungus planting environment Download PDFInfo
- Publication number
- CN117215351A CN117215351A CN202311195060.9A CN202311195060A CN117215351A CN 117215351 A CN117215351 A CN 117215351A CN 202311195060 A CN202311195060 A CN 202311195060A CN 117215351 A CN117215351 A CN 117215351A
- Authority
- CN
- China
- Prior art keywords
- value
- early warning
- data
- user
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 69
- 241000233866 Fungi Species 0.000 title claims abstract description 64
- 230000007613 environmental effect Effects 0.000 claims abstract description 87
- 238000005286 illumination Methods 0.000 claims abstract description 37
- 238000012545 processing Methods 0.000 claims abstract description 26
- 238000004458 analytical method Methods 0.000 claims abstract description 14
- 230000002159 abnormal effect Effects 0.000 claims abstract description 12
- 230000002262 irrigation Effects 0.000 claims abstract description 6
- 238000003973 irrigation Methods 0.000 claims abstract description 6
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 38
- 238000004519 manufacturing process Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 24
- 241000607479 Yersinia pestis Species 0.000 claims description 21
- 230000004720 fertilization Effects 0.000 claims description 21
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 19
- 239000001569 carbon dioxide Substances 0.000 claims description 19
- 201000010099 disease Diseases 0.000 claims description 19
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 19
- 238000011156 evaluation Methods 0.000 claims description 17
- 238000013079 data visualisation Methods 0.000 claims description 16
- 238000012937 correction Methods 0.000 claims description 12
- 239000003102 growth factor Substances 0.000 claims description 11
- 238000000034 method Methods 0.000 claims description 10
- 238000012800 visualization Methods 0.000 claims description 10
- 238000007405 data analysis Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 8
- 238000011217 control strategy Methods 0.000 claims description 6
- 238000009423 ventilation Methods 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 claims description 4
- 230000005856 abnormality Effects 0.000 claims description 3
- 230000009471 action Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 2
- 239000000284 extract Substances 0.000 abstract 1
- 238000007726 management method Methods 0.000 description 11
- 230000001276 controlling effect Effects 0.000 description 4
- 235000001674 Agaricus brunnescens Nutrition 0.000 description 2
- 241000238631 Hexapoda Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Abstract
The invention discloses an intelligent monitoring and early warning system for edible fungus planting environments, which relates to the technical field of monitoring and early warning, wherein the system collects environmental parameter data through a sensor group, extracts key information through data transmission and real-time processing, and acquires: unsuitable value Bsyz, a model is built based on the calculated data information to obtain: the growth coefficient Szxs and the growth condition value Sztj are compared with a preset threshold value through an environment safety index Hjaq to detect whether an edible fungus planting environment has abnormal conditions, once the abnormal conditions are identified, the system generates corresponding alarm and early warning notification, the alarm can be sent out and advice can be provided through real-time early warning, a user is allowed to remotely monitor and adjust environment parameters, the environment conditions are adjusted in real time, the system comprises an irrigation, temperature and illumination control system, user-friendly interface display data are provided, the real-time state and historical trend of the environment parameters are intuitively displayed to help the user to understand the data better, and the environment conditions are automatically controlled according to analysis results and user instructions.
Description
Technical Field
The invention relates to the technical field of monitoring and early warning, in particular to an intelligent monitoring and early warning system for edible fungus planting environments.
Background
Edible fungus planting environment detection early warning is a vital ring in modern agriculture, edible fungus planting industry is widely focused and developed as an important agricultural product production field, however, the growth of edible fungi is very sensitive to environmental conditions including temperature, humidity, illumination, fertilization amount and plant diseases and insect pests, farmers and growers need to monitor and maintain the environmental parameters in an optimal state in order to promote the output of high-yield and high-quality edible fungi, and an intelligent monitoring early warning system is developed in order to meet the requirement, integrates advanced sensing technology and data analysis method, and aims to realize intelligent monitoring and management of edible fungus planting environment.
Although intelligent monitoring and early warning systems of edible fungus planting environments represent a great advance in modern agriculture, conventional edible fungus planting methods still have problems and disadvantages, in the past, farmers often relied on experience and feel to manage planting environments, which may lead to instability of growth condition values and production uncertainty, and secondly, even with sensors and monitoring systems, there are still certain limitations in data processing and analysis, because these systems may lack advanced algorithms to accurately predict problems, and furthermore, conventional systems may not provide the ability to remotely monitor and automatically control, which limits the management flexibility of farmers at different sites and times. Therefore, the development of the intelligent monitoring and early warning system for the edible fungus planting environment is important to solve the existing problems, and a more efficient, sustainable and intelligent edible fungus planting management method is provided to meet the growing market demands.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides an intelligent monitoring and early warning system for edible fungus planting environments, which solves the problems in the background art.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: an intelligent monitoring and early warning system for edible fungi planting environments comprises a sensor module, a data acquisition and processing module, a modeling module, a real-time early warning module, a remote control module, a data visualization module and an automatic control module;
the sensor module is used for collecting environmental parameter data through the sensor group to be used as a first data group;
the data acquisition processing module is used for transmitting acquired data to the modeling module, carrying out real-time data processing and analysis, and extracting key information characteristics to obtain: unsuitable value Bsyz;
the modeling module is used for processing sensor data by using data analysis and a machine learning algorithm, identifying abnormal modes, trends and potential problems, carrying out combined calculation on the first data set and unsuitable value Bryz, and establishing a characteristic model to obtain the characteristic model by calculation: environmental safety index Hjaq;
the environmental safety index Hjaq is obtained by the following formula:
Hjaq＝(Szxs*64％)*α+(Sztj*β)*50％+(Bsyz*χ)+F
wherein Szxs represents a growth factor, sztj represents a growth condition value, bsyz represents an unsuitable value, α represents a weight value of the growth factor Szxs, β represents a weight value of the growth condition value Sztj, χ represents a weight value of the unsuitable value Bsyz, and F represents a correction constant;
wherein alpha is more than or equal to 0.55 and less than or equal to 0.75,0.45, beta is more than or equal to 0.55,0.35 and χ is more than or equal to 0.55, wherein alpha+beta+χ is more than or equal to 1.5;
the unsuitable value Bsyz is calculated by the first data set;
the growth coefficient Szxs is obtained through calculation of the first data set and the unsuitable value Bryz;
the growth condition value Sztj is obtained through calculation of the first data set and the unsuitable value Bsyz;
the real-time early warning module is used for sending out a real-time alarm to inform a user and provide advice or action advice once the system detects a potential problem or abnormal environmental condition;
the remote control module is used for allowing a user to remotely monitor and control environmental parameters and adjust environmental conditions in real time through a mobile phone application or a network interface;
the data visualization module is used for providing a user-friendly data visualization interface so as to intuitively display the real-time state and the historical trend of the environmental parameters and help a user to better understand the data;
the automatic control module is used for automatically controlling environmental parameters according to the data analysis result and the user instruction and automatically adjusting the irrigation, temperature and illumination control system.
