A smart management method and system for watering garden plants

Abstract

The present invention discloses an intelligent management method and system for watering garden plants, which is applied to the field of data processing methods for garden management. Environmental collection data is obtained by collecting data in a target garden area. The soil moisture content in the environmental collection data is used as the current state to obtain a predicted amount of soil moisture deficit. A sequence of water demand for plant growth is obtained. The water demand interval of various plants in the current growth stage is obtained, and the total predicted amount of water demand is calculated in combination with the predicted amount of soil moisture deficit to determine whether an early warning mechanism is triggered. If the plant watering log is triggered, the plant watering log is uploaded to a processing center to establish a watering demand model. The environmental collection data and the type of watering plants are input into the watering demand model to obtain the watering demand result. The technical problem of the difficulty in obtaining plant watering plans in garden management is solved to realize intelligent management of garden plant watering.

Description

Technical Field

[0001] The present invention relates to the field of data processing methods for garden greening management, and in particular to an intelligent management method and system for watering garden greening plants. Background Art

[0002] With the development of the Internet of Things, in the process of cultivating garden greening plants, the watering plan is generally only a fixed watering plan generated according to the plant species and the local climate. In the process of realizing the embodiments of the present application, the inventors found that since the watering of garden greening plants is a dynamic process, only relying on a preset watering plan cannot guarantee the healthy growth of garden greening plants. If it is verified by staff, the amount of data is too large and changes in real time, and it is difficult for staff to directly reflect the abnormalities of the data based on some data alone. Therefore, this is a great challenge to the professional quality and work intensity of the staff. Therefore, the intelligent management of garden greening plant watering is also difficult to achieve in the prior art.

[0003] Therefore, the existing method of obtaining plant watering plans in garden greening management is either to generate a rough watering plan based on local conditions, which is generally not changed. Although this method saves labor, it is impossible to customize the watering plan according to the growth conditions of plants in the garden. Therefore, it will still have a bad impact on the growth of garden plants in the long run. If a watering plan suitable for the garden under its jurisdiction is to be generated according to the garden conditions, it is mainly analyzed and calculated manually. Therefore, its garden management personnel, and if they want to obtain a watering plan suitable for the garden under their jurisdiction, it is not only time-consuming and labor-intensive, but also the watering plan should be modified according to the abnormalities in the watering of garden greening plants, and it is difficult for managers to fully learn about the abnormalities in the watering of garden greening plants. Therefore, this also tests the professional quality of its garden management personnel. Summary of the invention

[0004] The present application provides an intelligent management method and system for watering garden plants. By collecting and analyzing data on the garden area to predict the watering plan suitable for the garden in the next stage, the present application solves the technical problems in the prior art of low efficiency in obtaining watering plans for garden plants, low intelligence of the obtained watering plans, and difficulty in realizing intelligent management of watering of garden plants.

[0005] The present application provides an intelligent management method for watering garden plants, the method comprising: collecting data in a target garden area to obtain environmental collection data, the environmental collection data comprising soil moisture content, air temperature and humidity, and light intensity; taking the soil moisture content in the environmental collection data as the current state, and combining the air temperature and humidity and light intensity in the environmental collection data for prediction, to obtain a predicted amount of soil moisture deficit; in the target garden area, traversing to obtain a set of garden plants, and comparing the plant growth cycle to calculate a plant growth water requirement sequence for each plant at each growth stage; obtaining the target garden through the plant growth water requirement sequence and the garden plant set. The water demand range of various plants in the area at the current growth stage is combined with the predicted soil moisture deficit to calculate the predicted total water demand, and determine whether to trigger the early warning mechanism; if the early warning mechanism is triggered, the plant watering log of the target garden area is obtained, and the watering indicators corresponding to the plant watering log include watering amount, watering frequency, watering time, and irrigation method. The plant watering log is uploaded to the processing center, and a watering demand model is established based on the association mapping between each plant watering record in the plant watering log and the historical environment collection data; the environment collection data and the watering plant species are input into the watering demand model to obtain the watering demand result, and the watering demand result includes the early warning level.

