An intelligent monitoring and control system for agricultural ecology based on the Internet of Things

Abstract

The present invention discloses an agricultural ecological intelligent monitoring and control system based on the Internet of Things, which belongs to the field of monitoring technology and includes an image acquisition module for collecting image information of crops in an agricultural ecological engineering park; a data acquisition module for collecting harmful substance content information of soil in the agricultural ecological engineering park; a processing platform for analyzing and processing the acquired harmful substance content information and image information to determine whether the ecological condition in the agricultural ecological engineering park is abnormal; and a control and management module for generating corresponding control levels to control and manage the agricultural ecological engineering park when the ecological condition is abnormal. The present invention performs a comprehensive analysis based on the acquired image information and harmful substance content information to obtain a potential risk value of the monitoring area, and discovers potential ecological problems in the monitoring area based on the potential risk value status, thereby timely regulating and controlling the upcoming ecological abnormality to ensure the economic benefits of the agricultural ecological engineering park.

Description

Technical Field

[0001] The present invention belongs to the field of monitoring technology, and in particular relates to an agricultural ecological intelligent monitoring and control system based on the Internet of Things. Background Art

[0002] With the continuous development of society, people pay more and more attention to the protection of agricultural ecology in agricultural production to ensure the sustainable development of agricultural production and improve the efficiency and quality of agricultural production. Therefore, more and more agricultural ecological engineering parks have emerged, which achieve mutual promotion of agricultural development and ecological protection by optimizing resource allocation, adjusting industrial structure and developing new business formats.

[0003] In the existing agricultural ecological monitoring, generally multiple monitoring points are set up, and the ecological status of the monitoring area is judged according to the data obtained by the monitoring points. However, since the multiple data obtained by the monitoring points are independent, it is not accurate to judge the monitoring area only based on some independent data. Moreover, when the ecological status of the monitoring area is detected to be abnormal, its ecological status has already had problems. In this way, it is impossible to judge the potential abnormal problems, thereby affecting the economic benefits of the agricultural ecological engineering park. Summary of the invention

[0004] The purpose of the present invention is to provide an agricultural ecological intelligent monitoring and control system based on the Internet of Things to solve the problems faced by the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An agricultural ecological intelligent monitoring and control system based on the Internet of Things, the system comprising:

[0007] An image acquisition module, which is used to collect image information of crops in the agricultural ecological engineering park;

[0008] A data collection module, which is used to collect information on harmful substance content in the soil of the agricultural ecological engineering park;

[0009] A processing platform, the processing platform is used to receive the acquired image information and harmful substance content information, and analyze and process the acquired harmful substance content information and image information to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal;

[0010] A control and management module generates a corresponding control level to control and manage the agricultural ecological engineering park when the ecological situation is abnormal.

[0011] Furthermore, the method by which the processing platform determines whether the ecological conditions in the agricultural ecological engineering park are abnormal is:

[0012] The agricultural ecological engineering park is divided into multiple monitoring areas. A monitoring point is set in each monitoring area. The harmful substance content information in the soil collected at each monitoring point is obtained daily. The formula Calculate the soil quality index SQ;

[0013] The obtained soil quality index SQ is compared with the standard soil quality threshold index SQ preset in the system. bz To compare:

[0014] When SQ>SQ bz When , the ecological status of the monitoring area is judged to be abnormal;

[0015] Among them, C j is the content of the jth harmful substance in the monitoring area, a j is the weight coefficient of the jth harmful substance.

[0016] Furthermore, the method for the processing platform to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal also includes:

[0017] When it is determined that the ecological conditions in the monitoring area are normal, the soil quality index variation curve SQ(t) is obtained every monitoring period Δt;

[0018] By formula The potential risk value K is obtained;

[0019] The obtained potential risk value K is compared with the standard potential risk threshold K preset in the system. bz To compare:

[0020] When K>K bz When , the ecological status of the monitoring area is judged to be abnormal;

[0021] in, as well as is the preset proportional coefficient of the system, Gmt is the crop growth status value of the monitoring area, ΔSQ is the soil quality reference value, ΔGmt is the crop growth status reference value, SQ 0 (t) is the time-varying curve of the standard soil quality index preset by the system, Δt = t 2 -t 1 , and t 1 is the start time of the monitoring cycle, t 2 The end time of the monitoring period.