Preferably, the sensor module includes a sensor monitoring unit;
the sensor monitoring unit is used for connecting a sensor group to collect environmental parameter data, and comprises a temperature sensor, a humidity sensor, a carbon dioxide sensor and an illumination sensor, and acquiring a temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, difference values Jzlx of different strains, a disease and pest rate Bchl and a fertilization frequency Sfpl;
the first data set comprises a temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, a difference value Jzlx of different strains, a disease and pest rate Bchl and a fertilization frequency Sfpl.
Preferably, the data acquisition processing module comprises a real-time data processing unit;
the real-time data processing unit is used for calculating the pest and disease rate Bchl and fertilization frequency Sfpl acquired by the first data set to acquire: unsuitable value Bsyz;
the unsuitable value Bsyz is calculated and obtained by the following formula:
in Bchl represents the pest and disease rate, sfpl represents the fertilization frequency, a 1 Weight value, a, representing pest and disease rate Bchl 2 A weight value representing the fertilization frequency Sfpl;
wherein a is more than or equal to 0.55 1 ≤0.85，0.45≤a 2 Less than or equal to 0.75, wherein a 1 +a 2 ≤1.0。
Preferably, the modeling module includes a parameter calculation unit;
the parameter calculation unit is configured to perform modeling, analysis, and training according to the first data set and the inappropriateness value Bsyz information, so as to obtain: growth factor Szxs and growth condition value Sztj.
Preferably, the growth factor Szxs is calculated and obtained according to the following formula:
wherein Sjsd represents the actual growth rate, zjsd represents the optimal growth rate, jzlx represents the difference between different strains, delta represents the cycle time, x 1 And x 2 Weight values respectively representing the actual growth rate Sjsd and the optimal growth rate Zjsd;
wherein x is 0.45.ltoreq.x 1 ≤0.65，0.55≤x 2 Not more than 0.65, wherein x 1 +x 2 And 1.5. Gtoreq.H represents a correction constant.
Preferably, the growth condition value Sztj is obtained by calculation according to the following formula:
wherein Wdz denotes a temperature value, sdz denotes a humidity value, ndz denotes a carbon dioxide concentration value, gzqd denotes an illumination intensity value, and w 1 Weight value, w, representing temperature value Wdz 2 Weight value, w, representing humidity value Sdz 3 Weight value, w, representing carbon dioxide concentration value Ndz 4 A weight value representing the illumination intensity value Gzqd; wherein w is 0.35-w 1 ≤0.55，0.45≤w 2 ≤0.65，0.35≤w 3 ≤0.55，0.35≤w 4 Not more than 0.55, wherein w 1 +w 2 +w 3 +w 4 And 2,K represents a correction constant.
Preferably, the real-time early warning module comprises an abnormality detection unit and an alarm notification unit;
the anomaly detection unit compares the environmental safety index Hjaq with a preset threshold value to obtain a grade early warning strategy scheme:
hjaq is less than or equal to 20, a first-level early warning evaluation is obtained, the environmental safety index is far higher than a threshold standard, high yield and high quality are maintained, and production stability is maintained;
the method comprises the steps of (1) obtaining secondary early warning evaluation, wherein Hjaq is more than or equal to 21 and less than or equal to 40, the environmental safety index is higher than a threshold standard, and the secondary early warning evaluation is in a good state, keeps stable yield and quality, and needs to be improved to improve the production efficiency;
41-60, acquiring three-level early warning evaluation, wherein the environmental safety index is close to a threshold standard, the production is stable, the risk is minimized, the safety is not reduced to an unsafe level, and early warning measures are recommended;
the Hjaq is more than or equal to 61 and less than or equal to 80, four-level early warning evaluation is obtained, the environmental safety index is lower than a threshold standard, the environment is improved rapidly, the safety is ensured not to be further reduced to an unsafe level, and emergency early warning measures are required to be adopted;
81-99, acquiring five-level early warning evaluation, wherein the environmental safety index is seriously lower than a threshold standard, and has serious problems, immediately taking emergency early warning measures to ensure environmental safety, minimizing loss of yield and quality, and then gradually improving, wherein edible fungus planting activities may need to be suspended;
the alarm notification unit is used for conveying information through various notification modes when the potential problem or emergency exists in the edible fungus planting environment, including short messages, emails, mobile phone application program notifications and sound alarms, and sending alarm notifications to related personnel to ensure that the information is timely conveyed to responsible personnel so that the responsible personnel can take appropriate measures to cope with the problem.
