A partition coupling type intelligent environment monitoring and linkage control method and system for large-scale sheep farm

Abstract

The application discloses a kind of partition coupling type intelligent environment monitoring and linkage control method and system for large-scale sheep farm, method includes the following steps: real-time acquisition sheep farm multi-region temperature humidity environment data, obtain the environment data set of each area;Based on the environment data set, the area of sheep shed space is divided into regions, obtain several independent regions with significant temperature and humidity difference;For several independent regions, extract temperature characteristic value and humidity characteristic value from the environment data set of each independent region, obtain the current environment state index of each independent region;If current environment state index shows that the temperature of a certain area is lower than the first preset threshold, then judge the regulation and control demand type of the area, and generate heating insulation instruction;Predict the environmental change trend after heating insulation instruction execution, and according to the environmental change trend, obtain the sensor feedback data after actual execution, determine the deviation degree of regulation and control execution.

Description

Technical Field

[0001] This invention relates to the field of agricultural breeding technology, specifically to a zoned coupled intelligent environmental monitoring and linkage control method and system for large-scale sheep farms. Background Technology

[0002] In modern agricultural animal husbandry, the importance of environmental monitoring and control technology is self-evident. It directly relates to the healthy growth of livestock and the improvement of production efficiency, especially in enclosed environments such as sheep sheds, where environmental factors have a particularly significant impact on breeding outcomes. Scientific and rational environmental regulation is not only key to ensuring animal welfare but also an important means to improve the economic benefits of animal husbandry. However, current technologies often reveal some deep-seated shortcomings when dealing with complex breeding environments, urgently requiring innovative breakthroughs.

[0003] Existing methods often fall short when faced with the diverse environmental needs within aquaculture spaces. Many solutions lack the ability to accurately perceive and dynamically respond to spatial differences, making it impossible to make targeted adjustments based on the actual environmental conditions of different areas. This deficiency significantly reduces the effectiveness of environmental control, especially in large-scale aquaculture facilities where the uneven distribution of environmental parameters is more pronounced, and a single control strategy is insufficient to meet diverse needs.

[0004] A deeper technical challenge lies in how to achieve accurate environmental data collection and coordinated matching of zoned control in a multi-zone environment. Environmental parameters such as temperature, humidity, or gas concentration often vary significantly between different zones within the aquaculture space. Without accurately identifying these zone characteristics, an effective control basis cannot be formed. Furthermore, identifying these regional differences presents the challenge of efficient linkage between control commands and execution equipment, as different zones may require entirely different control measures. For example, one zone may need ventilation and cooling, while another requires heating and insulation. This coordination contradiction involving multiple zones and diverse needs becomes the core obstacle to technical implementation. Summary of the Invention

[0005] To address the above technical problems, this invention provides a zoned coupled intelligent environmental monitoring and linkage control method for large-scale sheep farms, comprising the following steps:

[0006] Real-time collection of temperature and humidity environmental data from multiple areas of the sheep farm yields a set of environmental data for each area;

[0007] Based on the aforementioned environmental data set, the sheepfold space was divided into several independent areas with significant differences in temperature and humidity.

[0008] For each of the independent regions, temperature and humidity feature values ​​are extracted from the environmental data set of each independent region to obtain the current environmental status index of each independent region.

[0009] If the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, then the control demand type of the area is determined and a heating and heat preservation command is generated.

[0010] Predict the environmental change trend after the heating and heat preservation command is executed, and based on the environmental change trend, obtain sensor feedback data after actual execution to determine the degree of deviation in the control execution.

[0011] Preferably, the method for obtaining the environmental data set includes:

[0012] The environment of the sheepfold is collected in real time through a sensor network to obtain temperature and humidity data from multiple points in the area, forming a preliminary set of environmental parameters.

[0013] The preliminary set of environmental parameters is subjected to outlier removal and noise reduction to obtain the environmental data set.

[0014] Preferably, the method for obtaining the independent region includes:

[0015] Based on the environmental dataset, the K-means clustering method is used to perform preliminary regional division based on temperature and humidity differences, resulting in preliminary regional division results.

[0016] Based on the preliminary division results, the average temperature and humidity in each region are obtained, and the rationality of the regional distribution is judged by comparing the numerical differences between regions.