[0022] Furthermore, the method for obtaining the crop growth status value Gmt is:

[0023] Obtain image information collected daily in the monitoring area, perform denoising, enhancement, and correction on the obtained image information to obtain a processed image;

[0024] The processed image is further processed using image processing technology to obtain the color feature value P of the monitored area. colour And the growth characteristic value P growth ;

[0025] Thus, through the formula P = w 1 *P colour +w 2 *P growth Obtain the daily characteristic status value P of the crops in the monitoring area;

[0026] According to the daily characteristic status value P, the characteristic status value change curve P(t) during the monitoring period is obtained;

[0027] So through the formula Obtain the crop growth status value Gmt;

[0028] Among them, P 0 (t) is the time-varying curve of the standard characteristic status value preset by the system, w 1 and w 2 is the proportionality coefficient.

[0029] Furthermore, the color feature value P colour And the growth characteristic value P growth The method to obtain it is:

[0030] The greenness characteristic value of the crops in the image is obtained through RGB technology, and the obtained greenness characteristic value is input into the color feature library trained by the neural network model to obtain the current color characteristic value P colour ;

[0031] The length feature value of the crop in the image is obtained through the edge detection algorithm, and the obtained length feature value is input into the growth feature library trained by the neural network model to obtain the current growth feature value P growth .

[0032] Furthermore, the processing platform also analyzes and evaluates the ecological status of the entire agricultural ecological engineering park according to the risk value of each monitoring area. The analysis and evaluation method is:

[0033] Obtain the risk value K obtained from each monitoring area in the agricultural ecological engineering park i ;

[0034] By formula The volatility coefficient V st ;

[0035] When V st When >1, the ecological status of the agricultural ecological engineering park is judged to be abnormal;

[0036] Where n is the total number of monitoring areas, The risk value K>K in all monitoring areas bz The number of monitoring areas, V bz The fluctuation threshold preset for the system.

[0037] Furthermore, the regulation level includes a primary regulation level, a secondary regulation level and a tertiary regulation level, and the priority of the primary regulation level is higher than the secondary regulation level, and the priority of the secondary regulation level is higher than the tertiary regulation level;

[0038] When V st >V bz When , a first-level regulation level is generated;

[0039] When SQ>SQ bz When , a secondary regulation level is generated;

[0040] When K>K bz When the three-level control level is generated.

[0041] Beneficial effects of the present invention:

[0042] The present invention obtains the content information of multiple harmful substances in the soil in the detection area, and then conducts a comprehensive analysis to determine whether the soil condition is abnormal, and then determines whether the ecological condition is abnormal. It can combine multiple data for comprehensive analysis to make the judgment result more accurate.

[0043] The present invention conducts a comprehensive analysis based on the acquired image information and harmful substance content information to obtain the potential risk value of the monitored area, and discovers the potential ecological problems in the monitored area based on the potential risk value status, so as to timely regulate and control the impending ecological abnormal conditions, reduce the occurrence of abnormal problems, and ensure the economic benefits of the agricultural ecological engineering park.

[0044] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0046] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In one embodiment, Figure 1 As shown, an agricultural ecological intelligent monitoring and control system based on the Internet of Things is disclosed, and the monitoring and control system includes:

[0049] An image acquisition module, which is used to collect image information of crops in the agricultural ecological engineering park;

[0050] Data collection module, the data collection module is used to collect information on harmful substance content in the soil of the agricultural ecological engineering park;

[0051] The processing platform is used to receive the acquired image information and harmful substance content information, and analyze and process the acquired harmful substance content information and image information to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal;

[0052] The regulation and management module generates corresponding regulation levels to regulate and manage the agricultural ecological engineering park when the ecological conditions are abnormal.

[0053] Through the above technical scheme, the present application obtains the content information of multiple harmful substances in the soil in the detection area, and then conducts a comprehensive analysis to determine whether the soil condition is abnormal, and then determines whether the ecological condition is abnormal. It can combine multiple data for comprehensive analysis to make the judgment result more accurate. At the same time, it can also conduct a comprehensive analysis based on the acquired image information and harmful substance content information to obtain the potential risk value of the monitoring area, and discover potential problems in the monitoring area based on the potential risk value status, so as to timely regulate and control the impending ecological abnormal conditions, reduce the occurrence of abnormal problems, and ensure the economic benefits of the agricultural ecological engineering park.

[0054] As an embodiment of the present invention, the method for the processing platform to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal is:

[0055] The agricultural ecological engineering park is divided into multiple monitoring areas. A monitoring point is set in each monitoring area. The harmful substance content information in the soil collected at each monitoring point is obtained daily. The formula Calculate the soil quality index SQ;

[0056] The obtained soil quality index SQ is compared with the standard soil quality threshold index SQ preset in the system. bzTo compare:

[0057] When SQ>SQ bz When , the ecological status of the monitoring area is judged to be abnormal;

[0058] Among them, C j is the content of the jth harmful substance in the monitoring area, a j is the weight coefficient of the jth harmful substance.

[0059] Through the above technical solution, this embodiment provides a method for the processing platform to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal. First, the agricultural ecological engineering park is divided into multiple monitoring areas, each monitoring area is set with a monitoring point, and then the content of harmful substances in the soil collected at each monitoring point is obtained daily, such as the content of lead, arsenic, mercury, chromium, cadmium, pesticide residues, etc., and then the formula is used to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal. Calculate the soil quality index SQ, where C j is the content of the jth harmful substance in the monitoring area, a j is the weight coefficient of the jth harmful substance. The weight coefficient can be set artificially according to the impact of various harmful substances in the local soil on crops. In this way, all the harmful substance content information obtained will be comprehensively analyzed to more accurately obtain the soil quality of the monitored area. The obtained soil quality index SQ is compared with the standard soil quality threshold index SQ preset in the system. bz For comparison, the standard soil quality threshold index SQ preset in the system bz It can be formulated based on empirical data. When SQ>SQ bz When the soil contains more harmful substances, the ecological condition is poor, and the ecological condition of the monitored area is abnormal. In this way, the data of multiple harmful substances in the soil can be combined for comprehensive analysis, so as to accurately obtain the soil quality of the monitored area, which can make the judgment of the ecological condition more accurate.

[0060] As an embodiment of the present invention, the method for the processing platform to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal also includes:

[0061] When it is determined that the ecological conditions in the monitoring area are normal, the soil quality index variation curve SQ(t) is obtained every monitoring period Δt;

[0062] By formula The potential risk value K is obtained;

[0063] The obtained potential risk value K is compared with the standard potential risk threshold K preset in the system. bz To compare:

[0064] When K>K bz When , the ecological status of the monitoring area is judged to be abnormal;

[0065] in, as well as is the preset proportional coefficient of the system, Gmt is the crop growth status value of the monitoring area, ΔSQ is the soil quality reference value, ΔGmt is the crop growth status reference value, SQ 0 (t) is the time-varying curve of the standard soil quality index preset by the system, Δt = t 2 -t 1 , and t 1 is the start time of the monitoring cycle, t 2 The end time of the monitoring period.

[0066] Through the above technical solution, this embodiment provides a method for the processing platform to determine whether there is a potential abnormality in the ecological status of the agricultural ecological engineering park. Specifically, when it is determined that the ecological status of the monitoring area is not abnormal, the monitoring period Δt is set manually according to the actual situation at intervals. For example, it can be determined according to each fertilization cycle, so as to obtain the soil quality index over time curve SQ(t). Through the formula The potential risk value K is obtained, the formula It can express the difference between the soil quality status obtained within the preset period and the standard soil quality status. The larger the difference, the worse the soil quality. The formula Gmt represents the crop growth status value of the monitoring area within the preset period. Finally, the formula Based on the comprehensive analysis of soil quality changes and crop growth changes, the potential risk value is obtained. It can be seen from the formula that when The larger the value of , or the larger the value of Gmt, that is, the larger the potential risk value K, the greater the potential possibility of ecological anomalies in the monitoring area. Therefore, the obtained potential risk value K is compared with the standard potential risk threshold K preset in the system. bz Compare, when K>K bz When the soil quality and crop growth changes are obtained, a comprehensive analysis can be conducted to analyze and judge the potential ecological problems in the monitoring area, so as to timely regulate and manage the potential conditions, ensure soil health and crop safety, and ensure the overall economic benefits of the agricultural ecological engineering park.

[0067] In the above technical solution, the system preset proportional coefficient Soil quality reference value ΔSQ and crop growth reference value ΔGmt, standard potential risk threshold K preset in the system bz All can be determined based on empirical data, and the system preset standard soil quality index curve SQ 0 (t) It can be formulated based on local historical data combined with experimental data, which will not be described in detail here.

[0068] As an implementation mode of the present invention, the method for obtaining the crop growth status value Gmt is:

[0069] Obtain image information collected daily in the monitoring area, perform denoising, enhancement, and correction on the obtained image information to obtain a processed image;

[0070] The processed image is further processed using image processing technology to obtain the color feature value P of the monitored area. colour And the growth characteristic value P growth Specifically, the greenness characteristic value of the crops in the image is obtained through RGB technology, and the obtained greenness characteristic value is input into the color feature library trained by the neural network model to obtain the current color characteristic value P colour , the length feature value of the crop in the image is obtained through the edge detection algorithm, and the obtained length feature value is input into the growth feature library trained by the neural network model to obtain the current growth feature value P growth ;

[0071] Then, by the formula P = w 1 *P colour +w 2 *P growth Obtain the daily characteristic status value P of the crops in the monitoring area;

[0072] According to the daily characteristic status value P, the characteristic status value change curve P(t) during the monitoring period is obtained;

[0073] So through the formula Obtain the crop growth status value Gmt;

[0074] Among them, P 0 (t) is the time-varying curve of the standard characteristic status value preset by the system, w 1 and w 2 is the proportionality coefficient.

[0075] Through the above technical solution, this implementation provides a specific method for obtaining the crop growth status value Gmt. Since the height and color of crops in the image can reflect the growth of crops, generally speaking, under good ecological conditions, the color of crops is bright green and the growth is relatively upright. Therefore, the greenness feature value of crops in the image is obtained by RGB technology, and the obtained greenness feature value is input into the color feature library trained by the neural network model to obtain the current color feature value P colour , the length feature value of the crop in the image is obtained through the edge detection algorithm, and the obtained length feature value is input into the growth feature library trained by the neural network model to obtain the current growth feature value P growth , then through the company P = w 1 *P colour +w 2 *P growth Combined with color feature value P colour And the growth characteristic value P growth Comprehensive analysis is performed to obtain the daily characteristic condition value P of the crops in the monitoring area. Based on the daily characteristic condition value P, the characteristic condition value change curve P(t) during the monitoring period is obtained, and then the formula The crop growth status value Gmt is obtained by analyzing and comparing the change of the acquired characteristic status value with the change of the standard characteristic status value preset by the system. It can be seen from the formula that the larger the difference between the change of the acquired characteristic status value and the change of the standard characteristic status value preset by the system, that is, The larger the value, the worse the growth of crops, and the worse the ecological condition of the area. In this way, the growth of crops in the detection area can be judged based on the acquired image information, so as to facilitate the subsequent judgment of the ecological condition.

[0076] In the above technical solution, the system preset standard characteristic condition value changes with time curve P 0 (t) can be formulated based on local historical data combined with experimental data, and the proportionality coefficient w 1 and w 2 It can be obtained based on empirical data and will not be described in detail here.

[0077] As an embodiment of the present invention, the processing platform also analyzes and evaluates the ecological status of the entire agricultural ecological engineering park according to the risk value of each monitoring area. The analysis and evaluation method is:

[0078] Obtain the risk value K obtained from each monitoring area in the agricultural ecological engineering park i ;

[0079] By formula The volatility coefficient Vst ;

[0080] When V st When >1, the ecological status of the agricultural ecological engineering park is judged to be abnormal;

[0081] Where n is the total number of monitoring areas, The risk value K>K in all monitoring areas bz The number of monitoring areas, V bz The fluctuation threshold preset for the system.

[0082] Through the above technical solution, this implementation provides a method for the processing platform to analyze and evaluate the ecological status of the entire agricultural ecological engineering park. Since the agricultural ecological engineering park is divided into multiple monitoring intervals in this application, and the agricultural ecological engineering park is a whole, if the risk value difference between each monitoring interval varies greatly, it means that the more unstable the ecological status of the entire agricultural ecological engineering park is, the more unfavorable it is for crop growth. Therefore, this embodiment specifically uses the formula The volatility coefficient V st , and then judge according to the size of the fluctuation coefficient. When V st >1, the ecological status of the agricultural ecological engineering park is judged to be abnormal; Formula It can be seen that the stability of the entire agricultural ecological engineering park is that the larger the value, the greater the difference in changes between the monitoring areas, and the formula It indicates the proportion of monitoring intervals with ecological anomalies. The larger its value is, the greater the ecological risk anomaly in the entire agricultural ecological engineering park is. Therefore, when the obtained value is compared with the fluctuation threshold V preset by the system, bz Compare and get the fluctuation coefficient V st , when the risk factor V st When the fluctuation coefficient of the whole agro-ecological engineering park is greater than 1, it indicates that the ecological condition of the whole agro-ecological engineering park is poor, and the ecological condition of the whole agro-ecological engineering park is judged to be abnormal. In this way, the ecological condition of the whole agro-ecological engineering park can be judged to be abnormal according to the fluctuation coefficient of the whole agro-ecological engineering park, so as to timely regulate and manage the agro-ecological engineering park, maintain the balance of the agro-ecological engineering park, and ensure the economic benefits of the agro-ecological engineering park.

[0083] In the above technical solution, the fluctuation threshold V preset by the system bz It can be formulated based on empirical data.

[0084] As an implementation mode of the present invention, the regulation level includes a primary regulation level, a secondary regulation level and a tertiary regulation level, and the priority of the primary regulation level is greater than the secondary regulation level, and the priority of the secondary regulation level is greater than the tertiary regulation level;

[0085] When Vst >V bz When , a first-level regulation level is generated;

[0086] When SQ>SQ bz When , a secondary regulation level is generated;

[0087] When K>K bz When the three-level control level is generated.

[0088] Through the above technical solution, this embodiment provides the priority level when regulating the agricultural ecological engineering park. st >V bz When SQ>SQ bz When K>K, it means that an abnormal ecological situation has occurred in a certain monitoring area in the park, which also needs to be dealt with first, but the urgency is not as high as the abnormal situation in the entire park, so a secondary regulation level is generated; when K>K bz When , it means that there is a potential ecological abnormality problem in a certain monitoring area in the park, and a third-level control level is generated.

[0089] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. An agricultural ecological intelligent monitoring and control system based on the Internet of Things, characterized in that: The system comprises: An image acquisition module, which is used to collect image information of crops in the agricultural ecological engineering park; A data collection module, which is used to collect information on harmful substance content in the soil of the agricultural ecological engineering park; A processing platform, the processing platform is used to receive the acquired image information and harmful substance content information, and analyze and process the acquired harmful substance content information and image information to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal; A control and management module, which generates a corresponding control level to control and manage the agricultural ecological engineering park when the ecological condition is abnormal; The method for the processing platform to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal includes: dividing the agricultural ecological engineering park into a plurality of monitoring areas, and obtaining a soil quality index change curve SQ(t) over time at every monitoring period Δt when it is determined that the ecological conditions in the monitoring areas are not abnormal; By formula The potential risk value K is obtained; The obtained potential risk value K is compared with the standard potential risk threshold K preset in the system. bz To compare: When K>K bz When , the ecological status of the monitoring area is judged to be abnormal; in, as well as is the proportional coefficient preset by the system, Gmt is the crop growth status value of the monitoring area, ΔSQ is the soil quality reference value, ΔGmt is the crop growth status reference value, SQ0(t) is the time-varying curve of the standard soil quality index preset by the system, Δt=t2-t1, and t1 is the start time of the monitoring period, and t2 is the end time of the monitoring period; The method for obtaining the crop growth status value Gmt is: obtaining the image information collected daily in the monitoring area, performing denoising, enhancement and correction processing on the obtained image information to obtain a processed image; The processed image is further processed using image processing technology to obtain the color feature value P of the monitored area. colour And the growth characteristic value P growth ; Thus, through the formula P = w1*P colour +w2*P growth Obtain the daily characteristic status value P of the crops in the monitoring area; According to the daily characteristic status value P, the characteristic status value change curve P(t) during the monitoring period is obtained; So through the formula Obtain the crop growth status value Gmt; Among them, P0(t) is the time-varying curve of the standard characteristic condition value preset by the system, and w1 and w2 are proportional coefficients; The processing platform also analyzes and evaluates the ecological status of the entire agricultural ecological engineering park according to the risk value of each monitoring area. The analysis and evaluation method is: Obtain the risk value K obtained in each monitoring area in the agricultural ecological engineering park i ; By formula The volatility coefficient V st ; When V st >1, the ecological status of the agricultural ecological engineering park is judged to be abnormal; Where n is the total number of monitoring areas, The risk value K>K in all monitoring areas bz The number of monitoring areas, V bz The fluctuation threshold preset for the system.

2. According to claim 1, an agricultural ecological intelligent monitoring and control system based on the Internet of Things is characterized in that: The method for the processing platform to determine whether the ecological conditions in the agricultural ecological engineering park are abnormal is: A monitoring point is set in each monitoring area, and the harmful substance content information in the soil collected at each monitoring point is obtained every day. Calculate the soil quality index SQ; The obtained soil quality index SQ is compared with the standard soil quality threshold index SQ preset in the system. bz To compare: When SQ>SQ bz When , the ecological status of the monitoring area is judged to be abnormal; Among them, C j is the content of the jth harmful substance in the monitoring area, a j is the weight coefficient of the jth harmful substance.

3. The agricultural ecological intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The color feature value P colour And the growth characteristic value P growth The method to obtain it is: The greenness characteristic value of the crops in the image is obtained through RGB technology, and the obtained greenness characteristic value is input into the color feature library trained by the neural network model to obtain the current color characteristic value P colour ; The length feature value of the crop in the image is obtained by edge detection algorithm, and the obtained length feature value is input into the growth feature library trained by the neural network model to obtain the current growth feature value P growth .

4. The agricultural ecological intelligent monitoring and control system based on the Internet of Things according to claim 1 is characterized in that: The regulation level includes the first-level regulation level, the second-level regulation level and the third-level regulation level, and the first-level regulation level has a higher priority than the second-level regulation level, and the second-level regulation level has a higher priority than the third-level regulation level; When V st >V bz When , a first-level regulation level is generated; When SQ>SQ bz When , a secondary regulation level is generated; When K>K bz When the three-level control level is generated.

Images