Preferably, the remote control module comprises a remote monitoring unit and a remote control unit;
the remote monitoring unit is responsible for remotely monitoring the state and parameters of the edible fungus planting environment, including a real-time monitoring temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, a difference value Jzlx of different fungus species, a disease and pest rate Bchl and a fertilization frequency Sfpl, and monitoring data can be periodically transmitted to a remote server or a cloud platform for remote access and analysis;
the remote control unit is used for remotely controlling parameters of edible fungus planting environments of a grower, including remotely adjusting temperature, humidity and illumination conditions so as to meet the requirements of different growth stages and types, and the remote control is usually connected through the Internet and operated through a computer, a smart phone or other equipment.
Preferably, the data visualization module comprises a visualization unit and a user interface and reporting unit;
the visualization unit is used for collecting data, presenting the data by using charts, graphs and visualization tools, generating a real-time environment parameter trend chart, a history record and an alarm record, and helping a user to know the current state and the change trend of the edible fungus planting environment so that the user can understand the data deeply;
the user interface and reporting unit is used for providing a user-friendly interface, so that a user can easily access and interact information related to data visualization, including a Web interface, a mobile application program and a desktop application program, and the user can view real-time data, a history record, alarm information and a visual chart through the interfaces, and the user interface generates a custom report, so that the user can export and share the data.
Preferably, the automatic control module comprises an execution unit, a control algorithm unit and a user-defined setting unit;
the execution unit is used for monitoring environmental parameters and executing corresponding control operation, is closely related to the sensor module, automatically controls environmental conditions including temperature, humidity and illumination according to feedback of sensor data, and maintains stable growth conditions by using an automatic control heating system, a ventilation system and a lighting system;
the control algorithm unit is used for developing, implementing and optimizing a control algorithm so as to ensure that the environmental parameters are in a set range, and the control algorithm unit formulates a control strategy based on sensor data, preset environmental conditions and user input so as to realize automatic adjustment;
the custom setting unit is used for providing user-friendly custom settings, allowing a user to set required environmental parameters and control strategies, and the user can custom adjust temperature, humidity, illumination and other control parameters through the unit so as to meet the requirements of different stages and types.
(III) beneficial effects
The invention provides an intelligent monitoring and early warning system for edible fungus planting environments, which has the following beneficial effects:
(1) When the system operates, environmental parameter data are collected through a sensor group, key information is extracted through data transmission and real-time processing, and the key information is acquired: unsuitable value Bsyz, a model is built based on the calculated data information to obtain: the growth coefficient Szxs and the growth condition value Sztj are compared with a preset threshold value through an environment safety index Hjaq to detect whether an edible fungus planting environment has abnormal conditions, once the abnormal conditions are identified, the system generates corresponding alarm and early warning notification, the alarm can be sent out and advice can be provided through real-time early warning, a user is allowed to remotely monitor and adjust environment parameters, the environment conditions are adjusted in real time, the system comprises an irrigation, temperature and illumination control system, user-friendly interface display data are provided, the real-time state and historical trend of the environment parameters are intuitively displayed to help the user to understand the data better, and the environment conditions are automatically controlled according to analysis results and user instructions.
(2) Through real-time monitoring and automatic control of environmental parameters, the system can obviously optimize the growth conditions of edible fungi, thereby improving the yield, a planter can obtain more edible fungi products in a shorter time, higher agricultural output is realized, the system is not limited by geographic positions or time any more, a user is allowed to remotely monitor and control the planting environment through mobile phone application or a network interface, and the convenience and the flexibility enable the planter to manage agricultural operation at any time and any place.
(3) In order to help users better understand environmental parameters, the system provides visual interfaces, including charts, graphs and visualization tools, not only displays the state of real-time environmental parameters, but also presents historical trends, so that users can deeply analyze data, and the data visualization helps the users to make management strategies and decisions more intelligently so as to ensure optimal production conditions and maximized production.
(4) According to different grades of strategies, a grower can optimize the production flow according to the actual conditions of the environmental safety indexes, reduce the resource waste and deal with the problems in time so as to improve the production efficiency, can maintain high-efficiency production under excellent or good grades, can improve the conditions so as to improve the efficiency under medium or general grades, and flexibly select and implement the proper strategies so as to help improve the stability, the efficiency and the quality of the production, and simultaneously reduce the potential production risk.
Drawings
FIG. 1 is a block flow diagram of an intelligent monitoring and early warning system for edible fungi planting environment;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Edible fungus planting environment detection early warning is a vital ring in modern agriculture, edible fungus planting industry is widely focused and developed as an important agricultural product production field, however, the growth of edible fungi is very sensitive to environmental conditions including temperature, humidity, illumination, fertilization amount and plant diseases and insect pests, farmers and growers need to monitor and maintain the environmental parameters in an optimal state in order to promote the output of high-yield and high-quality edible fungi, and an intelligent monitoring early warning system is developed in order to meet the requirement, integrates advanced sensing technology and data analysis method, and aims to realize intelligent monitoring and management of edible fungus planting environment.
Although intelligent monitoring and early warning systems of edible fungus planting environments represent a great advance in modern agriculture, conventional edible fungus planting methods still have problems and disadvantages, in the past, farmers often relied on experience and feel to manage planting environments, which may lead to instability of growth condition values and production uncertainty, and secondly, even with sensors and monitoring systems, there are still certain limitations in data processing and analysis, because these systems may lack advanced algorithms to accurately predict problems, and furthermore, conventional systems may not provide the ability to remotely monitor and automatically control, which limits the management flexibility of farmers at different sites and times. Therefore, the development of the intelligent monitoring and early warning system for the edible fungus planting environment is important to solve the existing problems, and a more efficient, sustainable and intelligent edible fungus planting management method is provided to meet the growing market demands.
