Intelligent agricultural water-saving irrigation control system based on wireless sensor network

Abstract

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent agricultural water-saving irrigation control system based on a wireless sensor network, which comprises the steps of acquiring crop information, judging an irrigation regulation and control stage, marking land parcels, reading marks, extracting serial numbers of associated acquisition points to divide data transmission priorities, humidity measurement values of different depths are extracted based on the priority list and compared with a crop root layer demand threshold value to identify a water layer needing to be supplemented, root layer types are distinguished according to the soil layer depth, intervals or continuous irrigation periods are set, and the water supply rhythm is arranged; water supply arrangement is read, and instruction contents such as plot numbers, starting and ending time and water supply depth are generated and combined into tasks capable of being continuously called. By monitoring the crop growth and the soil humidity in real time, the irrigation strategy can be accurately judged and dynamically adjusted to meet the water requirement of each stage, the crop root depth and the soil humidity are accurately matched, the irrigation periods of deep and shallow soil layers are optimized, and a flexible scheme is provided to improve the water resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of smart agriculture technology, and in particular to a smart agricultural water-saving irrigation control system based on a wireless sensor network. Background Technology

[0002] The field of smart agriculture technology encompasses the use of modern information technology, automation technology, and control technology, combined with the actual needs of agricultural production, to promote the intelligentization, informatization, and modernization of agricultural production. This technology involves the efficient utilization of agricultural resources, environmental monitoring, crop growth regulation, and automation of agricultural equipment. It particularly focuses on achieving precise management and intelligent control of agriculture through technologies such as wireless sensor networks, the Internet of Things, and big data analysis. The core content of smart agriculture includes environmental data collection based on sensor technology, data monitoring and analysis in the agricultural production process, intelligent irrigation and fertilization control, and remote control and scheduling of agricultural production equipment. This can improve the efficiency of agricultural resource utilization, optimize the agricultural production process, reduce costs, and reduce the waste of natural resources.

[0003] Among them, the smart agricultural water-saving irrigation control system based on wireless sensor networks refers to the use of wireless sensor network technology to monitor various data in the agricultural water environment in real time, such as soil moisture, temperature, and weather information. Combined with the actual needs of agricultural planting, the irrigation system is dynamically adjusted to achieve precise water resource management. By deploying sensor nodes in the farmland to collect environmental data, the data is transmitted to the central processing unit through a wireless network. After data analysis using algorithms, the system automatically controls the start and stop of irrigation equipment and adjusts the water volume. It can also flexibly adjust irrigation strategies according to different crop growth stages and soil characteristics, thereby ensuring the rational allocation and utilization of water resources in agricultural production and avoiding water waste.

[0004] Existing technologies in irrigation management typically rely on fixed standards for water volume regulation, failing to dynamically adapt to changes in crop growth and soil moisture. This leads to insufficient or excessive irrigation at different crop growth stages, and does not adequately consider the different needs of soil layers, resulting in uneven irrigation between shallow and deep soils and an inability to achieve effective water conservation. When responding to climate change and soil moisture fluctuations, existing technologies are slow to adjust irrigation strategies in a timely manner, leading to water waste or restricted crop growth. These shortcomings reduce agricultural productivity and fail to optimize water resource use. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a smart agricultural water-saving irrigation control system based on wireless sensor networks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a smart agricultural water-saving irrigation control system based on a wireless sensor network, the system comprising: The crop stage identification module collects the crop type, sowing time and location of the planting area, calculates the current date and the number of days between sowing and planting, matches them with the crop growth stage value, and makes a judgment based on the water demand regulation stage boundary, marks the plot status, and generates a regulation stage plot marking table. The node priority allocation module reads the land parcel data in the land parcel marking table of the control phase, extracts the collection point number, classifies the collection points according to whether the land parcel is in a control state, and enters the immediate transmission sequence in the control state and the secondary sequence in the non-control state, and generates a node data priority transmission list. The humidity difference extraction module extracts the humidity values ​​at different depths based on the collection point numbers in the node data priority transmission list. It then compares the root distribution depth with the humidity threshold layer by layer. If the humidity is lower than the threshold, it is marked as a state that needs water replenishment, and an identification map of the layer to be irrigated is generated. The water supply rhythm scheduling module reads the soil depth data in the identification map of the layer to be irrigated, distinguishes different root layer types, sets shallow layers to intermittent irrigation to generate short-cycle sequences, and sets deep layers to continuous irrigation to generate long-cycle sequences, integrates them, and generates a phased water-saving water supply schedule.

[0007] As a further embodiment of the present invention, the regulation stage plot marking table includes plot number, crop type, sowing time, stage value, water demand regulation stage boundary, and irrigation regulation stage mark; the node data priority transmission list includes plot number, collection point number, transmission sequence identifier, and processing status; the irrigation layer identification map includes collection point number, humidity measurement value, root layer depth, humidity demand threshold, and water replenishment depth layer; and the staged water-saving water supply arrangement table includes soil layer depth, irrigation cycle type, water supply cycle, and water supply arrangement plan.

[0008] As a further embodiment of the present invention, the crop stage identification module includes an information acquisition submodule, a stage matching submodule, and an irrigation regulation judgment submodule; The information collection submodule acquires information on crop types, sowing times, and spatial locations within the planting area. By comparing the collected sowing times with the current date, it calculates the number of days between the current date and the sowing time. Simultaneously, it records the spatial location information of the planting area to obtain the crop growth days. The stage matching submodule performs interval matching based on the crop growth days and the growth stage value of the crop type to obtain the corresponding growth stage. Combining the characteristics of different crop types in their growth period, it determines which growth stage the current crop is in. Based on the comparison between the growth stage value and the preset water demand regulation stage boundary, it performs interval matching to obtain the crop growth stage. The irrigation regulation judgment submodule determines whether the current crop has entered the irrigation regulation stage based on the boundary between the crop growth stage and the water demand regulation stage. If the current stage falls within the water demand regulation stage range, the corresponding plot is marked as being in a regulation state; otherwise, it remains in a non-regulation state.

[0009] As a further embodiment of the present invention, the node priority allocation module includes a land parcel status reading submodule, a collection point classification submodule, and a priority transmission sorting submodule; The land parcel status reading submodule reads the status data of each land parcel from the land parcel marking table in the regulation stage, extracts the data collection point number associated with the land parcel, identifies the status of each land parcel, determines whether the land parcel is in a regulation state, and obtains the land parcel status information. The data collection point classification submodule classifies the corresponding data collection point numbers according to the land parcel status information. If the land parcel is in a controlled state, the relevant data collection point is marked as immediate transmission; if it is in a non-controlled state, the data collection point is marked as delayed processing, and a data collection point classification list is generated. The priority transmission sorting submodule sorts the collection points according to the collection point classification list according to the immediate transmission priority and includes them in the immediate transmission sequence. At the same time, it includes the collection points that are to be processed later in the secondary transmission sequence and generates a node data priority transmission list.

[0010] As a further embodiment of the present invention, the humidity difference extraction module includes a data acquisition submodule, a threshold comparison submodule, and a marking submodule for the layer to be irrigated; The data acquisition submodule extracts all relevant acquisition point numbers from the node data priority transmission list, obtains the humidity measurement results uploaded by each acquisition point at different depth locations, and records the relationship between the measurement results and depth of each acquisition point according to the distribution depth range of crop roots to obtain the humidity measurement results. The threshold comparison submodule compares the humidity measurement results with the humidity requirement threshold of the crop root layer layer by layer. For each depth layer, it checks whether the measurement result is lower than the corresponding humidity threshold. If it is lower than the threshold, the depth layer is marked as needing water replenishment and a humidity difference result is generated. The layer marking submodule filters out all depth layers below the humidity threshold based on the humidity difference results, marks them as needing water replenishment, and generates an identification map of the layer to be irrigated based on these markings.

[0011] As a further embodiment of the present invention, the water supply rhythm scheduling module includes a depth data reading submodule, a cycle classification submodule, and a water supply plan scheduling submodule; The depth data reading submodule reads the marked soil layer depth data from the identification map of the layer to be irrigated, extracts the soil layer depth of each record, obtains the corresponding root layer type information, performs preliminary screening based on the root layer type, and obtains depth data records. The periodic classification submodule distinguishes between shallow and deep records based on the root layer type information in the depth data records. Shallow records are set to intermittent irrigation to generate short-cycle irrigation sequences, while deep records are set to continuous irrigation to generate long-cycle irrigation sequences, thus obtaining irrigation cycle classification. The water supply planning submodule integrates the short-cycle and long-cycle irrigation sequences in the irrigation cycle classification, splices them sequentially into a unified execution list, and generates a phased water-saving water supply arrangement table based on the list, thus obtaining the water-saving water supply arrangement table.

[0012] As a further aspect of the present invention, the system further includes: The execution command output module reads the plot number, start time, duration and water supply depth data from the phased water-saving water supply schedule, fills them into the instruction format to generate a complete data package, and combines all instructions in the order of plots to form a continuously callable task, generating a set of zoned water-saving irrigation control instructions. The zoned water-saving irrigation control instruction set includes plot number, water supply start time, water supply duration, water supply depth, and execution instruction package.

[0013] As a further embodiment of the present invention, the command output module includes a data reading submodule, an instruction formatting submodule, and an instruction combination submodule; The data reading submodule reads the number, water supply start time, duration and water supply depth of each plot from the phased water-saving water supply schedule table, extracts each record in sequence, organizes the data and generates a plot data list; The instruction formatting submodule, based on the plot number, water supply start time, duration, and water supply depth in the plot data list, fills these data into the corresponding fields according to the predetermined instruction format, generates an instruction data package for each plot, and obtains the instruction data package; The instruction combination submodule arranges and combines all the generated instruction data packets according to the plot number order to form a complete instruction set, organizes these instruction data into a continuous task sequence, and generates a water-saving irrigation control instruction set.

[0014] As a further aspect of the present invention, after generating the identification map of the layer to be irrigated, the humidity status of different depth layers is classified and identified. Depth segments with similar humidity measurement values, differences in humidity demand thresholds, soil types, and root layer distribution characteristics are grouped into the same type of humidity pattern group and assigned corresponding humidity pattern labels. In the subsequent process of generating and issuing irrigation control strategies, differentiated water replenishment strategies are executed based on the humidity pattern labels.

[0015] As a further aspect of the present invention, after obtaining the water-saving water supply schedule, the water supply needs of different plots are classified and identified. Plots with similar water supply depth, continuous irrigation time and soil type are grouped into the same type of water supply mode group, and respectively assigned corresponding irrigation mode labels. In the subsequent irrigation task arrangement and execution process, a differentiated water supply strategy invocation mechanism is executed based on the irrigation mode labels.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by monitoring crop growth stages and soil moisture data in real time, the required irrigation amount for each crop can be accurately determined, and irrigation strategies can be dynamically adjusted to ensure that the water demand at each stage is met in a timely manner. By accurately matching the crop root growth depth with soil moisture, the coarse treatment of soil moisture requirements in traditional methods is avoided, reducing ineffective irrigation. Differentiated treatment of soil depth optimizes the irrigation cycle of shallow and deep soils, providing flexible irrigation solutions, improving the efficiency of water resource use, and avoiding unnecessary water waste. In farmland water resource management, this invention not only improves accuracy but also effectively reduces dependence on water resources and enhances the sustainability of agricultural production. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart illustrating the acquisition process of the crop stage identification module of the present invention. Figure 3 A flowchart illustrating the acquisition process of the node priority allocation module in this invention; Figure 4 This is a flowchart of the humidity difference extraction module of the present invention. Figure 5 This is a flowchart illustrating the acquisition process of the water supply rhythm scheduling module of the present invention. Figure 6 This is a flowchart illustrating the process of obtaining the command output module of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] Please see Figure 1 This invention provides a technical solution: a smart agricultural water-saving irrigation control system based on a wireless sensor network, the system comprising: The crop stage identification module collects the crop type, sowing time and spatial location of the planting area, accumulates the number of days between the current date and the sowing time, matches the accumulated number of days with the growth stage value corresponding to the crop type, judges the current stage value with the water demand regulation stage boundary, marks whether the corresponding plot has entered the irrigation regulation stage, and generates a regulation stage plot marking table. The node priority allocation module reads the land parcel status data from the land parcel marking table during the regulation phase, extracts the data collection point number associated with each land parcel, determines the correspondence between the collection point number and the land parcel status, and if the corresponding land parcel has been marked as being in a regulation state, the collection point is included in the immediate transmission sequence; if it is in a non-regulation state, it is marked as being delayed and enters the secondary transmission sequence, generating a node data priority transmission list. The humidity difference extraction module extracts humidity measurement values ​​at different depths from the corresponding collection points based on the collection point numbers listed in the node data priority transmission list. It calls the crop root distribution depth range value and compares the difference between the measured value and the humidity demand threshold corresponding to the root layer. If the measured value is lower than the threshold, the depth layer is marked as needing water replenishment, and an identification map of the layer to be irrigated is generated. The water supply rhythm scheduling module reads the soil depth data marked in the identification map of the layer to be irrigated, distinguishes and judges the root layer type corresponding to each record, sets shallow records as intermittent irrigation and generates short-cycle sequences, sets deep records as continuous irrigation and generates long-cycle sequences, splices all cycle sequences into a unified execution list, and generates a phased water-saving water supply schedule. The execution command output module reads the plot number, water supply start time, duration and water supply depth data from the phased water-saving water supply schedule table, writes each set of data into the instruction format field in sequence, generates a complete instruction data package according to the plot order, and combines all instruction data into a continuously callable task to generate a zoned water-saving irrigation control instruction set.

[0024] The regulation phase plot marking table includes plot number, crop type, sowing time, phase value, water demand regulation phase boundary, and irrigation regulation phase marker. The node data priority transmission list includes plot number, collection point number, transmission sequence identifier, and processing status. The irrigation layer identification map includes collection point number, humidity measurement value, root layer depth, humidity demand threshold, and water replenishment depth layer. The phased water-saving water supply arrangement table includes soil layer depth, irrigation cycle type, water supply cycle, and water supply arrangement plan. The zoned water-saving irrigation control instruction set includes plot number, water supply start time, water supply duration, water supply depth, and execution instruction package.

[0025] Please see Figure 2 The crop stage identification module includes an information collection submodule, a stage matching submodule, and an irrigation regulation judgment submodule. The information collection submodule acquires information on crop types, sowing times, and spatial locations within the planting area. By comparing the collected sowing times with the current date, it calculates the number of days between the current date and the sowing time. Simultaneously, it records the spatial location information of the planting area to obtain the crop growth days. Periodic access to a comprehensive crop information archive database, which records static and dynamic information of all plots within the farm (as shown in Table 1), is performed. The archive information of plots A-01, B-02, and C-03 is processed sequentially to obtain core parameters such as crop type, spatial location, sowing time stamp, and the baseline temperature required for growth cycle calculation. The information acquisition submodule then retrieves meteorological monitoring data and sets the current processing date as the calculation baseline. Based on this, the effective accumulated temperature (GDD) is calculated for each plot, replacing the simple number of growing days. For plot A-01, from its sowing date to the day before the current processing date, its daily average temperature is calculated and subtracted by 10 degrees Celsius from the baseline temperature. All positive values ​​are then accumulated. For example, on the last day of the calculation cycle, if the highest temperature is 32 degrees Celsius and the lowest temperature is 22 degrees Celsius, then the effective accumulated temperature for that day is... The total effective accumulated temperature (GDD) is calculated to be 485 GDD after accumulation. Similarly, the total effective accumulated temperature calculated for plot B-02 (spring wheat) is 1150 GDD, and the total effective accumulated temperature calculated for plot C-03 (tomato) is 310 GDD. The information acquisition submodule associates and stores these effective accumulated temperature values ​​that accurately reflect the physiological development process of crops with the corresponding plot number, crop type and spatial location information to form a current growth status list containing three records, and obtains the number of days of crop growth.

[0026] Table 1 Basic Information of Land Parcels ; As shown in Table 1, this table lists in detail the basic file information of each plot in the embodiment, which is the data source for all subsequent calculations and judgments.

[0027] The stage matching submodule performs interval matching based on the crop growth days and the growth stage value of the crop type to obtain the corresponding growth stage. Combining the characteristics of different crop types in their growth period, it determines which growth stage the current crop is in. Based on the comparison between the growth stage value and the preset water demand regulation stage boundary, interval matching is performed to obtain the crop growth stage. The system receives growth status records for three plots and performs stage matching on the effective accumulated temperature for each plot. For plot A-01, it retrieves the effective accumulated temperature of 485 GDD and the type of summer maize crop. It then queries a pre-set database of summer maize growth stages based on effective accumulated temperature. The effective accumulated temperature range for the large trumpet stage in this database is [450, 650] GDD. The system determines the appropriate stage based on interval matching criteria. Established, the crop in this plot is determined to be in the large trumpet stage. Next, plot B-02 will be processed, and its effective accumulated temperature of 1150 GDD and spring wheat crop type will be retrieved. The spring wheat growth period database will be queried, and the effective accumulated temperature range for the grain-filling stage is [1100, 1350] GDD. The interval matching criteria will be used to determine the appropriate conditions. Established, the plot was determined to be in the grain-filling stage. For plot C-03, the effective accumulated temperature of 310 GDD and the tomato crop type were retrieved. The tomato growth period database was queried, and the effective accumulated temperature range for the flowering and fruit-setting period was found to be [300, 550] GDD. This was determined by interval matching criteria. Once established, it is determined that the crop is in the flowering and fruiting stage. The module calls the water requirement regulation stage boundaries defined by the effective accumulated temperature for each crop. The boundary for summer maize is [460, 700] GDD, the boundary for spring wheat is [850, 1050] GDD, and the boundary for tomato is [300, 550] GDD. The module performs interval judgment between the current effective accumulated temperature of each crop and its regulation boundary. For A-01, the determination is... Established, for B-02, the determination is... This is not valid. For C-03, the judgment is as follows: Established, it enters the crop growth stage.

[0028] The irrigation regulation judgment submodule makes interval judgments based on the boundary between the crop growth stage and the water demand regulation stage to determine whether the current crop has entered the irrigation regulation stage. If the current stage falls within the water demand regulation stage range, the corresponding plot is marked as being in a regulation state; otherwise, it remains in a non-regulation state. Based on the results of the interval judgment between the effective accumulated temperature and the water demand regulation stage boundary for each crop, the irrigation regulation status of each plot is finely marked. For plot A-01, since its effective accumulated temperature of 485 GDD falls within the water demand regulation stage boundary of [460, 700] GDD, its status field is updated to "Regulated Status". For plot B-02, since its effective accumulated temperature of 1150 GDD does not fall within the water demand regulation stage boundary of [850, 1050] GDD, its status field is marked as "Non-Regulated Status". For plot C-03, since its effective accumulated temperature of 310 GDD... If DD falls within the water demand regulation stage limit of [300, 550]GDD, its status field is also updated to "regulation status". Subsequently, the irrigation regulation judgment submodule writes these marking results into the regulation stage plot marking table one by one. This table contains two core fields: plot number and plot status. The final table contains three records, which clearly indicate that plot A-01 is "regulation status", plot B-02 is "non-regulation status", and plot C-03 is "regulation status". This table provides a direct basis for the subsequent data collection priority allocation and generates the regulation stage plot marking table.

[0029] Please see Figure 3 The node priority allocation module includes a land parcel status reading submodule, a data collection point classification submodule, and a priority transmission sorting submodule. The land parcel status reading submodule reads the status data of each land parcel from the land parcel marking table in the regulation stage, extracts the data collection point number associated with the land parcel, identifies the status of each land parcel, determines whether the land parcel is in a regulation state, and obtains the land parcel status information. The system reads the land parcel marker table for the control phase. This table currently contains three records. The data is read and extracted sequentially. When processing the first record about parcel A-01, the parcel number "A-01" and its status "Control Status" are extracted. Immediately, a pre-defined parcel and data collection point association configuration library is queried based on this number. This library maps the physical deployment of sensor nodes within each parcel in detail, thus revealing the list of data collection point numbers under parcel A-01 as ['N001', 'N002', 'N003']. The system then processes the second record about parcel B-02, extracting "B-..." For "02" and "non-regulated status", the unique collection point corresponding to them is found to be ['N004'] by querying the association database. Finally, the third record about plot C-03 is processed to extract "C-03" and "regulated status", and the list of collection points under its jurisdiction is found to be ['N005', 'N006']. Through this series of precise query and matching operations, all the extracted plot status information and their associated collection point numbers are integrated to construct a temporary, structured internal data set, which provides complete data input for subsequent classification and sorting tasks to obtain plot status information.

[0030] The data collection point classification submodule classifies the corresponding data collection point numbers according to the land parcel status information. If the land parcel is in a control state, the relevant data collection point is marked as immediate transmission; if it is in a non-control state, the data collection point is marked as delayed processing, and a data collection point classification list is generated. The system receives and parses the land parcel status information data structure and introduces a multi-level priority classification mechanism for more refined data transmission management. It not only determines whether a land parcel is in a controlled state but also considers the proximity of soil moisture to a threshold from the previous data collection period. For land parcel B-02, its status is "non-controlled," and its associated collection point 'N004' is assigned the lowest "normal" priority. For land parcel A-01, its status is "controlled," and historical data shows that its lowest moisture value in the previous period was higher than the threshold by 2.1%, not reaching the alarm line. Therefore, its associated collection point 'N004' is assigned the lowest priority. 'N001', 'N002', and 'N003' are assigned the "priority" priority. For plot C-03, its status is also "regulation status", but historical data shows that its lowest humidity value in the previous cycle was only 0.4% higher than the threshold, which has entered the warning range. Therefore, its associated collection points 'N005' and 'N006' are assigned the highest "emergency" priority. After all plots are processed, all collection points and their corresponding multi-level priority tags are summarized to form a complete classification list. This list contains detailed information on all collection points and their transmission priorities, generating a collection point classification list.

[0031] The priority transmission sorting submodule sorts the collection points according to the collection point classification list according to the immediate transmission priority and includes them in the immediate transmission sequence. At the same time, it includes the collection points that are to be processed later in the secondary transmission sequence and generates a priority transmission list of node data. Based on the multi-level priorities (urgent, priority, and routine) defined in the data collection point classification list, all data collection points are strictly sorted to construct a single, ordered data transmission queue. The module initializes an empty transmission list, then iterates through the classification list, inserting all collection points into the list according to their priority labels. The priority sorting rule is: urgent > priority > routine. Within the same priority level, they are sorted in alphanumeric order according to the plot number. Therefore, collection points 'N005' and 'N006' with "urgent" priority are placed at the top of the list, followed by collection points 'N001', 'N002', and 'N003' with "priority" priority, and finally collection point 'N004' with "routine" priority. After the iteration, the final node data priority transmission list is in the following order: ['N005','N006','N001','N002','N003','N004']; The data communication module will strictly follow the order of this list, initiating data requests to the nodes in the list in sequence, thereby ensuring that the most critical data is obtained and processed first, and generating a priority data transmission list for nodes.

[0032] Please see Figure 4 The humidity difference extraction module includes a data acquisition submodule, a threshold comparison submodule, and a marking submodule for the layer to be irrigated. The data acquisition submodule extracts all relevant acquisition point numbers from the node data priority transmission list, obtains the humidity measurement results uploaded by each acquisition point at different depth locations, records the relationship between the measurement results and depth of each acquisition point according to the distribution depth range of crop roots, and obtains the humidity measurement results. Strictly follow the order of the node data priority transmission list: ['N005','N006','N001','N002','N003','N004']; Data requests are initiated to the collection points in the list. Data is requested from N005 (located in plot C-03), which has the highest priority. The uploaded result shows a humidity of 17.9% at a depth of 15 cm and 22.1% at a depth of 30 cm. Data is requested from N006, which shows a humidity of 18.5% at a depth of 15 cm and 21.8% at a depth of 30 cm. Data is requested from N001 (located in plot A-01), which shows a humidity of 24.8% at a depth of 20 cm, 20.9% at a depth of 40 cm, and 28.6% at a depth of 60 cm. After receiving each set of data, the root distribution depth range database of the corresponding crop is immediately called. The root zone range of tomatoes at 310 GDD is [0, 45] cm, and the root zone range of summer corn at 485 GDD is [0, 65] cm. All measured depths are verified to be within the effective root zone range of their respective crops. All successfully acquired effective measurement data are associated and stored with their plot, crop information, and collection timestamp to obtain the humidity measurement result.

[0033] The threshold comparison submodule compares the humidity measurement results with the humidity requirement threshold of the crop root layer layer by layer. For each depth layer, it checks whether the measurement result is lower than the corresponding humidity threshold. If it is lower than the threshold, the depth layer is marked as needing water replenishment and a humidity difference result is generated. For the real-time humidity measurement results at each collection point, a dynamic threshold calculation and comparison was performed considering soil characteristics, crop type, growth stage, and meteorological conditions. The soil physical property parameter table shown in Table 2 was used. For plot C-03 (sandy loam), its field capacity (FC) was 28% and wilting point (WP) was 12%. For plot A-01 (loam), its FC was 35% and WP was 15%. A root activity coefficient related to the crop growth stage was introduced. Tomato flowering and fruit setting period The value is 1.15, during the large trumpet stage of summer maize. The value is 1.10, and the evapotranspiration compensation coefficient is calculated based on the weather conditions of the day. The value is 1.05. The calculation process for the humidity requirement threshold is as follows: ; Where 0.55 is the set lower limit coefficient for suitable water management. For plot C-03, the threshold is calculated as follows:

[0034] ; A comparison was then made, and the depth of N005 was 15 cm: ; 30 cm depth: , For plot A-01, the threshold is calculated as follows: ; A comparison was then made, with N001 having a depth of 20 centimeters: 40 cm depth: ; Because the 60 cm depth layer exceeds the main root zone, a different coefficient is applied, and its threshold is lower. The measured value of 28.6% is higher than this threshold, generating the humidity difference result.

[0035] Table 2 Soil physical properties parameters ; Table 2 lists the key physical parameters corresponding to different soil types, which are directly used in irrigation threshold calculation and irrigation strategy formulation.

[0036] The submodule for marking the layer to be irrigated filters out all depth layers below the humidity threshold based on the humidity difference results, marks them as needing water replenishment, and generates an identification map of the layer to be irrigated based on these markings. After generating the identification map of the layer to be irrigated, the humidity status of different depth layers is classified and labeled. Depth segments with similar humidity measurement values, differences in humidity demand threshold, soil type and root layer distribution characteristics are grouped into the same humidity pattern group and assigned corresponding humidity pattern labels. In the subsequent generation and distribution of irrigation control strategies, differentiated water replenishment strategies are executed based on the humidity pattern labels. All humidity difference calculation results were collected and analyzed, and all depth layers with negative differences were screened out. For plot C-03, the 15 cm and 30 cm depth layers were identified as having significantly insufficient humidity, and these two layers were explicitly marked as "requiring water replenishment." For plot A-01, the 20 cm and 40 cm depth layers were also identified as having insufficient humidity and were also marked as "requiring water replenishment." Based on this marking information, a dynamic identification map of the irrigation-required layers was generated for each plot. In the map, the 20 cm and 40 cm soil layers of plot A-01 were identified. The soil layers at depths of 15 cm and 30 cm in plot A-01 and plot C-03 are highlighted in red to visually demonstrate the water shortage status. The humidity pattern is automatically classified. Plot A-01, which shows shallow to medium water shortage but relatively good moisture in the deeper layers, is classified into the "shallow to medium drought" humidity pattern group and is labeled HM-02. Plot C-03, which shows widespread water shortage in the entire root activity zone, is classified into the "overall drought" humidity pattern group and is labeled HM-03. These labels will be used for the precise application of subsequent differentiated irrigation strategies.

[0037] Please see Figure 5 The water supply rhythm scheduling module includes a deep data reading submodule, a cycle classification submodule, and a water supply plan scheduling submodule. The depth data reading submodule reads the marked soil layer depth data from the identification map of the layer to be irrigated, extracts the soil layer depth of each record, obtains the corresponding root layer type information, performs preliminary screening based on the root layer type, and obtains the depth data record. All marked plots and their required water depths were extracted from the irrigation layer identification map. A list of required water depths [20, 40] was extracted for plot A-01, and a list of required water depths [15, 30] was extracted for plot C-03. Then, data was classified according to a pre-defined database containing definitions of different crop root layer types. In this database, the root layer type of summer maize was divided into shallow (0-30 cm) and deep (over 30 cm), while the root layer type of tomato was divided into shallow (0-25 cm) and deep (25 cm and above). (Above 100 cm), the module processes the depth list of plot A-01 according to this definition, classifying a depth of 20 cm as a shallow record and a depth of 40 cm as a deep record. Then it processes the depth list of plot C-03, classifying a depth of 15 cm as a shallow record and a depth of 30 cm as a deep record. Finally, a structured record set is generated, which marks in detail the plot affiliation, specific depth and corresponding root layer type attribute of each water replenishment point, providing a refined data foundation for subsequent irrigation rhythm formulation, and obtaining depth data records.

[0038] The periodic classification submodule distinguishes between shallow and deep records based on the root layer type information in the depth data records. Shallow records are set to intermittent irrigation to generate short-cycle irrigation sequences, while deep records are set to continuous irrigation to generate long-cycle irrigation sequences, thus obtaining irrigation cycle classification. Based on depth data records and soil physical properties (see Table 2), irrigation quotas were calculated and irrigation patterns were established for each water-replenishing layer. For the 15 cm shallow layer of plot C-03, the soil moisture deficit is 7.31%, corresponding to a soil layer thickness of 150 mm, requiring an additional water volume of [missing information]. The soil (sandy loam) saturation infiltration rate of this plot is 25 mm / hour, indicating that continuous irrigation is suitable, with an irrigation duration of [missing information]. For a 30 cm deep layer, the deficit is 3.11%, corresponding to a soil layer thickness of 150 mm, a water requirement of 4.67 mm, and an irrigation duration of 672 seconds. Combining the two layers, the total irrigation duration is 2252 seconds, generating a long-period sequence. For the 20 cm shallow layer of plot A-01, the deficit is 5.23%, the water requirement is 7.85 mm, and its soil (loam) permeability is only 15 mm / hour. To prevent runoff, the maximum single irrigation duration is set at 1200 seconds. Therefore, the total duration is... The irrigation task of 1 second is divided into two short cycle sequences: irrigate for 1200 seconds, rest for 1800 seconds, and then irrigate for 684 seconds. For a depth of 40 cm, the water deficit is 9.13%, the water requirement is 13.7 mm, and the duration is 3288 seconds. This is set as continuous irrigation, and the irrigation cycle is classified.

[0039] The water supply planning submodule integrates the short-cycle and long-cycle irrigation sequences in the irrigation cycle classification, splices them in sequence into a unified execution list, and generates a phased water-saving water supply arrangement table based on the list. After obtaining the water-saving water supply schedule, the water supply needs of different plots are classified and identified. Plots with similar water supply depth, continuous irrigation time and soil type are grouped into the same type of water supply mode group, and respectively assigned corresponding irrigation mode labels. In the subsequent irrigation task arrangement and execution process, a differentiated water supply strategy call mechanism is implemented based on the irrigation mode labels. The irrigation cycle classification of each plot is integrated, and scheduling optimization is performed based on the total pump flow limit (e.g., set to be unable to meet the maximum water demand of two plots simultaneously). Given that plot C-03 has a priority of "urgent," it is scheduled to execute a continuous irrigation task with a total duration of 2252 seconds, starting in the first available time window, i.e., at 20:00 on the same day. Plot A-01 has a priority of "priority," and its irrigation task is scheduled to begin after the task of plot C-03 ends, i.e., starting at 20:37:32. The water supply planning submodule merges its shallow interval irrigation sequence with its deep continuous irrigation sequence to form a complex comprehensive execution list that includes start and stop actions, with a total duration of [missing information]. The optimized plans are formatted in seconds (based on the shorter total duration of shallow tasks, during which deep water is continuously supplied), generating a phased water-saving water supply schedule containing multiple entries (see Table 3). The table clearly lists the start time, irrigation mode, duration, and other precise parameters for each plot, resulting in the water-saving water supply schedule.

[0040] Table 3. Phased Water Conservation and Supply Arrangement ; Table 3 presents the scheduling test results of the embodiment, and differentiated detailed irrigation tasks were formulated for plots A-01 and C-03 respectively.

[0041] Please see Figure 6 The command output module includes a data reading submodule, an instruction formatting submodule, and an instruction combination submodule. The data reading submodule reads the number, water supply start time, duration and water supply depth of each plot from the phased water-saving water supply schedule table, extracts each record in sequence, organizes the data and generates a plot data list; Access the optimized phased water-saving supply schedule and read it line by line in chronological order of planned execution time. Process the first line of record, extracting the plot number "C-03", the planned water supply start time "20:00:00 today", the duration of 2252 seconds, and a water supply depth list [15,30] containing multiple targets. Combine these data items into a structured data object. Process the second line of record in the same way, extracting the plot number "A-01", the planned water supply start time "20:37:32 today", the duration of 3684 seconds (with its complex start and stop timing information), and the water supply depth list [20,40]. Combine them into a second data object. After all planned tasks in the table have been read, store all generated data objects in a queue in chronological order of their execution time. This queue is the plot data list, which serves as the precise input for subsequent instruction formatting steps, generating the plot data list.

[0042] The instruction formatting submodule, based on the plot number, water supply start time, duration, and water supply depth in the plot data list, fills these data into the corresponding fields according to the predetermined instruction format, generates an instruction data package for each plot, and obtains the instruction data package; Data objects are retrieved from the land parcel data list in queue order for processing. The data object for parcel C-03 is processed by calling a command template that supports simple continuous irrigation. An example of this template is as follows: HEAD:{plot ID}; CMD:IRRIGATE_START;START:{YYYYMMDDHHMMSS}; DURATION: {seconds}; TARGET_DEPTH:{depth}; CHECKSUM:{checksum}”; The module fills the template with the values ​​from the data object, generating the core part of the instruction, for example: "HEAD:C-03;CMD:IRRIGATE_START; START: [Planned start timestamp]; DURATION:2252; The string "TARGET_DEPTH:15,30" is used to perform a CRC16 checksum algorithm, calculating the checksum "7E5D". This forms a complete instruction data packet. The data object for plot A-01 is then processed. Due to its complex start-stop timing, an instruction template supporting segmented tasks is invoked to generate an instruction packet containing multiple sub-commands, for example: The command "HEAD:A-01;CMD:IRRIGATE_SEQ;...[subcommand]..." is used to calculate the checksum for the entire packet, thus obtaining the instruction data packet.

[0043] The instruction combination submodule arranges and combines all generated instruction data packets according to the plot number order to form a complete instruction set, organizes these instruction data into a continuous task sequence, and generates a water-saving irrigation control instruction set. Receive all formatted individual instruction data packets generated in the preceding steps, namely the instructions for plot C-03 and plot A-01. Sort these instruction packets according to the execution time sequence determined in the phased water-saving and water supply schedule, ensuring that the instructions for C-03 come first, followed by those for A-01. Then, sequentially concatenate the sorted instruction data packets using a clear delimiter to distinguish different tasks, such as a newline character \n, to form a continuous, multi-line, structured instruction text. This text fully describes all tasks for this irrigation cycle. An example of its content is as follows: "HEAD:C-03;CMD:IRRIGATE_START; START: [Planned start timestamp]; DURATION:2252; TARGET_DEPTH:15,30;CHECKSUM:7E5D\nHEAD:A-01; The combined text "CMD:IRRIGATE_SEQ;...;CHECKSUM:..." is encapsulated into a task sequence that can be continuously called and parsed by the underlying control unit. It is then prepared to be sent to the communication gateway of the irrigation control device. The gateway will distribute the instruction to the field valve controller of the corresponding plot at a precise time point based on the timestamp and plot ID in the instruction, thereby generating a zoned water-saving irrigation control instruction set.

[0044] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart agricultural water-saving irrigation control system based on a wireless sensor network, characterized in that, The system includes: The crop stage identification module collects the crop type, sowing time and location of the planting area, calculates the current date and the number of days between sowing and planting, matches them with the crop growth stage value, and makes a judgment based on the water demand regulation stage boundary, marks the plot status, and generates a regulation stage plot marking table. The node priority allocation module reads the land parcel data in the land parcel marking table of the control phase, extracts the collection point number, classifies the collection points according to whether the land parcel is in a control state, and enters the immediate transmission sequence in the control state and the secondary sequence in the non-control state, and generates a node data priority transmission list. The humidity difference extraction module extracts the humidity values ​​at different depths based on the collection point numbers in the node data priority transmission list. It then compares the root distribution depth with the humidity threshold layer by layer. If the humidity is lower than the threshold, it is marked as a state that needs water replenishment, and an identification map of the layer to be irrigated is generated. The water supply rhythm scheduling module reads the soil depth data in the identification map of the layer to be irrigated, distinguishes different root layer types, sets shallow layers to intermittent irrigation to generate short-cycle sequences, and sets deep layers to continuous irrigation to generate long-cycle sequences, integrates them, and generates a phased water-saving water supply schedule.

2. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 1, characterized in that: The regulation phase plot marking table includes plot number, crop type, sowing time, phase value, water demand regulation phase boundary, and irrigation regulation phase marker. The node data priority transmission list includes plot number, collection point number, transmission sequence identifier, and processing status. The irrigation layer identification map includes collection point number, humidity measurement value, root layer depth, humidity demand threshold, and water replenishment depth layer. The phased water-saving water supply arrangement table includes soil layer depth, irrigation cycle type, water supply cycle, and water supply arrangement plan.

3. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 1, characterized in that: The crop stage identification module includes an information acquisition submodule, a stage matching submodule, and an irrigation regulation judgment submodule. The information collection submodule acquires information on crop types, sowing times, and spatial locations within the planting area. By comparing the collected sowing times with the current date, it calculates the number of days between the current date and the sowing time. Simultaneously, it records the spatial location information of the planting area to obtain the crop growth days. The stage matching submodule performs interval matching based on the crop growth days and the growth stage value of the crop type to obtain the corresponding growth stage. Combining the characteristics of different crop types in their growth period, it determines which growth stage the current crop is in. Based on the comparison between the growth stage value and the preset water demand regulation stage boundary, it performs interval matching to obtain the crop growth stage. The irrigation regulation judgment submodule determines whether the current crop has entered the irrigation regulation stage based on the boundary between the crop growth stage and the water demand regulation stage. If the current stage falls within the water demand regulation stage range, the corresponding plot is marked as being in a regulation state; otherwise, it remains in a non-regulation state.

4. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 1, characterized in that: The node priority allocation module includes a land parcel status reading submodule, a data collection point classification submodule, and a priority transmission sorting submodule. The land parcel status reading submodule reads the status data of each land parcel from the land parcel marking table in the regulation stage, extracts the data collection point number associated with the land parcel, identifies the status of each land parcel, determines whether the land parcel is in a regulation state, and obtains the land parcel status information. The data collection point classification submodule classifies the corresponding data collection point numbers according to the land parcel status information. If the land parcel is in a controlled state, the relevant data collection point is marked as immediate transmission; if it is in a non-controlled state, the data collection point is marked as delayed processing, and a data collection point classification list is generated. The priority transmission sorting submodule sorts the collection points according to the collection point classification list according to the immediate transmission priority and includes them in the immediate transmission sequence. At the same time, it includes the collection points that are to be processed later in the secondary transmission sequence and generates a node data priority transmission list.

5. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 1, characterized in that: The humidity difference extraction module includes a data acquisition submodule, a threshold comparison submodule, and a marking submodule for the layer to be irrigated. The data acquisition submodule extracts all relevant acquisition point numbers from the node data priority transmission list, obtains the humidity measurement results uploaded by each acquisition point at different depth locations, and records the relationship between the measurement results and depth of each acquisition point according to the distribution depth range of crop roots to obtain the humidity measurement results. The threshold comparison submodule compares the humidity measurement results with the humidity requirement threshold of the crop root layer layer by layer. For each depth layer, it checks whether the measurement result is lower than the corresponding humidity threshold. If it is lower than the threshold, the depth layer is marked as needing water replenishment and a humidity difference result is generated. The layer marking submodule filters out all depth layers below the humidity threshold based on the humidity difference results, marks them as needing water replenishment, and generates an identification map of the layer to be irrigated based on these markings.

6. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 1, characterized in that: The water supply rhythm scheduling module includes a depth data reading submodule, a cycle classification submodule, and a water supply plan scheduling submodule; The depth data reading submodule reads the marked soil layer depth data from the identification map of the layer to be irrigated, extracts the soil layer depth of each record, obtains the corresponding root layer type information, performs preliminary screening based on the root layer type, and obtains depth data records. The periodic classification submodule distinguishes between shallow and deep records based on the root layer type information in the depth data records. Shallow records are set to intermittent irrigation to generate short-cycle irrigation sequences, while deep records are set to continuous irrigation to generate long-cycle irrigation sequences, thus obtaining irrigation cycle classification. The water supply planning submodule integrates the short-cycle and long-cycle irrigation sequences in the irrigation cycle classification, splices them sequentially into a unified execution list, and generates a phased water-saving water supply arrangement table based on the list, thus obtaining the water-saving water supply arrangement table.

7. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 1, characterized in that: The system also includes: The execution command output module reads the plot number, start time, duration and water supply depth data from the phased water-saving water supply schedule, fills them into the instruction format to generate a complete data package, and combines all instructions in the order of plots to form a continuously callable task, generating a set of zoned water-saving irrigation control instructions. The zoned water-saving irrigation control instruction set includes plot number, water supply start time, water supply duration, water supply depth, and execution instruction package.

8. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 7, characterized in that: The command output module includes a data reading submodule, an instruction formatting submodule, and an instruction combination submodule. The data reading submodule reads the number, water supply start time, duration and water supply depth of each plot from the phased water-saving water supply schedule table, extracts each record in sequence, organizes the data and generates a plot data list; The instruction formatting submodule, based on the plot number, water supply start time, duration, and water supply depth in the plot data list, fills these data into the corresponding fields according to the predetermined instruction format, generates an instruction data package for each plot, and obtains the instruction data package; The instruction combination submodule arranges and combines all the generated instruction data packets according to the plot number order to form a complete instruction set, organizes these instruction data into a continuous task sequence, and generates a water-saving irrigation control instruction set.

9. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 1, characterized in that: After generating the identification map of the layer to be irrigated, the humidity status of different depth layers is classified and labeled. Depth segments with similar humidity measurement values, differences in humidity demand threshold, soil type and root layer distribution characteristics are grouped into the same humidity pattern group and assigned corresponding humidity pattern labels. In the subsequent generation and distribution of irrigation control strategies, differentiated water replenishment strategies are executed based on the humidity pattern labels.

10. The smart agricultural water-saving irrigation control system based on a wireless sensor network according to claim 1, characterized in that: After obtaining the water-saving water supply schedule, the water supply needs of different plots are classified and identified. Plots with similar water supply depth, continuous irrigation time and soil type are grouped into the same type of water supply mode group and assigned corresponding irrigation mode labels. In the subsequent irrigation task arrangement and execution process, a differentiated water supply strategy invocation mechanism is implemented based on the irrigation mode labels.

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