[0017] If the numerical difference in the regional distribution exceeds the second preset threshold, the environmental data set is subjected to secondary clustering adjustment to redetermine the boundaries of independent regions and obtain accurate regional division results.

[0018] Based on the precise region division results, the environmental data features of each independent region after re-division are obtained, and the presence of overlapping or ambiguous regions is determined through feature comparison analysis.

[0019] Based on the results of feature comparison analysis, if overlapping or ambiguous regions are found, the data in the overlapping or ambiguous regions are locally reprocessed to obtain the final region division scheme, and several independent regions are generated according to the final region division scheme.

[0020] Preferably, the method for obtaining the current environmental state index includes:

[0021] For each of the aforementioned independent regions, temperature and humidity feature values ​​are extracted from the environmental data set of each of the aforementioned independent regions.

[0022] The temperature and humidity feature values ​​are classified using a support vector machine to determine the specific state classification label for each independent region.

[0023] By using state classification labels and combining them with indicator generation rules, the current state of each independent region is matched with preset state indicators to obtain the current environmental state indicators.

[0024] Preferably, the method for generating the heating and heat preservation command includes:

[0025] If the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, the temperature detection mechanism is triggered, and historical temperature data is compared to determine whether there is a sustained low temperature state in the area.

[0026] Based on the determination of the sustained low temperature state, the type of adjustment requirement for this region is determined;

[0027] Based on the adjustment requirement type, a corresponding heating and heat preservation instruction is generated, and the instruction content is formatted to obtain a standardized control signal.

[0028] The present invention also provides a zoned coupled intelligent environmental monitoring and linkage control system for large-scale sheep farms. The system applies the above-mentioned method and includes: a data acquisition module, a zone division module, an environmental indicator acquisition module, an instruction generation module, and an execution module.

[0029] The data acquisition module is used to collect temperature and humidity environmental data in multiple areas of the sheep farm in real time, and obtain environmental data sets for each area.

[0030] The region division module divides the sheepfold space into several independent regions with significant temperature and humidity differences based on the environmental data set.

[0031] The environmental indicator acquisition module extracts temperature and humidity feature values ​​from the environmental data set of each of the several independent regions to obtain the current environmental status indicator of each independent region.

[0032] In the instruction generation module, if the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, the control demand type of the area is determined and a heating and heat preservation instruction is generated.

[0033] The execution module is used to predict the environmental change trend after the heating and heat preservation command is executed, and based on the environmental change trend, obtain sensor feedback data after actual execution to determine the degree of deviation in the control execution.

[0034] Preferably, the workflow of the data acquisition module includes:

[0035] The environment of the sheepfold is collected in real time by a sensor network to obtain temperature and humidity data from multiple points in the area, forming a preliminary set of environmental parameters.

[0036] The preliminary set of environmental parameters is subjected to outlier removal and noise reduction to obtain the environmental data set.

[0037] Preferably, the workflow of the region division module includes:

[0038] Based on the environmental dataset, the K-means clustering method is used to perform preliminary regional division based on temperature and humidity differences, resulting in preliminary regional division results.

[0039] Based on the preliminary division results, the average temperature and humidity in each region are obtained, and the rationality of the regional distribution is judged by comparing the numerical differences between regions.

[0040] If the numerical difference in the regional distribution exceeds the second preset threshold, the environmental data set is subjected to secondary clustering adjustment to redetermine the boundaries of independent regions and obtain accurate regional division results.

[0041] Based on the precise region division results, the environmental data features of each independent region after re-division are obtained, and the presence of overlapping or ambiguous regions is determined through feature comparison analysis.

[0042] Based on the results of feature comparison analysis, if overlapping or ambiguous regions are found, the data in the overlapping or ambiguous regions are locally reprocessed to obtain the final region division scheme, and several independent regions are generated according to the final region division scheme.

[0043] Preferably, the workflow of the environmental indicator acquisition module includes:

[0044] For each of the aforementioned independent regions, temperature and humidity feature values ​​are extracted from the environmental data set of each of the aforementioned independent regions.

[0045] The temperature and humidity feature values ​​are classified using a support vector machine to determine the specific state classification label for each independent region.

[0046] By using state classification labels and combining them with indicator generation rules, the current state of each independent region is matched with preset state indicators to obtain the current environmental state indicators.

[0047] Preferably, the workflow of the instruction generation module includes:

[0048] If the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, the temperature detection mechanism is triggered, and historical temperature data is compared to determine whether there is a sustained low temperature state in the area.

[0049] Based on the determination of the sustained low temperature state, the type of adjustment requirement for this region is determined;

[0050] Based on the adjustment requirement type, a corresponding heating and heat preservation instruction is generated, and the instruction content is formatted to obtain a standardized control signal.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] This invention achieves refined management and dynamic optimization of the sheepfold environment by combining data-driven and intelligent prediction. Its core lies in the closed-loop mechanism of regional division and predictive feedback, which ensures the accuracy and efficiency of environmental regulation, and ultimately improves the quality of the livestock's growth environment and breeding efficiency in the sheepfold. Attached Figure Description

[0053] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0056] Explanation of reference numerals in the attached figures:

[0057] 1010, Processor; 1020, Memory; 1030, Input / Output Interface; 1040, Communication Interface; 1050, Bus. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0060] Example 1

[0061] In this embodiment, as Figure 1 As shown, a zoned coupled intelligent environmental monitoring and linkage control method for large-scale sheep farms includes the following steps:

[0062] S1. Real-time collection of temperature and humidity environmental data in multiple areas of the sheep farm to obtain environmental data sets for each area.

[0063] The methods for obtaining the environmental data set include: real-time collection of the sheepfold environment through a sensor network to obtain temperature and humidity data from multiple regional points to form a preliminary environmental parameter set; and outlier removal and noise reduction of the preliminary environmental parameter set to obtain the environmental data set.

[0064] In this embodiment, a wireless temperature and humidity sensor network deployed at key locations in the sheepfold (such as the rest area, feeding passage, and around ventilation openings) collects real-time synchronous data from each area at a preset fixed frequency (e.g., every 5 minutes) to obtain raw temperature and humidity readings, forming a preliminary set of environmental parameters. To ensure data quality, an outlier detection and removal method based on statistical principles is first employed: the Z-score of the data stream at each monitoring point is calculated, and significant outliers caused by momentary sensor malfunctions or sudden external interference are identified and removed. Subsequently, moving average filtering is applied to smooth and denoise the data to preserve the true environmental change trend while suppressing high-frequency fluctuations. After the above cleaning and correction, a stable and reliable multi-region environmental data set is finally generated.

[0065] S2. Based on the environmental data set, the sheepfold space is divided into several independent areas with significant temperature and humidity differences.

[0066] The method for obtaining independent regions includes: based on the environmental dataset, using the K-means clustering method, preliminary regional division is performed based on temperature and humidity differences to obtain preliminary regional division results; based on the preliminary division results, the average temperature and humidity of each region are obtained, and the rationality of the regional distribution is judged by comparing the numerical differences between regions; if the numerical differences in the regional distribution exceed a second preset threshold, the environmental dataset is adjusted by secondary clustering to redefine the boundaries of independent regions and obtain precise regional division results; based on the precise regional division results, the environmental data features of each re-divided independent region are obtained, and feature comparison analysis is used to determine whether there are overlapping or ambiguous regions; based on the results of feature comparison analysis, if overlapping or ambiguous regions are found, the data of that part is locally reprocessed to obtain the final regional division scheme, and several independent regions are generated based on the final regional division scheme.

[0067] In this embodiment, firstly, based on the cleaned environmental data set, a K-means clustering algorithm is used for preliminary region division: the temperature and humidity data of each monitoring point are combined to form a two-dimensional feature vector, which serves as the clustering input; the elbow rule is used to determine the optimal number of clusters K to reflect the actual physical zoning within the sheepfold (such as rest areas, activity areas, ventilation areas, etc.); the algorithm uses Euclidean distance as a similarity measure and iteratively updates the cluster centers until the allocation is stable, thus obtaining the preliminary region division results. Secondly, the preliminary division results are evaluated for rationality and adjusted a second time; the average temperature and humidity of all data points within each region are calculated as representative environmental indicators of that region; subsequently, the average temperature difference and average humidity difference between any two regions are compared. If there exists a pair of regions where both bidirectional differences are less than a preset threshold (such as a temperature difference below 1.5°C and a humidity difference below 3%), the region division is deemed too redundant or the boundaries unclear, requiring a second clustering adjustment. This involves re-initializing the cluster centers, appropriately increasing the number of clusters K, or introducing geographical location-based constraints to ensure significant temperature and humidity differences between independent regions, resulting in a more precise region division with clearer boundaries. Finally, overlapping and ambiguous regions are detected and optimized. Based on the precise segmentation, environmental data features (such as mean, variance, and extreme values) of each independent region are extracted. Through feature comparison analysis, the existence of adjacent regions with high feature similarity is identified. If the confidence level of data points at the boundary of a region is lower than a set threshold, it is determined that the region has an ambiguous boundary, and such data subsets are locally reprocessed, for example, by using hierarchical clustering for fine-tuning, or by spatial correction based on the actual deployment location of sensors, until the features of each region are clearly distinguishable and the boundaries are well-defined. Finally, several independent region segmentation schemes with significant temperature and humidity differences are output, resulting in several independent regions.

[0068] S3. For several independent regions, extract temperature and humidity feature values ​​from the environmental data set of each independent region to obtain the current environmental status index of each independent region.

[0069] The method for obtaining the current environmental status index includes: for several independent regions, extracting temperature and humidity feature values ​​from the environmental data set of each independent region; using support vector machine to classify the temperature and humidity feature values ​​to determine the specific status classification label of each independent region; and matching the current status of each independent region with the preset status index by combining the status classification label with the index generation rule to obtain the current environmental status index.

[0070] In this embodiment, firstly, for each independent region, multidimensional feature values ​​are extracted from its environmental dataset: these include statistical features of temperature and humidity (such as mean, variance, and extreme values), time series features (such as short-term trends and fluctuation frequencies), and spatial distribution features within the region (such as the gradient difference between the center and edge points). Then, a pre-trained Support Vector Machine (SVM) model is used to classify these feature vectors to determine the specific state classification label for each region. The SVM model is trained based on historical labeled data, which covers various typical environmental states corresponding to different physiological stages of sheep (such as "comfortable," "mild cold stress," "heat stress," and "high humidity risk"). Finally, based on the obtained SVM state classification labels and a preset indicator generation rule, the current state of each region is mapped to a standardized current environmental state indicator. The indicator generation rule is a matching matrix that associates the classification label with specific temperature and humidity threshold ranges and priorities. For example, if a region is classified as "mild cold stress", its current environmental status index may be quantified as "low temperature, monitoring required"; if it is classified as "high humidity risk", the index may be quantified as "humidity seriously exceeds the standard, priority intervention required".

[0071] S4. If the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, then determine the type of control demand for that area and generate a heating and heat preservation command.

[0072] The method for generating heating and heat preservation instructions includes: if the current environmental status indicator shows that the temperature of a certain area is lower than a first preset threshold, a temperature detection mechanism is triggered, and historical temperature data is compared to determine whether there is a continuous low temperature state in the area; based on the determination result of the continuous low temperature state, the adjustment demand type of the area is determined; based on the adjustment demand type, a corresponding heating and heat preservation instruction is generated, and the instruction content is formatted to obtain a standardized control signal.

[0073] In this embodiment, when the system determines that the temperature of an independent area is lower than a first preset threshold (e.g., 12°C for lamb nursery and 8°C for adult sheep rest area) based on the current environmental status indicators, a deep temperature detection mechanism will be immediately triggered. This mechanism not only focuses on the instantaneous temperature but also retrieves historical temperature sequence data within the area over a recent time window (e.g., the past hour) for time-series comparison and trend analysis. By calculating the average temperature, the rate of decrease, and the proportion of low-temperature duration within that time period, the system comprehensively judges whether the area is in a "continuous low-temperature state." If the analysis results show that the temperature is lower than the threshold and the duration exceeds the preset time limit (e.g., 15 minutes), or if a stable downward trend in temperature is detected, it is determined that there is a clear continuous low-temperature state, and the control process is initiated. Based on the determination of the continuous low-temperature state, the specific control requirement type for the area is further determined: the state classification labels output by the support vector machine (SVM) model are refined and differentiated, and weighted corrections are made in combination with area attributes (e.g., whether it is a lamb area, farrowing house, or high-density feeding area). For example, for an adult sheep activity area classified as "mild cold stress," a "warming requirement" instruction might be generated, focusing on moderately raising the ambient temperature; while for a lamb rest area classified as "severe cold stress," an "emergency heating" instruction might be generated, requiring rapid and significant temperature increases. Based on the determined control demand type, the system generates corresponding heating and warming instructions. The instructions are specific, including the target area number, target temperature setpoint, recommended heating rate, preferred execution equipment (such as infrared heating lamps, warm air units, electric heating mats), and their recommended operating power or setting. Subsequently, the instruction content is formatted and converted into standardized, structured control signals.

[0074] S5. Predict the environmental change trend after the heating and heat preservation command is executed, and based on the environmental change trend, obtain the sensor feedback data after the actual execution to determine the degree of deviation in the control execution.

[0075] In this embodiment, after the heating and insulation command is issued, a thermodynamic dynamic model is first constructed based on the physical characteristics of the target area (such as space size, insulation material properties, and ventilation rate) and the current environmental data set to predict the trend of environmental changes. Subsequently, a wireless temperature and humidity sensor network deployed in the area is used to collect environmental data after the command is executed at a high frequency (such as every minute) to obtain a feedback dataset. Then, the actual measured feedback dataset is compared with the predicted trend at time points and the degree of deviation is calculated. If the deviation continues to exceed the threshold, it is marked as a significant execution deviation, and the type of deviation (such as temperature lag or sudden increase in humidity) is analyzed. At the same time, the deviation data is recorded to optimize the prediction model parameters and form an adaptive closed-loop control.

[0076] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0077] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the sequence number of each step in the above embodiments does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The actions or steps recorded in the claims can be performed in a different order than that in the above embodiments and can still achieve the desired result. In addition, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] Example 2

[0079] In this embodiment, a zoned coupled intelligent environmental monitoring and linkage control system for large-scale sheep farms includes: a data acquisition module, a zone division module, an environmental indicator acquisition module, an instruction generation module, and an execution module.

[0080] The data acquisition module is used to collect temperature and humidity environmental data in multiple areas of the sheep farm in real time, and obtain environmental data sets for each area.

[0081] The workflow of the data acquisition module includes: real-time acquisition of the sheepfold environment through a sensor network to obtain temperature and humidity data from multiple regional points, forming a preliminary set of environmental parameters; and outlier removal and noise reduction of the preliminary set of environmental parameters to obtain an environmental data set.

[0082] The regional division module divides the sheepfold space into several independent regions with significant temperature and humidity differences based on the environmental data set.

[0083] The workflow of the region division module includes: based on the environmental dataset, using the K-means clustering method, preliminary region division is performed according to temperature and humidity differences to obtain preliminary region division results; based on the preliminary division results, the average temperature and humidity of each region are obtained, and the rationality of the region distribution is judged by comparing the numerical differences between regions; if the numerical differences of the region distribution exceed a second preset threshold, the environmental dataset is adjusted by secondary clustering to redefine the boundaries of independent regions and obtain precise region division results; based on the precise region division results, the environmental data features of each re-divided independent region are obtained, and feature comparison analysis is used to determine whether there are overlapping or ambiguous regions; based on the feature comparison analysis results, if overlapping or ambiguous regions are found, the data of that part is locally reprocessed to obtain the final region division scheme, and several independent regions are generated according to the final region division scheme.

[0084] The environmental indicator acquisition module extracts temperature and humidity feature values ​​from the environmental data set of each independent region to obtain the current environmental status indicators of each independent region.

[0085] The workflow of the environmental indicator acquisition module includes: for several independent areas, extracting temperature and humidity feature values ​​from the environmental data set of each independent area; using support vector machine to classify the temperature and humidity feature values ​​to determine the specific state classification label of each independent area; and matching the current state of each independent area with the preset state indicators through the state classification labels and the indicator generation rules to obtain the current environmental state indicator.

[0086] In the instruction generation module, if the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, the control demand type of that area is determined and a heating and heat preservation instruction is generated.

[0087] The workflow of the instruction generation module includes: if the current environmental status indicator shows that the temperature of a certain area is lower than the first preset threshold, the temperature detection mechanism is triggered, and historical temperature data is compared to determine whether there is a continuous low temperature state in the area; based on the determination of the continuous low temperature state, the adjustment demand type of the area is determined; based on the adjustment demand type, the corresponding heating and heat preservation instruction is generated, and the instruction content is formatted to obtain a standardized control signal.

[0088] The execution module is used to predict the environmental change trend after the heating and heat preservation command is executed, and based on the environmental change trend, obtain sensor feedback data after actual execution to determine the degree of deviation in the control execution.

[0089] The system described in the above embodiments is used to implement the corresponding zonal coupled intelligent environmental monitoring and linkage control method for large-scale sheep farms in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0090] It should be noted that the aforementioned zoned coupled intelligent environmental monitoring and linkage control system for large-scale sheep farms is embodied in the form of functional units. The term "module" here can be implemented in software and / or hardware, without specific limitations.

[0091] For example, a "module" can be a software program, hardware circuit, or a combination of both that implements the above functions. Hardware circuits may include application-specific integrated circuits (ASICs), electronic circuits, processors (e.g., shared processors, proprietary processors, or group processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functions.

[0092] Example 3

[0093] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the partitioned coupled intelligent environmental monitoring and linkage control method for large-scale sheep farms described in any of the above embodiments.

[0094] Figure 2 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0095] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0096] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0097] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0098] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB (Universal Serial Bus), network cable, etc.) or wireless means (such as mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0099] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0100] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0101] The system described in the above embodiments is used to implement the corresponding zonal coupled intelligent environmental monitoring and linkage control method for large-scale sheep farms in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0102] Example 4

[0103] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, which are used to cause the computer to execute the partitioned coupled intelligent environmental monitoring and linkage control method for large-scale sheep farms as described in any of the above embodiments.

[0104] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0105] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the partitioned coupled intelligent environmental monitoring and linkage control method for large-scale sheep farms as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0106] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0107] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this disclosure, the provided drawings may or may not show well-known power / ground connections to integrated circuit (IC) chips and other components. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this disclosure, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this disclosure will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuitry) have been set forth to describe exemplary embodiments of this disclosure, it will be apparent to those skilled in the art that the embodiments of this disclosure may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0108] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0109] Therefore, the units of the various examples described in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] This disclosure is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for partition-coupled intelligent environment monitoring and linkage control for large-scale sheep farms, characterized in that, Includes the following steps: Real-time collection of temperature and humidity environmental data from multiple areas of the sheep farm yields a set of environmental data for each area; Based on the aforementioned environmental data set, the sheepfold space was divided into several independent areas with significant differences in temperature and humidity. For each of the independent regions, temperature and humidity feature values ​​are extracted from the environmental data set of each independent region to obtain the current environmental status index of each independent region. If the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, then the control demand type of the area is determined and a heating and heat preservation command is generated. Predict the environmental change trend after the heating and heat preservation command is executed, and based on the environmental change trend, obtain sensor feedback data after actual execution to determine the degree of deviation in the control execution; The methods for obtaining the environmental data set include: The environment of the sheepfold is collected in real time through a sensor network to obtain temperature and humidity data from multiple points in the area, forming a preliminary set of environmental parameters. The preliminary environmental parameter set is subjected to outlier removal and noise reduction to obtain the environmental data set; The methods for obtaining the independent regions include: Based on the environmental dataset, the K-means clustering method is used to perform preliminary regional division based on temperature and humidity differences, resulting in preliminary regional division results. Based on the preliminary division results, the average temperature and humidity in each region are obtained, and the rationality of the regional distribution is judged by comparing the numerical differences between regions. If the numerical difference in the regional distribution exceeds the second preset threshold, the environmental data set is subjected to secondary clustering adjustment to redetermine the boundaries of independent regions and obtain accurate regional division results. Based on the precise region division results, the environmental data features of each independent region after re-division are obtained, and the presence of overlapping or ambiguous regions is determined through feature comparison analysis. Based on the results of feature comparison analysis, if overlapping or ambiguous regions are found, the data in the overlapping or ambiguous regions are locally reprocessed to obtain the final region division scheme, and several independent regions are generated according to the final region division scheme. The methods for obtaining the current environmental state indicators include: For each of the aforementioned independent regions, temperature and humidity feature values ​​are extracted from the environmental data set of each of the aforementioned independent regions. The temperature and humidity feature values ​​are classified using a support vector machine to determine the specific state classification label for each independent region. By using state classification labels and combining them with indicator generation rules, the current state of each independent region is matched with preset state indicators to obtain the current environmental state indicators. The method for generating the heating and heat preservation command includes: If the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, the temperature detection mechanism is triggered, and historical temperature data is compared to determine whether there is a sustained low temperature state in the area. Based on the determination of the sustained low temperature state, the type of adjustment requirement for this region is determined; Based on the adjustment requirement type, a corresponding heating and heat preservation instruction is generated, and the instruction content is formatted to obtain a standardized control signal.

2. A partition-coupled intelligent environment monitoring and linkage control system for large-scale sheep farms, the system applies the method of claim 1, characterized in that, include: The module includes a data acquisition module, a region division module, an environmental indicator acquisition module, an instruction generation module, and an execution module. The data acquisition module is used to collect temperature and humidity environmental data in multiple areas of the sheep farm in real time, and obtain environmental data sets for each area. The region division module divides the sheepfold space into several independent regions with significant temperature and humidity differences based on the environmental data set. The environmental indicator acquisition module extracts temperature and humidity feature values ​​from the environmental data set of each of the several independent regions to obtain the current environmental status indicator of each independent region. In the instruction generation module, if the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, the control demand type of the area is determined and a heating and heat preservation instruction is generated. The execution module is used to predict the environmental change trend after the heating and heat preservation command is executed, and based on the environmental change trend, obtain sensor feedback data after actual execution to determine the degree of deviation in the control execution.

3. The partition-coupled intelligent environment monitoring and linkage control system for large-scale sheep farms according to claim 2, characterized in that, The workflow of the data acquisition module includes: The environment of the sheepfold is collected in real time through a sensor network to obtain temperature and humidity data from multiple points in the area, forming a preliminary set of environmental parameters. The preliminary set of environmental parameters is subjected to outlier removal and noise reduction to obtain the environmental data set.

4. The partition-coupled intelligent environment monitoring and linkage control system for large-scale sheep farms according to claim 2, characterized in that, The workflow of the region division module includes: Based on the environmental dataset, the K-means clustering method is used to perform preliminary regional division based on temperature and humidity differences, resulting in preliminary regional division results. Based on the preliminary division results, the average temperature and humidity in each region are obtained, and the rationality of the regional distribution is judged by comparing the numerical differences between regions. If the numerical difference in the regional distribution exceeds the second preset threshold, the environmental data set is subjected to secondary clustering adjustment to redetermine the boundaries of independent regions and obtain accurate regional division results. Based on the precise region division results, the environmental data features of each independent region after re-division are obtained, and the presence of overlapping or ambiguous regions is determined through feature comparison analysis. Based on the results of feature comparison analysis, if overlapping or ambiguous regions are found, the data in the overlapping or ambiguous regions are locally reprocessed to obtain the final region division scheme, and several independent regions are generated according to the final region division scheme.

5. The zoned coupled intelligent environmental monitoring and linkage control system for large-scale sheep farms according to claim 2, characterized in that, The workflow of the environmental indicator acquisition module includes: For each of the aforementioned independent regions, temperature and humidity feature values ​​are extracted from the environmental data set of each of the aforementioned independent regions. The temperature and humidity feature values ​​are classified using a support vector machine to determine the specific state classification label for each independent region. By using state classification labels and combining them with indicator generation rules, the current state of each independent region is matched with preset state indicators to obtain the current environmental state indicators.

6. The zoned coupled intelligent environmental monitoring and linkage control system for large-scale sheep farms according to claim 2, characterized in that, The workflow of the instruction generation module includes: If the current environmental status indicator shows that the temperature in a certain area is lower than the first preset threshold, the temperature detection mechanism is triggered, and historical temperature data is compared to determine whether there is a sustained low temperature state in the area. Based on the determination of the sustained low temperature state, the type of adjustment requirement for this region is determined; Based on the adjustment requirement type, a corresponding heating and heat preservation instruction is generated, and the instruction content is formatted to obtain a standardized control signal.

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