Example 1
The invention provides an intelligent monitoring and early warning system for edible fungi planting environments, referring to fig. 1, which comprises a sensor module, a data acquisition and processing module, a modeling module, a real-time early warning module, a remote control module, a data visualization module and an automatic control module;
the sensor module is used for collecting environmental parameter data through the sensor group to be used as a first data group;
the data acquisition processing module is used for transmitting acquired data to the modeling module, carrying out real-time data processing and analysis, and extracting key information characteristics to obtain: unsuitable value Bsyz;
the modeling module is used for processing sensor data by using data analysis and a machine learning algorithm, identifying abnormal modes, trends and potential problems, carrying out combined calculation on the first data set and unsuitable value Bryz, and establishing a characteristic model to obtain the characteristic model by calculation: environmental safety index Hjaq;
the environmental safety index Hjaq is obtained by the following formula:
Hjaq＝(Szxs*64％)*α+(Sztj*β)*50％+(Bsyz*χ)+F
wherein Szxs represents a growth factor, sztj represents a growth condition value, bsyz represents an unsuitable value, α represents a weight value of the growth factor Szxs, β represents a weight value of the growth condition value Sztj, χ represents a weight value of the unsuitable value Bsyz, and F represents a correction constant;
wherein alpha is more than or equal to 0.55 and less than or equal to 0.75,0.45, beta is more than or equal to 0.55,0.35 and χ is more than or equal to 0.55, wherein alpha+beta+χ is more than or equal to 1.5;
the unsuitable value Bsyz is calculated by the first data set;
the growth coefficient Szxs is obtained through calculation of the first data set and the unsuitable value Bryz;
the growth condition value Sztj is obtained through calculation of the first data set and the unsuitable value Bsyz;
the real-time early warning module is used for sending out a real-time alarm to inform a user and provide advice or action advice once the system detects a potential problem or abnormal environmental condition;
the remote control module is used for allowing a user to remotely monitor and control environmental parameters and adjust environmental conditions in real time through a mobile phone application or a network interface;
the data visualization module is used for providing a user-friendly data visualization interface so as to intuitively display the real-time state and the historical trend of the environmental parameters and help a user to better understand the data;
the automatic control module is used for automatically controlling environmental parameters according to the data analysis result and the user instruction and automatically adjusting the irrigation, temperature and illumination control system.
In this embodiment, environmental parameter data is collected through a sensor group to form a first data group, and key information is extracted by transmitting and processing data in real time to obtain: unsuitable value Bsyz, based on the first data set and unsuitable value Bsyz information, a modeling is performed to obtain: the growth coefficient Szxs and the growth condition value Sztj are compared with a preset threshold value through an environment safety index Hjaq to detect whether an edible fungus planting environment has abnormal conditions, once the abnormal conditions are identified, the system generates corresponding alarm and early warning notification, the alarm can be sent out and advice can be provided through real-time early warning, a user is allowed to remotely monitor and adjust environment parameters, the environment conditions are adjusted in real time, the system comprises an irrigation, temperature and illumination control system, user-friendly interface display data are provided, the real-time state and historical trend of the environment parameters are intuitively displayed to help the user to understand the data better, and the environment conditions are automatically controlled according to analysis results and user instructions.
Example 2
This embodiment is explained in embodiment 1, please refer to fig. 1, specifically: the sensor module comprises a sensor monitoring unit;
the sensor monitoring unit is used for connecting a sensor group to collect environmental parameter data, and comprises a temperature sensor, a humidity sensor, a carbon dioxide sensor and an illumination sensor, and acquiring a temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, difference values Jzlx of different strains, a disease and pest rate Bchl and a fertilization frequency Sfpl;
the first data set comprises a temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, a difference value Jzlx of different strains, a disease and pest rate Bchl and a fertilization frequency Sfpl.
The data acquisition processing module comprises a real-time data processing unit;
the real-time data processing unit is used for calculating the pest and disease rate Bchl and fertilization frequency Sfpl acquired by the first data set to acquire: unsuitable value Bsyz;
the unsuitable value Bsyz is calculated and obtained by the following formula:
wherein Bchl represents the pest rate,sfpl indicates the fertilization frequency, a 1 Weight value, a, representing pest and disease rate Bchl 2 A weight value representing the fertilization frequency Sfpl;
wherein a is more than or equal to 0.55 1 ≤0.85，0.45≤a 2 Less than or equal to 0.75, wherein a 1 +a 2 ≤1.0。
The modeling module comprises a parameter calculation unit;
the parameter calculation unit is configured to perform modeling, analysis, and training according to the first data set and the inappropriateness value Bsyz information, so as to obtain: growth factor Szxs and growth condition value Sztj.
The growth coefficient Szxs is obtained by calculation according to the following formula:
wherein Sjsd represents the actual growth rate, zjsd represents the optimal growth rate, jzlx represents the difference between different strains, delta represents the cycle time, x 1 And x 2 Weight values respectively representing the actual growth rate Sjsd and the optimal growth rate Zjsd;
wherein x is 0.45.ltoreq.x 1 ≤0.65，0.55≤x 2 Not more than 0.65, wherein x 1 +x 2 And 1.5. Gtoreq.H represents a correction constant.