[0006] The present application also provides an intelligent management system for watering garden plants, the system comprising: a data acquisition module, for collecting data in a target garden area and acquiring environmental data, the environmental data comprising soil moisture content, air temperature and humidity, and light intensity; a water deficit prediction module, for taking the soil moisture content in the environmental data as the current state, and predicting in combination with the air temperature and humidity and light intensity in the environmental data, to obtain a predicted amount of soil moisture deficit; a demand sequence acquisition module, for traversing a collection of garden plants in the target garden area, and calculating a plant growth water demand sequence for each plant at each growth stage in comparison with the plant growth cycle; a demand interval acquisition module, for obtaining a plant growth water demand sequence, the garden plants, and the water demand interval of the garden plants; A collection is used to obtain the water demand range of various plants in the target garden area at the current growth stage, calculate the total predicted water demand in combination with the predicted soil moisture deficit, and determine whether to trigger the early warning mechanism; a demand model construction module is used to obtain the plant watering log of the target garden area if the early warning mechanism is triggered, and the watering indicators corresponding to the plant watering log include watering amount, watering frequency, watering time, and irrigation method. The plant watering log is uploaded to the processing center, and a watering demand model is established based on the association mapping between each plant watering record in the plant watering log and the historical environment collection data; a demand result acquisition module is used to input the environmental collection data and the watering plant species into the watering demand model to obtain the watering demand result, and the watering demand result includes the early warning level.

[0007] The present application also provides an electronic device, including:

[0008] A memory for storing executable instructions;

[0009] The processor is used to implement an intelligent management method for watering garden plants provided by the present application when executing the executable instructions stored in the memory.

[0010] The present application provides a computer-readable storage medium storing a computer program. When the program is executed by a processor, an intelligent management method for watering landscaping plants provided by the present application is implemented.

[0011] The intelligent management method and system for watering garden plants proposed in this application is proposed to acquire environmental data by collecting data in the target garden area. The soil moisture content in the environmental data is used as the current state to obtain the predicted amount of soil moisture deficit. The water demand sequence for plant growth is obtained. The water demand range of various plants in the current growth stage is obtained, and the total predicted amount of water demand is calculated in combination with the predicted amount of soil moisture deficit to determine whether the early warning mechanism is triggered. If the plant watering log is triggered, the plant watering log is uploaded to the processing center to establish a watering demand model. The environmental data and the types of watered plants are input into the watering demand model to obtain the watering demand results. The automatic acquisition of the watering plan for garden plants is realized, and timely warning of plant water deficit is carried out to assist managers in better management of the garden. The technical problem of the difficulty in obtaining the watering plan for plants in garden management is solved to assist managers in realizing the intelligent management of watering for garden plants.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings of the embodiments of the present disclosure. Obviously, the drawings described below only relate to some embodiments of the present disclosure, and are not intended to limit the present disclosure.

[0014] Figure 1 A schematic diagram of a flow chart of an intelligent management method for watering garden plants provided in an embodiment of the present application;

[0015] Figure 2 A schematic diagram of a flow chart of obtaining a sequence of water demand for plant growth in a smart management method for watering garden plants provided in an embodiment of the present application;

[0016] Figure 3 A schematic diagram of a flow chart of establishing a watering demand model for a smart management method for watering garden plants provided in an embodiment of the present application;

[0017] Figure 4 A schematic diagram of the structure of a system for an intelligent management method for watering garden plants provided in an embodiment of the present application;

[0018] Figure 5 A schematic diagram of the structure of a system electronic device for an intelligent management method for watering garden plants provided in an embodiment of the present invention.

[0019] Explanation of the reference numerals: data acquisition module 11 , water deficit prediction module 12 , demand sequence acquisition module 13 , demand interval acquisition module 14 , demand model construction module 15 , demand result acquisition module 16 , processor 31 , memory 32 , input device 33 , output device 34 . DETAILED DESCRIPTION

[0020] Embodiment 1: In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0022] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0024] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, the modules are illustrative only, and different aspects of the system and method may use different modules.

