Cold region black soil quality information monitoring and evaluating system and method

Abstract

The invention discloses a cold region black soil quality information monitoring and evaluating system and method. The system comprises a data acquisition module, a remote communication module, an intelligent control module, a visualization module and an evaluation module, the intelligent control module is connected with the data acquisition module, the remote communication module, the visualization module and the evaluation module, and the data acquisition module, the remote communication module, the visualization module and the evaluation module are connected in sequence. And the remote communication module is connected with the visualization module. According to the invention, the information of the black soil in the cold region can be accurately and reliably monitored and evaluated.

Description

Technical Field

[0001] The present invention specifically relates to a cold-region black soil quality information monitoring and evaluation system and method. Background Art

[0002] As a valuable agricultural resource in China, cold-region black soil is famous for its rich fertility and excellent water retention performance. With the impact of climate change and human reclamation, cold-region black soil is facing a series of problems such as soil quality decline, fertility loss, and water pollution. Black soil restoration is imperative. At present, the imperfect soil monitoring system and the inaccurate soil fertility evaluation system make the restoration of cold-region black soil more difficult. Summary of the invention

[0003] In order to overcome the defects of the prior art, a cold-region black soil quality information monitoring system and a soil fertility evaluation method are provided to solve the above problems.

[0004] A cold-region black soil quality information monitoring and evaluation system comprises a data acquisition module, a remote communication module, an intelligent control module, a visualization module and an evaluation module, wherein the intelligent control module is respectively connected to the data acquisition module, the remote communication module, the visualization module and the evaluation module, the data acquisition module, the remote communication module, the visualization module and the evaluation module are connected in sequence, and the remote communication module is connected to the visualization module.

[0005] Furthermore, the data acquisition module is used to collect basic information of black soil and crop growth environment information. The basic information of black soil collected by the data acquisition module is soil temperature, humidity, nitrogen, phosphorus and potassium, pH value, and conductivity. The crop growth environment information is air temperature and humidity, light intensity, CO2 concentration, wind direction and speed.

[0006] Furthermore, the remote communication module is used to transmit the data information collected by the data collection module to the evaluation module and the visualization module respectively under the coordination of the intelligent control module;

[0007] The visualization module is used to visualize the soil data of the remote communication module and the evaluation module and realize the function of video monitoring;

[0008] The visualization module is divided into remote monitoring system and on-site monitoring system. Remote monitoring includes user data management function, remote data monitoring function, data query function, data evaluation function and file download function; on-site monitoring system includes user login function, data monitoring function, system control function and review and download function.

[0009] Furthermore, the intelligent control module is responsible for coordinating and managing other system modules. The intelligent control module processes the data information collected by the data acquisition module. If a certain indicator data exceeds a preset range, an alarm will be triggered.

[0010] Furthermore, the evaluation module screens the data information transmitted by the data acquisition module and constructs an evaluation system. The evaluation system includes the hierarchical analysis method for index weight analysis, the construction of a hierarchical structure model, and the fuzzy comprehensive evaluation method for membership calculation. The combination of the two constitutes a comprehensive soil index to achieve the evaluation of the quality of cold-region black soil.

[0011] The hierarchical model includes the target layer, the criterion layer and the indicator layer. The evaluation matrix is ​​constructed through the hierarchical model, and the weight values ​​of each layer are calculated through the expert evaluation method. Finally, the consistency test of the weights is carried out.

[0012] The fuzzy comprehensive evaluation method evaluates the level of each factor by combining the expert evaluation method with the membership function to determine the membership of the indicator layer.

[0013] Furthermore, the target layer is the quality evaluation of cold-region black soil, the criterion layer includes site conditions, profile characteristics, physical and chemical properties, soil nutrients, barrier factors, and soil management, and the indicator layer includes slope, terrain location, thickness of the tillage layer, texture, bulk density, pH, organic matter, available phosphorus, available potassium, available zinc, barrier layer type, irrigation capacity, and drainage capacity.

[0014] A method for monitoring and evaluating black soil quality information in cold regions, the evaluation method comprising:

[0015] Step 1: Collect basic information on black soil and crop growth environment, and build an evaluation model by screening data;

[0016] Step 2: The evaluation model is represented by the comprehensive soil index IFI:

[0017] IFI=c1r1+c2r2+c3r3+...+c i r i

[0018] Where c i is the combined weight of black soil quality evaluation factors, r i is the membership degree of black soil quality evaluation factors;

[0019] Step 3: Compare the relative importance of the evaluation factors, construct the cold-region black soil quality judgment matrix, and calculate the eigenvector corresponding to the maximum eigenvalue through the expert evaluation method. The eigenvector is the relative weight value.

[0020] The judgment matrix is:

[0021]

[0022] Then perform a consistency check on the weights

[0023] The consistency test calculation formula is:

[0024]

[0025] Where λ max is the maximum characteristic root, n is the number of elements;

[0026]

[0027] Where B is the judgment matrix, W is the eigenvector of the judgment matrix, (BW) i is the i-th element of the vector BW;

[0028] The larger the CI value, the greater the degree to which the judgment matrix deviates from complete consistency. The smaller the CI value, the better the consistency of the judgment matrix. When CI < 0.1, it is considered that the judgment matrix meets the consistency requirements. When CI ≥ 0.1, it is considered that the judgment matrix is ​​inconsistent, and the matrix is ​​adjusted to meet CI < 0.1.

[0029] Step 32: The membership degree is a method combining expert evaluation method and membership function to evaluate and determine the grade of each factor, and four types of membership functions are selected: upper function, peak function, linear function and conceptual function according to the relationship between evaluation indicators and cultivated land quality.