The growth condition value Sztj is obtained by calculation according to the following formula:
wherein Wdz denotes a temperature value, sdz denotes a humidity value, ndz denotes a carbon dioxide concentration value, gzqd denotes an illumination intensity value, and w 1 Weight value, w, representing temperature value Wdz 2 Weight value, w, representing humidity value Sdz 3 Weight value, w, representing carbon dioxide concentration value Ndz 4 A weight value representing the illumination intensity value Gzqd; wherein w is 0.35-w 1 ≤0.55，0.45≤w 2 ≤0.65，0.35≤w 3 ≤0.55，0.35≤w 4 Not more than 0.55, wherein w 1 +w 2 +w 3 +w 4 And 2,K represents a correction constant.
Example 3
This embodiment is explained in embodiment 1, please refer to fig. 1, specifically: the real-time early warning module comprises an abnormality detection unit and an alarm notification unit;
the anomaly detection unit compares the environmental safety index Hjaq with a preset threshold value to obtain a grade early warning strategy scheme:
the Hjaq is less than or equal to 20, the first-level early warning evaluation is obtained, the environmental safety index is far higher than the threshold value standard, the current environmental condition is maintained, the data are regularly monitored and recorded, the high yield and the high quality are maintained, the production stability is maintained, and the production process is continuously optimized to improve the yield and the quality;
the method comprises the steps of (1) obtaining a secondary early warning evaluation, wherein the environmental safety index is higher than a threshold standard, is in a good state, keeps stable yield and quality, needs to be improved to improve the production efficiency, continuously monitors the environmental conditions, and performs necessary adjustment to ensure that the environmental safety index is continuously kept at a good level, and needs to improve the management of some parameters;
41-60, acquiring three-level early warning evaluation, wherein the environmental safety index is close to a threshold standard, the production is stable, the risk is minimized, the safety is not reduced to an unsafe level, early warning measures are recommended to improve the environmental safety, and the temperature, humidity or illumination conditions may need to be regulated, and the environmental parameters are closely monitored;
the method comprises the steps that Hjaq is more than or equal to 61 and less than or equal to 80, four-level early warning evaluation is obtained, the environmental safety index is lower than a threshold standard, the environment is quickly improved, the environment is ensured not to be further reduced to an unsafe level, emergency measures are taken to improve the environmental conditions, including ventilation increase, temperature reduction and humidity improvement, and meanwhile monitoring and management are enhanced;
81-99, acquiring five-level early warning evaluation, wherein the environmental safety index is seriously lower than a threshold standard, and has serious problems, immediately taking emergency measures to ensure the environmental safety, minimizing the loss of yield and quality, suspending edible fungus planting activity, checking and repairing the environmental problems, resetting the environmental conditions, and gradually resuming production;
the alarm notification unit is used for conveying information through various notification modes when the potential problem or emergency exists in the edible fungus planting environment, including short messages, emails, mobile phone application program notifications and sound alarms, and sending alarm notifications to related personnel to ensure that the information is timely conveyed to responsible personnel so that the responsible personnel can take appropriate measures to cope with the problem.
In this embodiment, by adopting a corresponding policy scheme, the stability, efficiency and quality of production are improved, and meanwhile, the potential production risk is reduced. The grower can flexibly select and implement proper strategies according to the actual condition of the environmental safety index so as to meet the planting targets of the grower and maximally utilize the advantages of the intelligent monitoring and early warning system. The method not only improves the sustainability of the edible fungi planting industry, but also is helpful for optimizing the resource utilization and the economic benefit.
Example 4
This embodiment is explained in embodiment 1, please refer to fig. 1, specifically: the remote control module comprises a remote monitoring unit and a remote control unit;
the remote monitoring unit is responsible for remotely monitoring the state and parameters of the edible fungus planting environment, including a real-time monitoring temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, a difference value Jzlx of different fungus species, a disease and pest rate Bchl and a fertilization frequency Sfpl, and monitoring data can be periodically transmitted to a remote server or a cloud platform for remote access and analysis;
the remote control unit is used for remotely controlling parameters of edible fungus planting environments of a grower, including remotely adjusting temperature, humidity and illumination conditions so as to meet the requirements of different growth stages and types, and the remote control is usually connected through the Internet and operated through a computer, a smart phone or other equipment.
Example 5
Referring to fig. 1, specific details are shown in an intelligent monitoring and early warning system for an edible fungus planting environment: the data visualization module comprises a visualization unit, a user interface and a reporting unit;
the visualization unit is used for collecting data, presenting the data by using charts, graphs and visualization tools, generating a real-time environment parameter trend chart, a history record and an alarm record, and helping a user to know the current state and the change trend of the edible fungus planting environment so that the user can understand the data deeply;
the user interface and reporting unit is used for providing a user-friendly interface, so that a user can easily access and interact information related to data visualization, including a Web interface, a mobile application program and a desktop application program, and the user can view real-time data, a history record, alarm information and a visual chart through the interfaces, and the user interface generates a custom report, so that the user can export and share the data.
The automatic control module comprises an execution unit, a control algorithm unit and a user-defined setting unit;
the execution unit is used for monitoring environmental parameters and executing corresponding control operation, is closely related to the sensor module, automatically controls environmental conditions including temperature, humidity and illumination according to feedback of sensor data, and maintains stable growth conditions by using an automatic control heating system, a ventilation system and a lighting system;
the control algorithm unit is used for developing, implementing and optimizing a control algorithm so as to ensure that the environmental parameters are in a set range, and the control algorithm unit formulates a control strategy based on sensor data, preset environmental conditions and user input so as to realize automatic adjustment;
the custom setting unit is used for providing user-friendly custom settings, allowing a user to set required environmental parameters and control strategies, and the user can custom adjust temperature, humidity, illumination and other control parameters through the unit so as to meet the requirements of different stages and types.