[0025] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0026] like Figure 1 As shown, the embodiment of the present application provides an intelligent management method for watering garden plants, the method comprising:

[0027] Collect data in the target garden area to obtain environmental data, including soil moisture content, air temperature and humidity, and light intensity;

[0028] Taking the soil moisture content in the environmental collection data as the current state, combined with the air temperature and humidity and light intensity in the environmental collection data, a prediction is made to obtain a predicted amount of soil moisture deficit;

[0029] In the target garden area, a set of garden greening plants is obtained by traversing, and a sequence of plant growth water requirements of each plant at each growth stage is calculated by comparing the plant growth cycle;

[0030] Data collection is performed in the target garden area to obtain environmental collection data, which includes soil moisture content, air temperature and humidity, and light intensity. Subsequently, the soil moisture content in the environmental collection data is used as the current state, and the soil deficit in the current state is predicted in combination with the air temperature and humidity and light intensity in the environmental collection data to obtain the predicted amount of soil moisture deficit. Before the soil moisture deficit prediction is performed, a prediction model database is constructed, and the soil moisture content, air temperature and humidity, and light intensity data at different time nodes are obtained by obtaining the environmental collection data without interference in the historical records, and the soil moisture loss data corresponding to the preset time period are obtained, and the mean of multiple soil moisture loss data under the same conditions is calculated to obtain the corresponding soil moisture deficit prediction amount of the soil moisture content, air temperature and humidity, and light intensity data. The soil moisture deficit prediction amount of different environmental collection data is obtained, and a prediction model database is constructed. Thereby, the soil moisture deficit prediction amount is obtained based on the prediction model database and the environmental collection data, and the soil moisture deficit prediction amount is the moisture deficit loss generated by the soil. Furthermore, within the target garden area, a set of garden greening plants is traversed and obtained, and a plant growth water requirement sequence of each plant at each growth stage is calculated by comparing the plant growth cycle.

[0031] like Figure 2 As shown, the method provided in the embodiment of the present application also includes:

[0032] Deploy image acquisition equipment in the target garden area to identify and locate plant species, and record the collection of garden greening plants in the target garden area;

[0033] Collecting growth cycle information of each plant through a plant database, wherein the growth cycle information includes germination period, growth period, flowering period, fruiting period, and dormancy period;

[0034] The plant growth water requirement sequence of each plant at each growth stage is calculated based on the garden plant set and the growth cycle information according to the plant physiological characteristics and water requirement characteristics at different growth stages.

[0035] An image acquisition device is deployed in the target garden area to identify and locate plant species, and the collection of garden greening plants in the target garden area is recorded. Subsequently, the growth cycle information of each plant is collected through the plant database, and the growth cycle information includes the germination period, the growth period, the flowering period, the fruiting period, and the dormancy period. Each type of plant category and the corresponding growth cycle information are recorded in the plant database. Further, through the garden greening plant collection and the growth cycle information, according to the plant physiological characteristics and water demand characteristics of different growth stages, the plant physiological characteristics are the appearance characteristics of the plant, and the water demand characteristics are the specific growth water requirements, wherein each growth stage contains multiple plant physiological characteristics, each plant physiological characteristic corresponds to a water demand characteristic, and the water demand characteristics of the multiple plant physiological characteristics contained in each growth stage are added and calculated to obtain the plant growth water demand sequence of each plant in each growth stage.

[0036] The method provided in the embodiment of the present application also includes:

[0037] In the target garden area, using an image acquisition device to collect plant image information;

[0038] Analyze the plant image information in real time, identify and classify and record the type of each plant and the location coordinates of each plant;

[0039] By using the type of each plant and the position coordinates of each plant, a plant spatial distribution map corresponding to the target garden area is established to obtain a collection of garden greening plants.