[0030] The results obtained from step 3.1 and step 3.2 are brought into the comprehensive soil fertility index IFI evaluation model in step 1, and the IFI is sorted. The horizontal axis is the unit sequence number and the vertical axis is the comprehensive soil fertility index. The comprehensive soil fertility index distribution diagram is obtained. According to the mutation point of the slope of the curve, that is, the inflection point, combined with expert experience, the number of soil fertility grades and the critical point for dividing the comprehensive soil fertility index are determined to obtain the arable land soil fertility grade.

[0031] Furthermore, the evaluation factors in step 31 are slope, terrain location, thickness of cultivated layer, texture, bulk density, pH, organic matter, available phosphorus, available potassium, available zinc, barrier layer type, irrigation capacity, and drainage capacity.

[0032] Furthermore, in step 32, a linear function is used for slope, a ring-type function is used for plow layer thickness and organic matter, a conceptual function is used for terrain location, texture, barrier layer type, irrigation capacity and drainage capacity as qualitative indicators, and a peak function is used for bulk density, pH, available phosphorus, available potassium and available zinc.

[0033] Furthermore, the above-mentioned function model is used for evaluation elements whose values ​​are larger and whose corresponding cultivated land quality evaluation is higher. When the values ​​approach the critical value, the quality effect tends to be constant:

[0034]

[0035] Where: yi is the membership degree of the i-th factor, u i is the measured value of the sample, c i is the standard indicator, a i is the coefficient; u t is the lower limit of the indicator;

[0036] The peak-shaped function model is applicable to evaluation elements whose cultivated land quality evaluation becomes higher when the value approaches a specific range;

[0037]

[0038] Where: y i is the membership degree of the i-th factor, u i is the measured value of the sample, c i is the standard indicator, a i is the coefficient; u t1 is the lower limit of the indicator, u t2 is the upper limit of the indicator;

[0039] The linear function model is applicable to evaluation elements whose values ​​are proportional to the quality of cultivated land;

[0040] y i =a i u i +b, where b is the intercept.

[0041] The beneficial effects of the present invention are:

[0042] 1. The data acquisition module is used to collect basic information of black soil and crop growth environment information; the remote communication module is used for remote data transmission of farmland information, control alarm signals and other related data; the intelligent control module is used to realize the acquisition signal conversion and data abnormality alarm functions; the visualization module visualizes the soil data and realizes the video monitoring function; the evaluation module selects evaluation indicators and constructs an evaluation system; the present invention can accurately and reliably monitor and evaluate the information of cold-region black soil.

[0043] 2. A remote and on-site monitoring system was developed by comprehensively using the Internet of Things, remote communication and intelligent control technologies. The system can synchronously monitor key farmland indicator data such as soil temperature and humidity, verifying the real-time nature of data transmission and processing. At the same time, the system has functions such as threshold alarm, data query and evaluation, which can meet the development needs of informatization and intelligentization in my country's cold-region black soil farming areas.

[0044] 3. The present invention selects the hierarchical analysis method for index weight analysis and the fuzzy comprehensive evaluation method for membership calculation to evaluate the quality information of cold-region black soil. According to the planting needs of cold-region crops, the soil evaluation factors suitable for cold-region black soil are selected. According to the selected evaluation indicators as the basic objects, the influencing factors and internal connections are analyzed, and the data information is sorted, counted and analyzed to obtain the black soil evaluation. This method needs to construct a hierarchical model according to the research content, namely the target layer, the criterion layer and the indicator layer. Among them, the target layer is the top layer, which solves the research content problem; the criterion layer is the middle layer, which solves the criteria and sub-criteria involved in the research content; the indicator layer is the bottom layer, which contains the basis of all research contents. Combined with the established hierarchical model, an evaluation matrix is ​​constructed, and the weight values ​​of each layer are calculated by the expert evaluation method. Finally, by calculating the CR (Consistency Ratio) value, the consistency test of the weight is carried out to ensure that the weight value meets the design requirements. The membership is a method that combines expert evaluation and membership function to evaluate and determine the level of each factor. The combination of weight and membership constitutes a comprehensive soil index to achieve the quality evaluation of cold-region black soil. The evaluation system proposed in the present invention is verified based on a large amount of data, which verifies the rationality of the cold-region black soil quality evaluation system, is consistent with the evaluation results of the Ministry of Agriculture, and confirms its effectiveness and applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is the technical roadmap of the present invention; Figure 2 It is the overall structural diagram of the present invention; Figure 3 It is a threshold alarm flow chart of the present invention; Figure 4 is a functional diagram of the monitoring system of the present invention; Figure 5 is the overall ER diagram of the present invention; Figure 6 is an evaluation index system diagram of the present invention; Figure 7 It is a data sampling diagram of the present invention; Figure 8 is the slope membership function curve; Fig. 9 It is the membership function curve of tillage layer thickness; Fig.10 It is the bulk density membership function curve; Fig.11 It is the pH property function curve; Fig.12 It is the function curve of organic matter attribute; Fig.13 It is the effective phosphorus property function curve; Fig.14 It is the function curve of available potassium; Fig.15 It is the effective zinc content function curve. DETAILED DESCRIPTION Specific implementation method one:

[0047] See also Figure 1As shown, a cold-region black soil quality information monitoring and evaluation system described in this embodiment includes a data acquisition module, a remote communication module, an intelligent control module, a visualization module and an evaluation module, wherein the intelligent control module is respectively connected to the data acquisition module, the remote communication module, the visualization module and the evaluation module, the data acquisition module, the remote communication module, the visualization module and the evaluation module are connected in sequence, and the remote communication module is connected to the visualization module.

[0048] The data acquisition module is used to collect basic information of black soil and crop growth environment information. The basic information of black soil collected by the data acquisition module is soil temperature, humidity, nitrogen, phosphorus and potassium, pH value, and conductivity. The crop growth environment information is air temperature and humidity, light intensity, CO2 concentration, wind direction and speed.

[0049] In view of the characteristics of cold-region black soil, the soil comprehensive sensor uses the JXBS-3001-TR soil comprehensive sensor, which has high precision, fast response and stable output; it is less affected by soil salt content and is suitable for black soil; it can be buried in the soil for a long time, is resistant to long-term electrolysis, corrosion, vacuum potting, and is completely waterproof; it is easy to carry and meets the requirements of long-term placement in the soil of the test area and monitoring more data indicators;

[0050] The agricultural all-in-one sensor uses the JXBS-3001-CO2 / TH / GZ-RS agricultural all-in-one sensor. This module includes a light intensity sensor, an air temperature and humidity sensor, and a CO2 concentration sensor, which can simultaneously output CO2 concentration, air temperature, air humidity, and light intensity data.

[0051] The wind direction and wind speed sensors are PR-3000-FXJT-N01 wind direction sensor and PR-3000-FSJT-N01 wind speed sensor. The sensors can timely grasp the wind direction and wind speed information, and timely adjust the crop growth auxiliary process and avoid strong wind disasters; the wind direction sample data is calculated as follows;

[0052] Wind direction calculation:

[0053] (0-7 gears): 0002H (hexadecimal) = 2 ≥ wind direction = southeast wind;

[0054] (0-360°): 005AH (hexadecimal) = 90 ≥ wind direction = southeast wind.

[0055] When the data reads the wind direction value (0-7) as 3, (0-360°) is 135°, as shown in Table 1-1.

[0056]

[0057] Table 1-1

[0058] The corresponding relationship of wind direction is shown in Table 1-2.

[0059]

[0060] Table 1-2

[0061] Remote communication module: used for remote data transmission of farmland information, control alarm signals and other related data;

[0062] Communication process see Figure 2 ,First, the serial port data of the farmland environment collected by the terminal device ,i.e. the sensor, is transmitted from the processor to the 4G DTU module, and the 4G data processed by the 4G DTU is transmitted to the base station, and the data is ,packed in the base station and transmitted to the gateway node via the TCP / UDP protocol and ,processed, and finally sent to the remote server through the Internet.

[0063] The 4G DTU module adopts the USR-G776-GNSS type, which has the function of obtaining the required data and its location information such as longitude and latitude in real time, has high communication reliability, has a 4G signal transmission module, supports RS485 serial port communication, realizes the communication and transmission function between the information acquisition terminal and the remote monitoring software, and can realize the data transmission, communication, monitoring, remote positioning and timely feedback functions between the data acquisition module and the remote server or cloud platform.

[0064] Intelligent control module: responsible for coordinating and managing other system modules; it has functions such as task scheduling and allocation, system monitoring and fault handling, data management and transmission, user interface interaction and security management;

[0065] Data processing: PLC uses its logic operation, sequence control and arithmetic operation instructions to control various types of sensor monitoring and data transmission processing through digital or analog I / O;

[0066] Threshold alarm: Alarm flow chart see Figure 3 ,First, after the system is initialized, the intelligent control module starts to receive and process data through PLC, and transmits the data through the 4G DTU module in the remote communication module after processing. The intelligent control module judges the received data according to the preset data indicators. If the data does not exceed the preset indicators, it will proceed normally. If the data exceeds the preset indicators, the PLC will trigger an alarm and remind the user. The data processed by the PLC will be transmitted to the PC and mobile phone every 10 minutes using the 4G DTU module. Once the system operator intervenes and takes corresponding measures, the alarm will be lifted;

[0067] Router: Router equipment mainly provides network support for monitoring equipment and remote signal receiving data, supports DC12V power supply, has firewall function, and realizes IP filtering and URL filtering;

[0068] Human-computer interaction: The human-computer interaction module is the component in the system responsible for realizing communication and interaction between humans and machines. It has functions such as video display, data display and alarm notification;

[0069] Touch screen: The touch screen realizes the on-site human-computer interaction function, can quickly handle related events in the monitoring center, and adjust feedback collection and monitoring equipment in real time;

[0070] LCD display: LCD display is mainly used for outdoor farmland information monitoring;

[0071] Power supply module: mainly used to convert the electric energy of the input power supply into appropriate voltage, current and frequency, and provide stable and reliable power supply; the power supply module in this embodiment mainly adopts two power supply modes, on-site power supply and remote power supply mode. Since the remote monitoring system needs to operate in the field for a long time, there are problems such as long power supply time of equipment and pollution of farmland by power supply equipment; for this reason, this embodiment selects suitable solar power supply equipment and batteries as system power supply modules to meet all-weather monitoring.

[0072] Visualization module: visualizes soil data and realizes the function of video monitoring; the visualization module is divided into remote monitoring system and on-site monitoring system; remote monitoring includes user data management function, remote data monitoring function, data query function, data evaluation function and file download function; on-site monitoring system includes user login function, data monitoring function, system control function and access download function, see Figure 4 ;

[0073] Remote monitoring function: The remote monitoring interface is responsible for displaying the real-time monitoring data transmitted by the server data acquisition module, including key information such as time, location, environmental conditions of each tillage layer, and soil nutrients. In addition, the interface is equipped with a threshold alarm system, which displays a green signal light under normal circumstances; once the monitoring indicators exceed the normal range, the signal light will turn red and automatically send an alarm message to prompt the user to take timely measures;

[0074] Data query function: You can query and download the relevant information you need in EXCEL format according to your needs. The query content includes four items: nodes, sensors, variables and time, and is displayed in charts;

[0075] Database design: The design of the database system is mainly divided into two parts: conceptual design and logical design of the database. This embodiment adopts the Entity-Relationship (ER) design diagram for design. The functions of the overall ER diagram cover the aspects of user operations around the login / registration management module, data monitoring module, etc. Ordinary users mainly complete their own information verification and related operations. For specific ER diagrams, see Figure 5 .

[0076] Evaluation module: by screening evaluation indicators and building an evaluation system;

[0077] Evaluation indicators: The essence of cold-region black soil evaluation is to evaluate the influence of natural elements such as terrain location and soil nutrients on crop growth and development.

[0078] Principles for selecting evaluation indicators: select factors that have a greater impact on the fertility of cultivated land; select factors that have a long-term and stable impact on crops; select factors with greater variation characteristics within the region; select evaluation factors that are closely related to the size of the evaluation area; mainly use quantitative indicators, combining qualitative and quantitative; based on existing data; evaluation factors must have good operability and practical significance;

[0079] Determination of evaluation indicators: To ensure that the evaluation system is consistent with the characteristics of cold-region black soil crop planting, the embodiment of this article forms 6 criterion layers and 13 indicator layers;

[0080] The index layer specifically includes slope, terrain location, plough layer thickness, texture, bulk density, pH, organic matter, available phosphorus, available potassium, available zinc, barrier layer type, irrigation capacity, and drainage capacity. Figure 6 .

[0081] Slope: The angle between the farmland slope and the horizontal plane. The slope mainly affects the soil erosion and soil moisture of cultivated land. It is numerical and has the dimension of °.

[0082] Where: i is the slope of the evaluation unit, °; h is the vertical height of the slope, cm; l is the horizontal distance of the slope, cm.

[0083] Topographic location: small and medium-sized geomorphic units with specific morphological characteristics and causes. Different topographic locations have different effects on crop yields and are important factors affecting crop growth and yield in cold regions.

[0084] Thickness of cultivated layer: the thickness of the topsoil layer formed by cultivation and maturation. Here it is the thickness of the topsoil layer of cultivated black soil. This layer of soil has a loose texture, good structure, and contains more organic matter. Numerical type, dimension is cm;

[0085] Texture: The combination of mineral particles of different diameters in the plough layer of cold-region black soil, generally divided into four categories: sandy soil, loam, clay and gravelly soil;

[0086] Bulk density: The mass or weight of a unit volume of soil (including soil particles and pores) in the natural compacted state in the field. It reflects the soil structure and compaction state, and is numerical, with the unit of g / cm 3 ;

[0087]

[0088] Where: ρ is soil bulk density, g / cm 3 ; W1 is the weight of the dried ring cutter, g; W2 is the weight of the ring cutter and dried soil, g; V is the volume of the ring cutter, cm 3 ;

[0089] pH is an indicator of the acidity or alkalinity of the soil solution, expressed as a pH value. The soil in Jiamusi City is best in weak acidity. The pH collection method of this embodiment is sensor collection;

[0090] Organic matter: the main core of arable land fertility, numerical type, unit is g / kg;

[0091]

[0092] Where OM is the mass fraction of organic matter, g / kg; M1 is the organic matter content, mg; M is the sample mass, g; c is the concentration of ferrous sulfate standard solution, mol / L; V0 is the volume of ferrous sulfate standard solution consumed in the blank test, mL; V is the volume of ferrous sulfate standard solution consumed in the sample determination, mL; 0.003 is the millimolar mass of 1 / 4 carbon atom, g; 1.724 is the coefficient for converting organic carbon into organic matter; 1.10 is the oxidation correction coefficient; m is the mass of the air-dried sample weighed, g; m dm is the dry matter content of soil sample, %; 1000 is the content converted to per kilogram;

[0093] Available phosphorus: reflects the phosphorus supply capacity index of the tillage layer (0-20cm), numerical type, dimension is mg / kg. The effective phosphorus collection method in this embodiment is sensor collection;

[0094] Available potassium: reflects the potassium supply capacity index of the tillage layer (0-20cm), numerical type, dimension is mg / kg. The available potassium collection method of this embodiment is sensor collection;

[0095] Available zinc: Available zinc is one of the main elements that are indispensable to the investigated crops. It reflects the zinc supply capacity of the tillage layer (0-20 cm). It is numerical and has the unit of mg / kg.

[0096] Obstacle layer type: the type of soil layer that constitutes an obstacle to crop growth. In this embodiment, common types of obstacle layers in the tillage layer include sticky disc layer, white pulp layer, latent layer and other obstacle layer types;

[0097] Irrigation capacity: Through on-site investigation of water source type, location, irrigation method, and irrigation volume, a comprehensive judgment is made on the extent to which irrigation water demand can be met in many years of irrigation;

[0098] Drainage capacity: Through on-site investigation of drainage methods, the status of drainage facilities, etc., a comprehensive assessment is made of the farmland's ability to ensure normal crop growth, promptly remove surface water, and effectively control and lower the groundwater level.

[0099] A hierarchical analysis structure model was established: namely, the target layer, the criterion layer and the indicator layer. Among them, the target layer is the quality evaluation of cold-region black soil; the criterion layer is site conditions, profile properties, physical and chemical properties, soil nutrients, obstacle factors, and soil management; the indicator layer is slope, terrain location, plough layer thickness, texture, bulk density, pH, organic matter, available phosphorus, available potassium, available zinc, barrier layer type, irrigation capacity, and drainage capacity;

[0100] Construct judgment matrix: For the evaluation of cold-region black soil quality, the relative importance of the elements in the criterion layer is compared to construct a judgment matrix; the judgment matrix model is shown in Table 1-3, the value range of the matrix is ​​1 / 9-9, the diagonal is 1, and the diagonal is logarithmic. The importance scale value and meaning of the elements in the judgment matrix are shown in Table 1-4.

[0101]

[0102] Table 1-3

[0103]

[0104]

[0105] Table 1-4 Specific implementation method 2:

[0107] A method for monitoring and evaluating the quality information of black soil in cold regions, comprising:

[0108] Step 1: Collect basic information on black soil and crop growth environment, and build an evaluation model by screening data;

[0109] Step 2: The evaluation model is represented by the comprehensive soil index IFI:

[0110] IFI=c1r1+c2r2+c3r3+...+c i r i

[0111] Where c i is the combined weight of black soil quality evaluation factors, r i is the membership degree of black soil quality evaluation factors;

[0112] Step 3: Compare the relative importance of the evaluation factors, construct the cold-region black soil quality judgment matrix, and calculate the eigenvector corresponding to the maximum eigenvalue through the expert evaluation method. The eigenvector is the relative weight value.

[0113] The judgment matrix is:

[0114]

[0115] Then perform a consistency check on the weights

[0116] The consistency test calculation formula is:

[0117]

[0118] Where λ max is the maximum characteristic root, n is the number of elements;

[0119]

[0120] Where B is the judgment matrix, W is the eigenvector of the judgment matrix, (BW) i is the i-th element of the vector BW;

[0121] The larger the CI value, the greater the degree to which the judgment matrix deviates from complete consistency. The smaller the CI value, the better the consistency of the judgment matrix. When CI < 0.1, it is considered that the judgment matrix meets the consistency requirements. When CI ≥ 0.1, it is considered that the judgment matrix is ​​inconsistent, and the matrix is ​​adjusted to meet CI < 0.1.

[0122] Step 32: The membership degree is a method combining expert evaluation method and membership function to evaluate and determine the grade of each factor. According to the relationship between the evaluation index and the quality of cultivated land, it is divided into four types of membership functions: upper function, peak function, linear function and conceptual function.

[0123] The results obtained from step 3.1 and step 3.2 are brought into the comprehensive soil fertility index IFI evaluation model in step 1, and the IFI is sorted. The horizontal axis is the unit sequence number and the vertical axis is the comprehensive soil fertility index. The comprehensive soil fertility index distribution diagram is obtained. According to the mutation point of the slope of the curve, that is, the inflection point, combined with expert experience, the number of soil fertility grades and the critical point for dividing the comprehensive soil fertility index are determined to obtain the arable land soil fertility grade.

[0124] The evaluation factors in step 31 are slope, terrain location, thickness of cultivated layer, texture, bulk density, pH, organic matter, available phosphorus, available potassium, available zinc, barrier layer type, irrigation capacity, and drainage capacity.

[0125] In step 32, a linear function is used for slope, a ring-type function is used for plow layer thickness and organic matter, a conceptual function is used for terrain location, texture, barrier layer type, irrigation capacity and drainage capacity as qualitative indicators, and a peak function is used for bulk density, pH, available phosphorus, available potassium and available zinc.

[0126] The above-mentioned Jieshang type function model is used for evaluation elements whose values ​​are larger, and the corresponding cultivated land quality evaluation is higher. When the values ​​tend to the critical value, the quality effect tends to be constant:

[0127]

[0128] Where: y i is the membership degree of the i-th factor, u i is the measured value of the sample, c i is the standard indicator, a i is the coefficient; u t is the lower limit of the indicator;

[0129] The peak-shaped function model is applicable to evaluation elements whose cultivated land quality evaluation becomes higher when the value approaches a specific range;

[0130]

[0131] Where: y i is the membership degree of the i-th factor, u i is the measured value of the sample, c i is the standard indicator, a i is the coefficient; u t1 is the lower limit of the indicator, u t2 is the upper limit of the indicator;

[0132] The linear function model is applicable to evaluation elements whose values ​​are proportional to the quality of cultivated land;

[0133] y i =a i u i +b, where: b is the intercept;

[0134] The membership degree of the qualitative indicators is determined. Qualitative indicators are conceptual indicators, which are non-numerical and have a non-linear relationship with the quality of cultivated land, such as terrain location, texture, etc. There is no need to establish a function model for such elements.

[0135] Step 3-Specific content:

[0136] I. Hierarchical Analysis Structure Model

[0137] (1) According to the hierarchical analysis method, the correlation between each indicator is analyzed, and a three-layer hierarchical analysis model is established, including the target layer, the criterion layer and the indicator layer, and the relationship is explained in the form of tables and box diagrams. The hierarchical matrix structure model is shown in Table 2-1.

[0138]

[0139] Table 2-1 Hierarchical matrix structure model

[0140] (2) Constructing the judgment matrix

[0141] For a factor in the previous level, compare the relative importance of the elements in this level with that of the previous level. Refer to Table 2-3 for the importance of the elements and construct an n-order judgment matrix. Set the target level A and the criteria level B1, B2, ..., B n The judgment matrix is ​​constructed as shown in Table 2-2. ij =1 / b ji ; b nn =1; b ij >0(i,j=1,2,…,n).

[0142]

[0143] Table 2-2 Judgment matrix model (3) The importance scale values ​​and meanings of the judgment matrix elements are shown in Table 2-3.

[0144]

[0145] Table 2-3 Calculation of the importance ratio of the AHP evaluation index and its II and weights

[0146] (1) The elements of the criterion layer and target layer indicators are weighted according to the judgment matrix.

[0147] From Table 2-2, we can get the judgment matrix B (taking Table 2-2 as an example)

[0148]

[0149] Normalize each column of the elements of the judgment matrix B

[0150]

[0151] After normalization, the comparison matrix is ​​added row by row

[0152]

[0153] From formula (2-3) we get

[0154]

[0155] Normalize formula (2-4)

[0156]

[0157] Eigenvector

[0158] W=[W1,W2,…,W n ] T (2-6)

[0159] In the formula: W is the eigenvector of the judgment matrix B, which is also the weight of all elements of the B matrix.

[0160] Maximum characteristic root

[0161]

[0162] Where: (BW) i ——The i-th element of the vector BW.

[0163] (2)Consistency test

[0164] The CI test in the consistency index is

[0165]

[0166] In the formula: n is the order of the comparison matrix, that is, the number of elements.

[0167] The consistency ratio CR test and random consistency index RI are shown in Table 2-4.

[0168]

[0169] The larger the CI value, the greater the degree to which the judgment matrix deviates from complete consistency, and the smaller the CI value, the better the consistency of the judgment matrix. When CR < 0.1, the judgment matrix is ​​considered to meet the consistency requirements; when CR ≥ 0.1, the judgment matrix is ​​considered inconsistent, and the matrix is ​​adjusted to meet CR < 0.1.

[0170] In the evaluation of cultivated land, the commonly used index is the Random Consistency Index (RCI). By looking up the corresponding average random consistency index RI (a randomly generated value) in the table and comparing it with the calculated consistency index, the consistency of the evaluation results can be ensured, that is, the internal logical relationship between different evaluation indicators is taken into account when making decisions. When n is 1 or 2, RI is only a formal display; when n>2, the consistency is judged by the ratio of CI to RI CR. The introduction of RI can overcome the disadvantage that the consistency judgment index increases significantly with the increase of n to a certain extent.

[0171]

[0172] Table 2-4 Random consistency index

[0173] (3) Calculation of total hierarchical ranking

[0174] Using the ranking results of all elements at the same level for all levels, proceed from the highest level to the lowest level, and calculate the weights of the importance values ​​of all elements. The calculation of the total ranking weight of the level is shown in Table 2-5.

[0175]

[0176] Table 2-5 Calculation of total ranking weights of levels

[0177] Hierarchical total ranking test:

[0178] After obtaining the weight values ​​of each layer, it is necessary to verify the consistency of the overall hierarchy. The formula for the total hierarchical ranking test is shown in formula (2-10).

[0179]

[0180] Specific content of step 32:

[0181] Constructing evaluation sets and membership functions

[0182] Based on the expert evaluation standard grading and quantitative indicators, the evaluation factors are systematically and scientifically defined as quantitative indicators and graded and comprehensive evaluation is carried out, which has a certain degree of subjectivity.

[0183] (1) Establishing an evaluation level set

[0184] V=(v1,v2,…,v k ) (2-11)

[0185] Where: v k ——The kth level of the evaluation model

[0186] (2) Establishing an evaluation set

[0187] U=(u1,u2,…,u m ) (2-12)

[0188] Where: u m ——The weight of the mth indicator

[0189] (3) Determine the element membership

[0190] The factors were evaluated by combining expert evaluation and membership function. According to fuzzy mathematics theory, the relationship between evaluation index and cultivated land quality was divided into four types of membership functions: upper ring function, peak function, linear function and conceptual function.

[0191] 1) Calculation of quantitative indicator membership

[0192] Quantitativeness can be evaluated by expert scoring method to evaluate the corresponding membership of a group of evaluation units, and the corresponding membership function can be fitted according to the two sets of data to calculate the membership of the evaluation unit.

[0193] ① The upper function model

[0194] This function model is applicable to evaluation elements whose larger values ​​indicate higher corresponding cultivated land quality evaluation. When the values ​​approach the critical value, the quality effect tends to be constant, such as organic matter content.

[0195]

[0196] Where: y i is the membership degree of the i-th factor; u i is the measured value of the sample; c i is the standard indicator; a i is the coefficient; u t The lower limit of the indicator.

[0197] ② Peak function model

[0198] This function model is applicable to evaluation elements whose cultivated land quality evaluation becomes higher when the value approaches a specific range, such as soil pH value.

[0199]

[0200] Where: y i is the membership degree of the i-th factor; u i is the measured value of the sample; c i is the standard indicator; a i is the coefficient; ut1 is the lower limit of the indicator; ut2 is the upper limit of the indicator.

[0201] ③Linear function model

[0202] This function model is suitable for evaluation elements whose values ​​are proportional to the quality of cultivated land, such as slope, irrigation capacity, etc.

[0203] y i =a i u i +b (2-15)

[0204] Where: b is the intercept.

[0205] 2) Determination of qualitative indicator membership

[0206] Qualitative indicators are conceptual indicators that are non-numerical and have a non-linear relationship with the quality of cultivated land, such as terrain location and texture. There is no need to establish a functional model for such elements.

[0207] 3) Establish a membership matrix, divide the m element levels into k levels according to formula (2-11), calculate the membership of qualitative indicators and quantitative indicators, and incorporate them into the membership matrix.

[0208]

[0209] Where: r mkis the conversion index of the kth level of the mth index; R m is the single factor fuzzy evaluation relative to the mth index.

[0210] According to the characteristics of cold-region black soil, an evaluation system model was constructed based on the analytic hierarchy process and fuzzy comprehensive evaluation method to provide data support for the planting of cold-region black soil crops. Secondly, a weight analysis of the cold-region black soil in the experimental area was carried out based on the constructed evaluation system model to determine the weight index. Finally, a theoretical basis for soil data experiments was provided. Specific implementation method three:

[0212] Determine the evaluation unit

[0213] In this example, the study area is Jiamusi Academy of Agricultural Sciences, Huanan Experimental Area and other places as the evaluation area, forming 5 evaluation units, and the single-block cultivated land data manual collection mode uses the snake sampling method to collect data. Figure 7 .

[0214] Classification of evaluation levels

[0215] According to the evaluation index standard of black soil quality and health grade in Jiamusi City, the cultivated land grade is divided into five grades, as shown in Table 3-1.

[0216]

[0217] Table 3-1 Classification of cultivated land

[0218] Constructing the membership function

[0219] Constructing the membership degree of evaluation index

[0220] 1) Slope (linear function), refer to Figure 8 And Table 3-2

[0221]

[0222] Table 3-2 Expert evaluation of slope membership 2) Terrain location (conceptual type), see Table 3-3

[0223]

[0224] Table 3-3 Expert evaluation of terrain location membership 3) Thickness of cultivated layer (upper type function), see Fig. 9 and Table 3-4,

[0225]

[0226] Table 3-4 Expert evaluation of tillage layer thickness membership 4) Texture (conceptual type), see Table 3-5,

[0227]

[0228] Table 3-5 Expert evaluation of texture membership 5) Bulk density (peak shape function), see Fig.10 And Table 3-6,

[0229]

[0230] Table 3-6 Expert evaluation of bulk density membership

[0231] 6) pH (peak shape function), see Fig.11 And Table 3-7

[0232]

[0233] Table 3-7 pH membership expert evaluation 7) Organic matter (prevention type function), see Fig.12 and Table 3-8,

[0234]

[0235] Table 3-8 Expert evaluation of organic matter membership 8) Available phosphorus (peak function), see Fig.13 And Table 3-9,

[0236]

[0237] Table 3-9 Expert evaluation of available phosphorus membership 9) Available potassium (peak function), see Table 3-10,

[0238]

[0239] Table 3-10 Expert evaluation of the membership of available potassium 10) Available zinc (peak function), see Table 3-11,

[0240]

[0241] Table 3-11 Expert evaluation of effective zinc membership 11) Barrier layer type (conceptual type), see Table 3-12,

[0242]

[0243] Table 3-12 Expert evaluation of barrier layer type membership 12) Irrigation capacity (conceptual type), see Table 3-13,

[0244]

[0245] Table 3-13 Expert evaluation of irrigation capacity membership 13) Drainage capacity (conceptual type), see Table 3-14,

[0246]

[0247] Table 3-14 Expert evaluation of drainage capacity membership

[0248] Data collection and evaluation

[0249] Evaluation data collection and processing

[0250] (1) Data Collection

[0251] The pH, available phosphorus and quick-acting potassium values ​​were obtained, and monitoring and data extraction were carried out according to the cold-region black soil quality information monitoring system designed in the previous article. The node layout method was the serpentine layout method, and a sensor was pre-buried at each node in the test area at 20cm, 40cm and 60cm respectively.

[0252] Manual data collection: Soil bulk density, slope, etc. require manual data collection. The drying method is used to measure the bulk density of black soil.

[0253] (2) Statistics

[0254]

[0255] Table 3-15 Evaluation Unit Data Collection Table (3) Expert Scoring to Construct Judgment Matrix

[0256]

[0257]

[0258] Table 3-17 Criteria layer B3 judgment matrix

[0259]

[0260] Table 3-18 Criteria layer B4 judgment matrix

[0261]

[0262] Table 3-19 Weight calculation of criterion layer B6 judgment matrix (4)

[0263]

[0264]

[0265] Table 3-20 Total Hierarchy Analysis Weight Table

[0266] (5)Consistency test

[0267] The total hierarchical ranking meets the consistency requirements, and the indicator weight is the degree of impact on black soil fertility.

[0268] (6) Data conversion

[0269] According to the membership function formula and expert scores, the membership evaluation table is shown in Table 3-21.

[0270]

[0271] Table 3-21 Index membership evaluation table

[0272] According to Table 3-21, we can get:

[0273] The slope membership matrix is

[0274] R C1 =[1 0 0 0 0] (3-1)

[0275] The terrain location membership matrix is:

[0276] R C2 =[1 0 0 0 0] (3-2)

[0277] The membership matrix of tillage layer thickness is:

[0278] R C3 =[1 0 0 0 0] (3-3)

[0279] The texture membership matrix is

[0280] R C4 =[0.4 0.6 0 0 0] (3-4)

[0281] The bulk density membership matrix is

[0282] R C5 =[0.8 0.2 0 0 0] (3-5)

[0283] The pH membership matrix is

[0284] R C6 =[0.6 0.4 0 0 0] (3-6)

[0285] The organic matter membership matrix is

[0286] R C7 =[0 0.8 0.2 0 0] (3-7)

[0287] The effective phosphorus membership matrix is

[0288] R C8 =[0.6 0.2 0 0.2 0] (3-8)

[0289] The membership matrix of available potassium is

[0290] R C9=[1 0 0 0 0] (3-9)

[0291] The effective zinc membership matrix is

[0292] R C10 =[0.8 0.2 0 0 0] (3-10)

[0293] The obstacle layer type membership matrix is:

[0294] R C11 =[0 0.4 0.6 0 0] (3-11)

[0295] The irrigation capacity membership matrix is:

[0296] R C12 =[1 0 0 0 0] (3-12)

[0297] The drainage capacity membership matrix is:

[0298] R C13 =[1 0 0 0 0] (3-13)

[0299] Calculation of soil comprehensive index and data evaluation

[0300] The comprehensive soil index IFI indicates:

[0301] IFI=c1r1+c2r2+c3r3+...+c i r i =0.8287

[0302] Calculation shows that the result of the study area of ​​Jiamusi City is first-class land (0.8287), which is consistent with the standard of the Ministry of Agriculture.

Claims

1. A cold-region black soil quality information monitoring and evaluation system, characterized by: It includes a data acquisition module, a remote communication module, an intelligent control module, a visualization module and an evaluation module. The intelligent control module is connected to the data acquisition module, the remote communication module, the visualization module and the evaluation module respectively. The data acquisition module, the remote communication module, the visualization module and the evaluation module are connected in sequence. The remote communication module is connected to the visualization module.

2. The system according to claim 1, characterized in that: The data acquisition module is used to collect basic information of black soil and crop growth environment information. The basic information of black soil collected by the data acquisition module is soil temperature, humidity, nitrogen, phosphorus and potassium, pH value, and conductivity. The crop growth environment information is air temperature and humidity, light intensity, CO2 concentration, wind direction and speed.

3. The system according to claim 1, characterized in that: The remote communication module is used to transmit the data information collected by the data collection module to the evaluation module and the visualization module respectively under the coordination of the intelligent control module; The visualization module is used to visualize the soil data of the remote communication module and the evaluation module and realize the function of video monitoring; The visualization module is divided into remote monitoring system and on-site monitoring system. Remote monitoring includes user data management function, remote data monitoring function, data query function, data evaluation function and file download function; on-site monitoring system includes user login function, data monitoring function, system control function and review and download function.

4. The system according to claim 1, characterized in that: The intelligent control module is responsible for coordinating and managing other system modules. The intelligent control module processes the data information collected by the data acquisition module. If a certain indicator data exceeds the preset range, an alarm will be triggered.

5. The system according to claim 1, characterized in that: The evaluation module filters the data information transmitted by the data acquisition module and constructs an evaluation system. The evaluation system includes the hierarchical analysis method for index weight analysis, the construction of a hierarchical structure model, and the fuzzy comprehensive evaluation method for membership calculation. The combination of the two constitutes a comprehensive soil index to achieve the evaluation of the quality of cold-region black soil. The hierarchical model includes the target layer, the criterion layer and the indicator layer. The evaluation matrix is ​​constructed through the hierarchical model, and the weight values ​​of each layer are calculated through the expert evaluation method. Finally, the consistency test of the weights is carried out. The fuzzy comprehensive evaluation method evaluates the level of each factor by combining the expert evaluation method with the membership function to determine the membership of the indicator layer.

6. The system according to claim 1, characterized in that The target layer is the quality evaluation of cold-region black soil. The criterion layer includes site conditions, profile characteristics, physical and chemical properties, soil nutrients, barrier factors, and soil management. The indicator layer includes slope, terrain location, thickness of the tillage layer, texture, bulk density, pH, organic matter, available phosphorus, available potassium, available zinc, barrier layer type, irrigation capacity, and drainage capacity.

7. A method for monitoring and evaluating the quality information of black soil in cold regions, characterized in that: The evaluation method comprises: Step 1: Collect basic information on black soil and crop growth environment, and build an evaluation model by screening data; Step 2: The evaluation model is represented by the comprehensive soil index IFI: IFI=c1r1+c2r2+c3r3+...+c i r i Where ci is the combined weight of black soil quality evaluation factors, r i is the membership degree of black soil quality evaluation factors; Step 3: Compare the relative importance of the evaluation factors, construct the cold-region black soil quality judgment matrix, and calculate the eigenvector corresponding to the maximum eigenvalue through the expert evaluation method. The eigenvector is the relative weight value. The judgment matrix is: Then perform a consistency check on the weights The consistency test calculation formula is: Where λ max is the maximum characteristic root, n is the number of elements; Where B is the judgment matrix, W is the eigenvector of the judgment matrix, (BW) i is the i-th element of the vector BW; The larger the CI value, the greater the degree to which the judgment matrix deviates from complete consistency. The smaller the CI value, the better the consistency of the judgment matrix. When CI < 0.1, it is considered that the judgment matrix meets the consistency requirements. When CI ≥ 0.1, it is considered that the judgment matrix is ​​inconsistent, and the matrix is ​​adjusted to meet CI < 0.

1. Step 32: The membership degree is a method combining expert evaluation method and membership function to evaluate and determine the grade of each factor, and four types of membership functions are selected: upper function, peak function, linear function and conceptual function according to the relationship between evaluation indicators and cultivated land quality. The results obtained from step 3.1 and step 3.2 are brought into the comprehensive soil fertility index IFI evaluation model in step 1, and the IFI is sorted. The horizontal axis is the unit sequence number and the vertical axis is the comprehensive soil fertility index. The comprehensive soil fertility index distribution diagram is obtained. According to the mutation point of the slope of the curve, that is, the inflection point, combined with expert experience, the number of soil fertility grades and the critical point for dividing the comprehensive soil fertility index are determined to obtain the arable land soil fertility grade.

8. The method according to claim 7, characterized in that The evaluation factors in step 31 are slope, terrain location, thickness of cultivated layer, texture, bulk density, pH, organic matter, available phosphorus, available potassium, available zinc, barrier layer type, irrigation capacity, and drainage capacity.

9. The method according to claim 7, characterized in that: In step 32, a linear function is used for slope, a ring-type function is used for plow layer thickness and organic matter, a conceptual function is used for terrain location, texture, barrier layer type, irrigation capacity and drainage capacity as qualitative indicators, and a peak function is used for bulk density, pH, available phosphorus, available potassium and available zinc.

10. The method according to claim 7, characterized in that The above-mentioned Jieshang type function model is used for evaluation elements whose values ​​are larger, and the corresponding cultivated land quality evaluation is higher. When the values ​​tend to the critical value, the quality effect tends to be constant: Where: y i is the membership degree of the i-th factor, u i is the measured value of the sample, c i is the standard indicator, a i is the coefficient; u t is the lower limit of the indicator; The peak-shaped function model is applicable to evaluation elements whose cultivated land quality evaluation becomes higher when the value approaches a specific range; Where: y i is the membership degree of the i-th factor, u i is the measured value of the sample, c i is the standard indicator, a i is the coefficient; u t1 is the lower limit of the indicator, u t2 is the upper limit of the indicator; The linear function model is applicable to evaluation elements whose values ​​are proportional to the quality of cultivated land; y i =a i u i +b, where b is the intercept.

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