In this embodiment, through real-time monitoring and automatic control of environmental parameters, the system can optimize the growth conditions of edible fungi, improve the yield, help the grower to obtain more edible fungi products in a shorter time, allow the user to monitor and control the planting environment remotely through mobile phone application or network interface, make the grower manage his agricultural operations anytime and anywhere, improve convenience and flexibility, the system provides intuitive interface, help the user to understand the real-time state and historical trend of environmental parameters better, thus make more intelligent management and decision.
Specific examples:
suppose we are growing mushrooms and have installed corresponding sensors to monitor environmental parameters. The following are example parameter values:
pest control Bchl:35%, fertilization frequency Sfpl: 3/per cycle, corresponding weight value: a, a 1 ：0.55，a 2 ：0.65；
Now, we substitute these coefficient values into the formula:
unsuitable value
Actual growth rate Sjsd:0.12/H, optimal growth rate Zjsd:0.15/H, cycle time delta: 3 cycles, difference Jzlx of different strains: 6, correction constant: 5, corresponding weight value: x is x 1 ：0.43，x 2 ：0.52；
Growth factor
Temperature value Wdz:25 degrees celsius, humidity value Sdz:75%, illumination intensity value Gzqd:60, carbon dioxide concentration value Ndz:40, correction constant: 6, corresponding weight value: w (w) 1 ：0.36，w 2 ；0.48，w 3 ：0.51，w 4 ：0.4；
Growth condition value
Correction constant: 3, corresponding weight value: alpha: 0.65, beta: 0.48, χ:5, a step of;
environmental safety index
Hjaq＝(8.13*64％)*0.65+(45.2*0.48)*50％+(3.65*5)+3＝35.43
Thus, in this example, the environment safety index of the mushroom planting environment is about 35.43, the environment safety index is higher than the threshold standard, in good condition, keep stable yield and quality, to be improved to improve the production efficiency, continue to monitor the environment condition, make necessary adjustments to ensure that the environment safety index continues to be kept at good level, need to improve the management of some parameters, the actual coefficient values and threshold standard should be adjusted and determined according to the specific planting condition and experience, this example is only for illustrating the calculation process;
although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. An intelligent monitoring and early warning system for edible fungi planting environment is characterized in that: the system comprises a sensor module, a data acquisition and processing module, a modeling module, a real-time early warning module, a remote control module, a data visualization module and an automatic control module;
the sensor module is used for collecting environmental parameter data through the sensor group to be used as a first data group;
the data acquisition processing module is used for transmitting acquired data to the modeling module, carrying out real-time data processing and analysis, and extracting key information characteristics to obtain: unsuitable value Bsyz;
the modeling module is used for processing sensor data by using data analysis and a machine learning algorithm, identifying abnormal modes, trends and potential problems, carrying out combined calculation on the first data set and unsuitable value Bryz, and establishing a characteristic model to obtain the characteristic model by calculation: environmental safety index Hjaq;
the environmental safety index Hjaq is obtained by the following formula:
Hjaq＝(Szxs*64％)*α+(Sztj*β)*50％+(Bsyz*χ)+F
wherein Szxs represents a growth factor, sztj represents a growth condition value, bsyz represents an unsuitable value, α represents a weight value of the growth factor Szxs, β represents a weight value of the growth condition value Sztj, χ represents a weight value of the unsuitable value Bsyz, and F represents a correction constant;
wherein alpha is more than or equal to 0.55 and less than or equal to 0.75,0.45, beta is more than or equal to 0.55,0.35 and χ is more than or equal to 0.55, wherein alpha+beta+χ is more than or equal to 1.5;
the unsuitable value Bsyz is calculated by the first data set;
the growth coefficient Szxs is obtained through calculation of the first data set and the unsuitable value Bryz;
the growth condition value Sztj is obtained through calculation of the first data set and the unsuitable value Bsyz;
the real-time early warning module is used for sending out a real-time alarm to inform a user and provide advice or action advice once the system detects a potential problem or abnormal environmental condition;
the remote control module is used for allowing a user to remotely monitor and control environmental parameters and adjust environmental conditions in real time through a mobile phone application or a network interface;
the data visualization module is used for providing a user-friendly data visualization interface so as to intuitively display the real-time state and the historical trend of the environmental parameters and help a user to better understand the data;
the automatic control module is used for automatically controlling environmental parameters according to the data analysis result and the user instruction and automatically adjusting the irrigation, temperature and illumination control system.
2. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the sensor module comprises a sensor monitoring unit;
the sensor monitoring unit is used for connecting a sensor group to collect environmental parameter data, and comprises a temperature sensor, a humidity sensor, a carbon dioxide sensor and an illumination sensor, and acquiring a temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, difference values Jzlx of different strains, a disease and pest rate Bchl and a fertilization frequency Sfpl;
the first data set comprises a temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, a difference value Jzlx of different strains, a disease and pest rate Bchl and a fertilization frequency Sfpl.
3. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the data acquisition processing module comprises a real-time data processing unit;
the real-time data processing unit is used for calculating the pest and disease rate Bchl and fertilization frequency Sfpl acquired by the first data set to acquire: unsuitable value Bsyz;
the unsuitable value Bsyz is calculated and obtained by the following formula:
wherein Bchl represents the pest and disease rate, sfpl represents the fertilization frequency, a 1 Weight value, a, representing pest and disease rate Bchl 2 A weight value representing the fertilization frequency Sfpl;
wherein a is more than or equal to 0.55 1 ≤0.85，0.45≤a 2 Less than or equal to 0.75, wherein a 1 +a 2 ≤1.0。
4. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the modeling module comprises a parameter calculation unit;
the parameter calculation unit is configured to perform modeling, analysis, and training according to the first data set and the inappropriateness value Bsyz information, so as to obtain: growth factor Szxs and growth condition value Sztj.
5. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the growth coefficient Szxs is obtained by calculation according to the following formula:
wherein Sjsd represents the actual growth rate, zjsd represents the optimal growth rate, jzlx represents the difference between different strains, delta represents the cycle time, x 1 And x 2 Weight values respectively representing the actual growth rate Sjsd and the optimal growth rate Zjsd;
wherein x is 0.45.ltoreq.x 1 ≤0.65，0.55≤x 2 Not more than 0.65, wherein x 1 +x 2 And 1.5. Gtoreq.H represents a correction constant.
6. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the growth condition value Sztj is obtained by calculation according to the following formula:
wherein Wdz denotes a temperature value, sdz denotes a humidity value, ndz denotes a carbon dioxide concentration value, gzqd denotes an illumination intensity value, and w 1 Weight value, w, representing temperature value Wdz 2 Weight value, w, representing humidity value Sdz 3 Weight value, w, representing carbon dioxide concentration value Ndz 4 A weight value representing the illumination intensity value Gzqd; wherein w is 0.35-w 1 ≤0.55，0.45≤w 2 ≤0.65，0.35≤w 3 ≤0.55，0.35≤w 4 Not more than 0.55, wherein w 1 +w 2 +w 3 +w 4 And 2,K represents a correction constant.
7. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the real-time early warning module comprises an abnormality detection unit and an alarm notification unit;
the anomaly detection unit compares the environmental safety index Hjaq with a preset threshold value to obtain a grade early warning strategy scheme:
hjaq is less than or equal to 20, a first-level early warning evaluation is obtained, the environmental safety index is far higher than a threshold standard, high yield and high quality are maintained, and production stability is maintained;
the method comprises the steps of (1) obtaining secondary early warning evaluation, wherein Hjaq is more than or equal to 21 and less than or equal to 40, the environmental safety index is higher than a threshold standard, and the secondary early warning evaluation is in a good state, keeps stable yield and quality, and needs to be improved to improve the production efficiency;
41-60, acquiring three-level early warning evaluation, wherein the environmental safety index is close to a threshold standard, the production is stable, the risk is minimized, the safety is not reduced to an unsafe level, and early warning measures are recommended;
the Hjaq is more than or equal to 61 and less than or equal to 80, four-level early warning evaluation is obtained, the environmental safety index is lower than a threshold standard, the environment is improved rapidly, the safety is ensured not to be further reduced to an unsafe level, and emergency early warning measures are required to be adopted;
81-99, acquiring five-level early warning evaluation, wherein the environmental safety index is seriously lower than a threshold standard, and has serious problems, immediately taking emergency early warning measures to ensure environmental safety, minimizing loss of yield and quality, and then gradually improving, wherein edible fungus planting activities may need to be suspended;
the alarm notification unit is used for conveying information through various notification modes when the potential problem or emergency exists in the edible fungus planting environment, including short messages, emails, mobile phone application program notifications and sound alarms, and sending alarm notifications to related personnel to ensure that the information is timely conveyed to responsible personnel so that the responsible personnel can take appropriate measures to cope with the problem.
8. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the remote control module comprises a remote monitoring unit and a remote control unit;
the remote monitoring unit is responsible for remotely monitoring the state and parameters of the edible fungus planting environment, including a real-time monitoring temperature value Wdz, a humidity value Sdz, a carbon dioxide concentration value Ndz, an illumination intensity value Gzqd, a difference value Jzlx of different fungus species, a disease and pest rate Bchl and a fertilization frequency Sfpl, and monitoring data can be periodically transmitted to a remote server or a cloud platform for remote access and analysis;
the remote control unit is used for remotely controlling parameters of edible fungus planting environments of a grower, including remotely adjusting temperature, humidity and illumination conditions so as to meet the requirements of different growth stages and types, and the remote control is usually connected through the Internet and operated through a computer, a smart phone or other equipment.
9. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the data visualization module comprises a visualization unit, a user interface and a reporting unit;
the visualization unit is used for collecting data, presenting the data by using charts, graphs and visualization tools, generating a real-time environment parameter trend chart, a history record and an alarm record, and helping a user to know the current state and the change trend of the edible fungus planting environment so that the user can understand the data deeply;
the user interface and reporting unit is used for providing a user-friendly interface, so that a user can easily access and interact information related to data visualization, including a Web interface, a mobile application program and a desktop application program, and the user can view real-time data, a history record, alarm information and a visual chart through the interfaces, and the user interface generates a custom report, so that the user can export and share the data.