[0040] In the target garden area, an image acquisition device is used to collect plant image information. Subsequently, the plant image information is analyzed in real time according to the collected plant image information, the image information is identified and the type of each plant and the position coordinates of each plant are classified and recorded. Further, a plant spatial distribution map corresponding to the target garden area is established through the type of each plant and the position coordinates of each plant, and a garden greening plant set is obtained. The garden greening plant set contains the plant distribution location and plant distribution category data corresponding to the target garden area.

[0041] The water demand range of various plants in the target garden area at the current growth stage is obtained through the plant growth water demand sequence and the garden greening plant set, and the total predicted water demand is calculated in combination with the predicted soil water deficit, and it is determined whether to trigger the early warning mechanism;

[0042] If the early warning mechanism is triggered, the plant watering log of the target garden area is obtained, and the watering indicators corresponding to the plant watering log include watering amount, watering frequency, watering time, and irrigation method. The plant watering log is uploaded to the processing center, and a watering demand model is established based on the association mapping between each plant watering record in the plant watering log and the historical environmental collection data;

[0043] The environmental collection data and the watering plant species are input into the watering demand model to obtain a watering demand result, wherein the watering demand result includes a warning level.

[0044] Through the plant growth water demand sequence and the garden greening plant set, the water demand interval of various plants in the target garden area at the current growth stage is obtained. The water demand interval is the interval composed of the minimum growth water demand and the maximum growth water demand of various plants in the current growth stage. The soil water deficit prediction amount is compensated on the basis of the water demand interval to obtain the compensated water demand interval. Then, the total predicted water demand is calculated based on the compensated water demand interval minus the soil moisture content in the current environmental collection data. The total predicted water demand is the actual water demand interval. And it is judged whether to trigger the early warning mechanism, that is, whether the upper limit of the total predicted water demand is greater than or equal to the early warning value. If it is greater than or equal to the early warning value, the early warning mechanism is triggered, otherwise it is not triggered. Different predicted water demand totals correspond to different early warning levels. If the early warning mechanism is triggered, the plant watering log of the target garden area is obtained. The watering indicators corresponding to the plant watering log include watering amount, watering frequency, watering time, and irrigation method. Subsequently, the plant watering log is uploaded to the processing center, and based on the association mapping between each plant watering record in the plant watering log and the historical environmental collection data, the corresponding relationship between each plant watering record in the plant watering log and the historical environmental collection data is obtained, and a watering demand model is established. Finally, the environmental collection data and the watering plant species are input into the watering demand model to obtain the watering demand result, which includes an early warning level, and the early warning level is obtained based on the predicted total amount of water demand. The automatic acquisition of the watering plan for gardening plants is realized, and timely early warning of plant water deficit is carried out.

[0045] The method provided in the embodiment of the present application also includes:

[0046] Based on the plant watering log and growth cycle information, evaluating water resource utilization efficiency;

[0047] Using the water resource utilization efficiency and the preset utilization efficiency, determining the irrigation strategy to be optimized;

[0048] The historical rainfall information of the target garden area is obtained, and the weather forecast data for the next period is obtained, and the irrigation demand model is constrained and adjusted in combination with the irrigation strategy to be optimized.

[0049] Based on the plant watering log and growth cycle information, the water resource utilization efficiency is evaluated. When evaluating the water resource utilization efficiency, the average growth cycle data of the same plant category is obtained by obtaining the same or similar watering method as the plant watering log, and the average growth cycle data and the growth cycle information are converted numerically according to the required time of the standard plant. For example, when the flowering period is reached, the time proportion of the total growth cycle is 0.5, and the corresponding numerical conversion is 0.5. The water resource utilization efficiency is determined according to the ratio of the growth cycle information after numerical conversion to the average growth cycle data. Finally, the water resource utilization efficiency is compared with the preset utilization efficiency to determine whether the water resource utilization efficiency is greater than or equal to the preset utilization efficiency. If it is greater than or equal to, there is no need to optimize the irrigation strategy. If it is less than, the irrigation strategy to be optimized is determined. The irrigation strategy to be optimized is 1 minus the water resource utilization efficiency, and the adjustment ratio is obtained, and the total irrigation amount is adjusted according to the adjustment ratio. Finally, the historical rainfall information of the target garden area is obtained, and the weather forecast data of the next period is obtained, and the historical rainfall information is added to the weather forecast data of the next period to obtain the total rainfall data before the end of the next period, and the average rainfall data of the target garden area is obtained, and the difference between the average rainfall data and the total rainfall data is obtained. According to the obtained difference data and the irrigation strategy to be optimized, the model output result of the irrigation demand model is constrained and adjusted. The constraint adjustment is to first perform proportional compensation of the irrigation strategy to be optimized, and then supplement the difference data, so as to obtain the final constraint adjustment result.

[0050] like Figure 3 As shown, the method provided in the embodiment of the present application also includes:

[0051] Each plant watering record in the plant watering log includes a historical watering start time, a historical watering end time, a historical watering method, a historical watering amount, and a historical watering plant type;

[0052] Based on the environmental collection data, according to the historical watering start time and the historical watering end time, synchronously intercept the environmental collection matching data corresponding to each plant watering record in the plant watering log;

[0053] The plant watering log is traversed, and matching data is collected by comparing each plant watering record in the plant watering log with the environment corresponding to each plant watering record in the plant watering log, and a training set and a validation set are set.

[0054] When establishing a watering demand model, it includes: each plant watering record in the plant watering log includes a historical watering start time, a historical watering end time, a historical watering method, a historical watering amount, and a historical watering plant type. Subsequently, based on the environmental collection data, according to the historical watering start time and the historical watering end time, the environmental collection matching data corresponding to each plant watering record in the plant watering log is synchronously intercepted. The plant watering log is traversed, and the environmental collection matching data corresponding to each plant watering record in the plant watering log is compared with each plant watering record in the plant watering log, and the traversal data of multiple plant watering logs are obtained. Among them, each plant watering record traversal data includes the environmental collection data of the watering start time and the environmental collection data of the watering end time. Based on the corresponding data, the data is divided according to the preset data ratio, and a training set and a verification set are set, and the training set data ratio is greater than the verification set ratio.

[0055] The method provided in the embodiment of the present application also includes:

[0056] Using the training set, a primary watering demand model is constructed, and the pre-watering demand result output by the primary watering demand model is compared with the actual watering demand in the verification set in combination with the verification set to obtain a model verification result;

[0057] According to the model verification results, the prediction accuracy and error range of the primary irrigation demand model are evaluated;

[0058] Whether the model performance meets the preset requirements is judged by the prediction accuracy and error range of the primary watering demand model. If the preset requirements are met, the primary watering demand model is used as the watering demand model.

[0059] Use the training set to train the neural network model, build a primary watering demand model, input the environmental collection data and watering plant species in the verification set into the primary watering demand model, and output the pre-watering demand result, that is, the specific watering method data, the watering method data includes the watering start time, watering end time, watering method, watering amount, and the final environmental collection data when the watering is completed, and compare it with the actual watering demand in the verification set to determine whether the pre-watering demand result is consistent with the actual watering demand, and obtain the model verification result. According to the model verification result, the prediction accuracy and error range of the primary watering demand model are evaluated. Finally, the model performance is judged by the prediction accuracy and error range of the primary watering demand model to determine whether it meets the preset requirements. If it meets the preset requirements, the primary watering demand model is used as the watering demand model.

[0060] The technical solution provided by the embodiment of the present invention acquires environmental data by collecting data in the target garden area. The soil moisture content in the environmental data is used as the current state to acquire the predicted amount of soil moisture deficit. In the target garden area, the garden greening plant set is traversed to obtain the set, and the plant growth water demand sequence of each plant at each growth stage is calculated by comparing the plant growth cycle. The water demand interval of various plants in the target garden area at the current growth stage is obtained through the plant growth water demand sequence and the garden greening plant set, and the total predicted amount of water demand is calculated in combination with the predicted amount of soil moisture deficit, and it is determined whether to trigger the early warning mechanism. If the early warning mechanism is triggered, the plant watering log of the target garden area is obtained, and the watering indicators corresponding to the plant watering log include watering amount, watering frequency, watering time, and irrigation method. The plant watering log is uploaded to the processing center, and a watering demand model is established based on the association mapping between each plant watering record in the plant watering log and the historical environmental data. The environmental data and the watering plant species are input into the watering demand model to obtain the watering demand result, and the watering demand result includes the early warning level. The system realizes the automatic acquisition of the watering plan for garden greening plants and provides a timely warning of plant water deficiency. The system solves the technical problems in the prior art that the acquisition efficiency of the watering plan for garden greening plants is low, the acquired watering plan is not intelligent, and it is difficult to realize the intelligent management of the watering of garden greening plants.

[0061] Embodiment 2: Based on the same inventive concept as the intelligent management method for watering garden plants in the above embodiment, the present invention also provides a system for the intelligent management method for watering garden plants. The system can be implemented by hardware and / or software, and can generally be integrated into an electronic device to execute the method provided by any embodiment of the present invention. Figure 4 As shown, the system comprises:

[0062] The data collection module 11 is used to collect data in the target garden area and obtain environmental data, wherein the environmental data includes soil moisture content, air temperature and humidity, and light intensity;

[0063] The water deficit prediction module 12 is used to use the soil moisture content in the environmental collection data as the current state, and to make a prediction in combination with the air temperature, humidity and light intensity in the environmental collection data to obtain a predicted amount of soil moisture deficit;

[0064] The demand sequence acquisition module 13 is used to traverse and obtain the set of garden greening plants in the target garden area, and calculate the plant growth water demand sequence of each plant in each growth stage according to the plant growth cycle;

[0065] The demand interval acquisition module 14 is used to obtain the water demand interval of various plants in the target garden area at the current growth stage through the plant growth water demand sequence and the garden greening plant set, calculate the total predicted water demand in combination with the soil water deficit predicted amount, and determine whether to trigger the early warning mechanism;

[0066] The demand model building module 15 is used to obtain the plant watering log of the target garden area if the early warning mechanism is triggered. The watering indicators corresponding to the plant watering log include watering amount, watering frequency, watering time, and irrigation method. The plant watering log is uploaded to the processing center, and a watering demand model is established based on the association mapping between each plant watering record in the plant watering log and the historical environment collection data;

[0067] The demand result acquisition module 16 is used to input the environmental collection data and the watering plant types into the watering demand model to obtain the watering demand result, and the watering demand result includes the warning level.

[0068] Furthermore, the demand sequence acquisition module 13 is also used for:

[0069] Deploy image acquisition equipment in the target garden area to identify and locate plant species, and record the collection of garden greening plants in the target garden area;

[0070] Collecting growth cycle information of each plant through a plant database, wherein the growth cycle information includes germination period, growth period, flowering period, fruiting period, and dormancy period;

[0071] The plant growth water requirement sequence of each plant at each growth stage is calculated based on the garden plant set and the growth cycle information according to the plant physiological characteristics and water requirement characteristics at different growth stages.

[0072] Furthermore, the demand sequence acquisition module 13 is also used for:

[0073] In the target garden area, using an image acquisition device to collect plant image information;

[0074] Analyze the plant image information in real time, identify and classify and record the type of each plant and the location coordinates of each plant;

[0075] By using the type of each plant and the position coordinates of each plant, a plant spatial distribution map corresponding to the target garden area is established to obtain a collection of garden greening plants.

[0076] Furthermore, the demand model building module 15 is also used for:

[0077] Based on the plant watering log and growth cycle information, evaluating water resource utilization efficiency;

[0078] Using the water resource utilization efficiency and the preset utilization efficiency, determining the irrigation strategy to be optimized;

[0079] The historical rainfall information of the target garden area is obtained, and the weather forecast data for the next period is obtained, and the irrigation demand model is constrained and adjusted in combination with the irrigation strategy to be optimized.

[0080] Furthermore, the demand model building module 15 is also used for:

[0081] Each plant watering record in the plant watering log includes a historical watering start time, a historical watering end time, a historical watering method, a historical watering amount, and a historical watering plant type;

[0082] Based on the environmental collection data, according to the historical watering start time and the historical watering end time, synchronously intercept the environmental collection matching data corresponding to each plant watering record in the plant watering log;

[0083] The plant watering log is traversed, and matching data is collected by comparing each plant watering record in the plant watering log with the environment corresponding to each plant watering record in the plant watering log, and a training set and a validation set are set.

[0084] Furthermore, the demand model building module 15 is also used for:

[0085] Using the training set, a primary watering demand model is constructed, and the pre-watering demand result output by the primary watering demand model is compared with the actual watering demand in the verification set in combination with the verification set to obtain a model verification result;

[0086] According to the model verification results, the prediction accuracy and error range of the primary irrigation demand model are evaluated;

[0087] Whether the model performance meets the preset requirements is judged by the prediction accuracy and error range of the primary watering demand model. If the preset requirements are met, the primary watering demand model is used as the watering demand model.

[0088] The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0089] Embodiment three: Figure 5The schematic diagram of the structure of the electronic device provided in the third embodiment of the present invention shows a block diagram of an exemplary electronic device suitable for implementing the implementation mode of the present invention. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 5 As shown, the electronic device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the electronic device can be one or more. Figure 5 Taking a processor 31 as an example, the processor 31, memory 32, input device 33 and output device 34 in the electronic device can be connected through a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0090] The memory 32 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to a smart management method for watering garden plants in an embodiment of the present invention. The processor 31 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 32, that is, realizing the above-mentioned smart management method for watering garden plants.

[0091] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A smart management method for watering garden plants, characterized in that: The method comprises: Collect data in the target garden area, obtain environmental data, and build a prediction model database. The environmental data includes soil moisture, air temperature and humidity, and light intensity. By obtaining the uninterrupted environmental data in the historical records, the soil moisture, air temperature and humidity, and light intensity data at different time nodes are obtained, and the soil moisture loss data corresponding to the preset time interval is obtained; Taking the soil moisture content in the environmental collection data as the current state, combined with the air temperature and humidity and light intensity in the environmental collection data, prediction is performed, and the soil moisture content, air temperature and humidity, and light intensity data at different time nodes are used to obtain the soil moisture loss data corresponding to the preset time period; and the mean of multiple soil moisture loss data under the same conditions is calculated to obtain the corresponding soil moisture deficit prediction amount of the soil moisture content, air temperature and humidity, and light intensity data; The predicted amount of soil moisture deficit of data collected from different environments is obtained, and a prediction model database is constructed, so as to obtain the predicted amount of soil moisture deficit based on the prediction model database and the environmental collection data, wherein the predicted amount of soil moisture deficit is the moisture deficit loss generated by the soil; In the target garden area, a set of garden greening plants is obtained by traversing, and a sequence of plant growth water requirements of each plant at each growth stage is calculated by comparing the plant growth cycle; The water demand range of various plants in the target garden area at the current growth stage is obtained through the plant growth water demand sequence and the garden greening plant set, and the total predicted water demand is calculated in combination with the predicted soil water deficit, and it is determined whether to trigger the early warning mechanism; If the early warning mechanism is triggered, the plant watering log of the target garden area is obtained, and the watering indicators corresponding to the plant watering log include watering amount, watering frequency, watering time, and irrigation method. The plant watering log is uploaded to the processing center, and a watering demand model is established based on the association mapping between each plant watering record in the plant watering log and the historical environmental collection data; The environmental collection data and the watering plant species are input into the watering demand model to obtain a watering demand result, wherein the watering demand result includes a warning level.

2. The method according to claim 1, characterized in that In the target garden area, a set of garden greening plants is obtained by traversing, and a plant growth water requirement sequence of each plant at each growth stage is calculated by comparing the plant growth cycle. The method includes: Deploy image acquisition equipment in the target garden area to identify and locate plant species, and record the collection of garden greening plants in the target garden area; Collecting growth cycle information of each plant through a plant database, wherein the growth cycle information includes germination period, growth period, flowering period, fruiting period, and dormancy period; The plant growth water requirement sequence of each plant at each growth stage is calculated based on the garden plant set and the growth cycle information according to the plant physiological characteristics and water requirement characteristics at different growth stages.

3. The method according to claim 2, characterized in that In the target garden area, an image acquisition device is arranged in the target garden area to identify and locate plant species, and a collection of garden greening plants in the target garden area is recorded. The method includes: In the target garden area, using an image acquisition device to collect plant image information; Analyze the plant image information in real time, identify and classify and record the type of each plant and the location coordinates of each plant; By using the type of each plant and the position coordinates of each plant, a plant spatial distribution map corresponding to the target garden area is established to obtain a collection of garden greening plants.

4. The method according to claim 1 or 2, characterized in that: The method further comprises: Based on the plant watering log and growth cycle information, evaluating water resource utilization efficiency; Using the water resource utilization efficiency and the preset utilization efficiency, determining the irrigation strategy to be optimized; The historical rainfall information of the target garden area is obtained, and the weather forecast data for the next period is obtained, and the irrigation demand model is constrained and adjusted in combination with the irrigation strategy to be optimized.

5. The method according to claim 1, characterized in that The plant watering log is uploaded to a processing center, and a watering demand model is established based on the association mapping between each plant watering record in the plant watering log and the historical environmental collection data. The method includes: Each plant watering record in the plant watering log includes a historical watering start time, a historical watering end time, a historical watering method, a historical watering amount, and a historical watering plant type; Based on the environmental collection data, according to the historical watering start time and the historical watering end time, synchronously intercept the environmental collection matching data corresponding to each plant watering record in the plant watering log; The plant watering log is traversed, and matching data is collected by comparing each plant watering record in the plant watering log with the environment corresponding to each plant watering record in the plant watering log, and a training set and a validation set are set.

6. The method according to claim 5, characterized in that The method comprises: Using the training set, a primary watering demand model is constructed, and the pre-watering demand result output by the primary watering demand model is compared with the actual watering demand in the verification set in combination with the verification set to obtain a model verification result; According to the model verification results, the prediction accuracy and error range of the primary irrigation demand model are evaluated; Whether the model performance meets the preset requirements is judged by the prediction accuracy and error range of the primary watering demand model. If the preset requirements are met, the primary watering demand model is used as the watering demand model.

7. An intelligent management system for watering garden plants, characterized in that: The system is used to execute the method according to any one of claims 1 to 6, and the system comprises: A data collection module is used to collect data in the target garden area and obtain environmental data, wherein the environmental data includes soil moisture content, air temperature and humidity, and light intensity; A water deficit prediction module is used to use the soil moisture content in the environmental collection data as the current state, and to make a prediction in combination with the air temperature, humidity and light intensity in the environmental collection data to obtain a predicted amount of soil moisture deficit; The demand sequence acquisition module is used to traverse and obtain the set of garden greening plants in the target garden area, and calculate the plant growth water demand sequence of each plant at each growth stage according to the plant growth cycle; A demand interval acquisition module is used to obtain the water demand interval of various plants in the target garden area at the current growth stage through the plant growth water demand sequence and the garden greening plant set, calculate the total predicted water demand in combination with the soil water deficit predicted amount, and determine whether to trigger the early warning mechanism; A demand model building module is used to obtain the plant watering log of the target garden area if the early warning mechanism is triggered. The watering indicators corresponding to the plant watering log include watering amount, watering frequency, watering time, and irrigation method. The plant watering log is uploaded to the processing center, and a watering demand model is established based on the association mapping between each plant watering record in the plant watering log and the historical environment collection data; The demand result acquisition module is used to input the environmental collection data and the watering plant species into the watering demand model to obtain the watering demand result, and the watering demand result includes an early warning level.

8. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement the intelligent management method for watering landscaping plants as described in any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, an intelligent management method for watering landscaping plants as described in any one of claims 1-6 is implemented.

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