10. The intelligent monitoring and early warning system for the edible fungi planting environment according to claim 1, wherein the intelligent monitoring and early warning system is characterized in that: the automatic control module comprises an execution unit, a control algorithm unit and a user-defined setting unit;
the execution unit is used for monitoring environmental parameters and executing corresponding control operation, is closely related to the sensor module, automatically controls environmental conditions including temperature, humidity and illumination according to feedback of sensor data, and maintains stable growth conditions by using an automatic control heating system, a ventilation system and a lighting system;
the control algorithm unit is used for developing, implementing and optimizing a control algorithm so as to ensure that the environmental parameters are in a set range, and the control algorithm unit formulates a control strategy based on sensor data, preset environmental conditions and user input so as to realize automatic adjustment;
the custom setting unit is used for providing user-friendly custom settings, allowing a user to set required environmental parameters and control strategies, and the user can custom adjust temperature, humidity, illumination and other control parameters through the unit so as to meet the requirements of different stages and types.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311195060.9A CN117215351A (en) | 2023-09-16 | 2023-09-16 | Intelligent monitoring and early warning system for edible fungus planting environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311195060.9A CN117215351A (en) | 2023-09-16 | 2023-09-16 | Intelligent monitoring and early warning system for edible fungus planting environment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117215351A true CN117215351A (en) | 2023-12-12 |
Family
ID=89038485
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311195060.9A Pending CN117215351A (en) | 2023-09-16 | 2023-09-16 | Intelligent monitoring and early warning system for edible fungus planting environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117215351A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202886979U (en) * | 2012-11-19 | 2013-04-17 | 吉林农业大学 | Agricultural production remote monitoring and intelligent decision making system based on Internet of Things |
| US20180146626A1 (en) * | 2015-05-26 | 2018-05-31 | Jixiang XU | Intelligent growing management method and intelligent growing device |
| CN209517198U (en) * | 2019-04-15 | 2019-10-18 | 广州鑫博软件技术有限公司 | A kind of wisdom agricultural standardization management system |
| CN113080036A (en) * | 2021-04-29 | 2021-07-09 | 四川省林业科学研究院 | Intelligent irrigation control system |
| CN115127617A (en) * | 2022-07-21 | 2022-09-30 | 湖南博立农业科技发展有限公司 | Intelligent management and control system for edible fungus bionic planting |
| CN115759643A (en) * | 2022-11-17 | 2023-03-07 | 贵州省农村综合经济信息中心 | Blueberry QACCP production management system based on Internet of things |
| CN116543531A (en) * | 2023-07-04 | 2023-08-04 | 湖南泓森智能科技有限公司 | Data center system for centralized management and displaying underground monitoring and control data |
| CN116720752A (en) * | 2023-08-07 | 2023-09-08 | 济宁金虹装配式建筑科技有限公司 | Assembled building quality information supervision system based on big data |
| CN116757510A (en) * | 2023-08-21 | 2023-09-15 | 贵州正易科技有限公司 | Intelligent management system for landscaping engineering project |
-
2023
- 2023-09-16 CN CN202311195060.9A patent/CN117215351A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202886979U (en) * | 2012-11-19 | 2013-04-17 | 吉林农业大学 | Agricultural production remote monitoring and intelligent decision making system based on Internet of Things |
| US20180146626A1 (en) * | 2015-05-26 | 2018-05-31 | Jixiang XU | Intelligent growing management method and intelligent growing device |
| CN209517198U (en) * | 2019-04-15 | 2019-10-18 | 广州鑫博软件技术有限公司 | A kind of wisdom agricultural standardization management system |
| CN113080036A (en) * | 2021-04-29 | 2021-07-09 | 四川省林业科学研究院 | Intelligent irrigation control system |
| CN115127617A (en) * | 2022-07-21 | 2022-09-30 | 湖南博立农业科技发展有限公司 | Intelligent management and control system for edible fungus bionic planting |
| CN115759643A (en) * | 2022-11-17 | 2023-03-07 | 贵州省农村综合经济信息中心 | Blueberry QACCP production management system based on Internet of things |
| CN116543531A (en) * | 2023-07-04 | 2023-08-04 | 湖南泓森智能科技有限公司 | Data center system for centralized management and displaying underground monitoring and control data |
| CN116720752A (en) * | 2023-08-07 | 2023-09-08 | 济宁金虹装配式建筑科技有限公司 | Assembled building quality information supervision system based on big data |
| CN116757510A (en) * | 2023-08-21 | 2023-09-15 | 贵州正易科技有限公司 | Intelligent management system for landscaping engineering project |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106657401B (en) | Crop growth monitoring platform based on Internet of things and monitoring method thereof | |
| CN209517198U (en) | A kind of wisdom agricultural standardization management system | |
| CN108881423A (en) | A kind of wisdom agricultural planting system based on Internet of Things | |
| CN103235579A (en) | Network-based self-adaptive control system for greenhouses of facility agriculture | |
| CN116664332B (en) | Agricultural production monitoring system based on digital twinning | |
| CN112465109A (en) | Green house controlling means based on cloud limit is in coordination | |
| CN105278476A (en) | System and method for monitoring traditional brewing process based on Internet of things | |
| CN103093389A (en) | Agricultural product production management system based on network | |
| CN111953769A (en) | Intelligent integrated system based on LoRa communication protocol | |
| CN115454176A (en) | Wisdom green house ventilation control system based on thing networking | |
| CN109116827B (en) | Solar greenhouse water and fertilizer integrated irrigation control method and device based on Internet of things | |
| CN117215351A (en) | Intelligent monitoring and early warning system for edible fungus planting environment | |
| CN110102075A (en) | The chlorination control method and device that view-based access control model identifies in bromine distillation technique | |
| KR20030018604A (en) | Production management system for agriculture and fisheries | |
| CN117350525A (en) | Crop growth data management decision-making system based on artificial intelligence | |
| CN116739216A (en) | Garden operation management system and method based on Internet of things | |
| KR101668820B1 (en) | Environment control system per cultivation block | |
| CN115425749A (en) | Intelligent monitoring disc and fault early warning system for intelligent power plant | |
| CN113587986A (en) | Self-adaptive and self-adjusting multi-dimensional breeding environment quality evaluation method and system | |
| CN115442405A (en) | Wisdom agricultural production management service system | |
| CN108469776A (en) | A kind of reading intelligent agriculture management system and its working method based on Internet of Things | |
| KR20180127566A (en) | Farm automation system that monitors plant growth environment and controls led lighting | |
| CN113625639A (en) | Agricultural intelligent monitoring system and monitoring method thereof | |
| CN111309082A (en) | Cloud-edge interactive intelligent greenhouse energy utilization control system | |
| WO2020198794A1 (en) | Process monitoring system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |