Chinese herbal medicine intelligent drip irrigation regulation and control system based on terminal cloud collaboration

Abstract

The invention discloses a Chinese herbal medicine intelligent drip irrigation regulation and control system based on end-cloud cooperation, and relates to the technical field of agricultural intelligent irrigation, and the system comprises an edge equipment end which is deployed in a Chinese herbal medicine planting area, comprises a multi-mode sensor module, a communication module and an irrigation execution mechanism, and is used for collecting soil humidity, illumination intensity and meteorological data in real time, and sending the collected data to a cloud server; and the drip irrigation valve is controlled through the irrigation executing mechanism. According to the Chinese herbal medicine intelligent drip irrigation regulation and control system based on end-cloud collaboration, through an end-cloud collaboration architecture and a hierarchical decision-making mechanism, the drip irrigation regulation and control accuracy and real-time performance in a Chinese herbal medicine planting environment are improved. A local AI inference engine of the edge equipment end is combined with a dynamic learning module, an optimization instruction can be automatically generated during network fluctuation, dependence on real-time communication of the cloud end is reduced, and continuity and reliability of irrigation operation in complex terrains are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural intelligent irrigation, and specifically to an intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration. Background Art

[0002] In traditional Chinese herbal medicine intelligent drip irrigation control systems, the high degree of dependence between the cloud and the device side has become a core bottleneck restricting precision irrigation. Existing technologies generally adopt a one-way communication mode, that is, the cloud centrally processes environmental monitoring data and issues irrigation instructions, and the device side only serves as an execution terminal. However, Chinese herbal medicine planting areas are mostly distributed in complex terrains such as mountains and terraces, and the problems of poor network signal stability and uneven coverage have long existed. When the communication between the cloud and the device side causes data synchronization delays due to network fluctuations or interruptions, the device side cannot obtain real-time control instructions in time, and the cloud side also finds it difficult to perceive changes in the on-site environment in time, resulting in a dynamic deviation between the irrigation amount and the actual water requirement of the Chinese herbal medicine. This deviation is particularly significant during the critical growth period of Chinese herbal medicines that are sensitive to water requirements. At the very least, it affects the accumulation of effective ingredients in the medicinal materials, and at worst, it causes root rot or drought stress, which directly affects the planting efficiency.

[0003] While existing technologies attempt to improve communication range and energy efficiency through low-power wide area network (LPWAN) technology, they lack local autonomous decision-making mechanisms for network instability. Devices remain overly reliant on real-time cloud responses and lack emergency control capabilities based on historical data or pre-set rules. Furthermore, centralized cloud-based processing struggles to cope with the demands of high-concurrency data exchange, further exacerbating latency issues in command issuance and data transmission. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration, which solves the problem of how to achieve real-time synchronization of irrigation control instructions and monitoring data to ensure drip irrigation accuracy in complex planting environments with unstable network signals by optimizing the end-cloud collaborative architecture.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a Chinese herbal medicine intelligent drip irrigation control system based on end-cloud collaboration, comprising:

[0006] Edge devices, deployed in Chinese herbal medicine planting areas, include multimodal sensor modules, communication modules, and irrigation actuators. They collect soil moisture, light intensity, and meteorological data in real time and control drip irrigation valves through irrigation actuators.

[0007] The cloud server, connected to the edge device via a wide area network, is used to store historical irrigation data, train global optimization models, and issue control instructions. Through a federated learning framework, the cloud server periodically receives local model parameters and irrigation logs from each edge node, aggregates the data, and trains the global optimization model. For example, the cloud analyzes the differences in irrigation performance between different nodes under the same climatic conditions, extracts common patterns, generates universal optimization parameters, and sends them to the edge via an encrypted channel. After receiving the updated model, the edge gradually replaces inefficient rules in the local model, such as adjusting fixed thresholds to dynamic interval thresholds. When the network is unstable, the cloud uses an asynchronous update mechanism, allowing the edge to receive model patches in batches after communication is restored, avoiding interruptions to model iteration.

[0008] The dynamic edge collaborative control module is integrated between the edge device and the cloud server, and includes an edge autonomous unit, a cloud optimization unit and a redundant communication unit; the edge autonomous unit generates preliminary control instructions based on local sensor data, the cloud optimization unit aggregates multi-node data through a federated learning framework to optimize the global model, and the redundant communication unit configures the main link and the backup link, and dynamically switches the communication channel according to the network signal strength.

[0009] Preferably, the edge device further includes:

[0010] AI inference engine with built-in transfer learning algorithm generates irrigation duration and water volume thresholds based on local sensor data and historical rule base;

[0011] The emergency rule base stores soil moisture gradient thresholds and periodic water replenishment strategies, triggering offline irrigation control when the network is interrupted.

[0012] Preferably, the hierarchical decision-making architecture of the dynamic edge collaborative control module includes:

[0013] The edge autonomous unit generates preliminary instructions by fusing real-time sensor data with a preset rule base through an AI inference engine, and adjusts decision weights based on a local dynamic learning module;

[0014] The cloud optimization unit periodically receives local model parameters and execution logs uploaded by each edge node, trains the global optimization model, and sends it to the edge device through an encrypted channel.

[0015] Preferably, the redundant communication unit includes:

[0016] The main link uses NB-IoT to achieve wide-area coverage;

[0017] The backup link uses LoRa self-organizing network to form a short-range communication network;

[0018] When the signal strength of the main link is lower than the preset threshold, it automatically switches to the backup link, and after the network is restored, the cached data is synchronized to the cloud according to the timeliness weight.

[0019] The redundant communication unit ensures communication continuity by dynamically switching between the main and backup links. The main link uses NB-IoT technology to achieve wide-area coverage, and the backup link is based on a short-range network built on a LoRa self-organizing network. When the signal strength of the main link remains below the preset threshold, the system automatically switches to the backup link. For example, in terraced planting areas, adjacent devices relay data to network coverage points via LoRa modules. After the network is restored, the system asynchronously and synchronously caches data by timeliness weight, prioritizing uploading of high-timeliness data and uploading persistent data in batches. High-timeliness data includes records of humidity drops within the past 10 minutes, and persistent data includes model parameters. If the network bandwidth is insufficient, data compression technology is enabled to reduce the transmission load. Data compression technology involves converting humidity values ​​into interval codes.

[0020] Preferably, the dynamic edge collaborative control module further includes:

[0021] Intelligent cache queue, including real-time cache module and persistent cache module;

[0022] The real-time cache module temporarily stores high-frequency environmental data and marks the timeliness weight;

[0023] The persistent cache module stores key control instructions and model parameters, and prioritizes uploading high-timeliness weighted data after network recovery.

[0024] The intelligent cache queue ensures data integrity and efficient synchronization through a hierarchical storage and verification mechanism. The real-time cache module temporarily stores high-frequency environmental data and weights it according to its timeliness, while the persistent cache module stores key instructions and model parameters. When cache space approaches its upper limit, older data is overwritten in ascending order of weight, retaining the highest-weighted critical events, such as irrigation failure alarms. After network recovery, the system synchronizes data in stages: high-weighted real-time data is uploaded first and its integrity verified, followed by persistent data. A checksum, such as a hash value, is appended to data before uploading. Upon receipt, the cloud verifies its integrity, triggering a retransmission if failure occurs.

[0025] Preferably, the hierarchical decision-making architecture further includes:

[0026] Multimodal data verification unit, deployed in the cloud and on edge devices;

[0027] The cloud verification module generates a command rationality score by comparing the historical irrigation effect database with real-time weather forecast data;

[0028] The edge verification module verifies the matching degree of irrigation amount through correlation analysis between soil moisture and leaf transpiration rate. If the deviation exceeds the limit, it triggers local re-decision.

[0029] The multimodal data verification unit ensures the rationality of instructions through dual verification on the cloud and edge. The cloud verification module retrieves the historical irrigation effect database and real-time meteorological data to reversely evaluate the risk level of edge instructions. For example, if the edge recommends large-scale irrigation during a rainstorm warning, the cloud will mark it as high risk after comparing historical data and issue correction suggestions. The edge verification module verifies the execution result through sensor data fusion. If the humidity increases after irrigation but the transpiration rate does not increase, a local re-decision is triggered. Among them, sensor data fusion includes correlation analysis between soil moisture and leaf transpiration rate. When the network fluctuates, the edge increases the verification frequency and relies on local data for rapid verification; if the verification fails multiple times in a row, it switches to the backup decision model and marks the anomaly for cloud analysis.

[0030] Preferably, the working logic of the emergency rule library is:

[0031] When the duration of network interruption exceeds the preset threshold, the preset soil moisture gradient threshold method is called to generate emergency instructions;

[0032] After the network is restored, the cloud server generates compensation instructions based on the differences between the emergency execution log and the global model to correct the irrigation deviation.

[0033] The emergency rule library invokes pre-stored policies to ensure irrigation continuity during network outages. If the network outage lasts longer than a preset duration, the system matches gradient threshold rules based on real-time humidity: short-term, high-frequency irrigation is triggered when humidity falls below 30%, and long-term, deep water replenishment is initiated when humidity falls below 15%. All operations are recorded in a local log and uploaded to the cloud by priority after network restoration. The cloud compares the log with the global model, generates compensation instructions such as incremental irrigation or suspended drainage, and updates local threshold parameters, such as adjusting the low-gradient threshold from 30% to 25%. The cloud also analyzes multiple emergency logs to extract abnormal patterns and optimize the federated learning model. These abnormal patterns include misjudgments caused by sensor drift.

[0034] Preferably, the data synchronization method of the intelligent cache queue includes:

[0035] The data in the real-time cache module is sorted in descending order of timeliness weight, with priority given to uploading environmental data that is close to expiration date;

[0036] The data in the persistent cache module is appended with a checksum during synchronization to ensure transmission integrity.

[0037] Preferably, the workflow of the federated learning framework is:

[0038] The cloud server periodically receives local model parameters and irrigation effect feedback from each edge node;

[0039] Differential privacy technology is used to aggregate data and train the global model, which is then encrypted and sent to the edge device to achieve model iteration and privacy protection.

[0040] Preferably, the verification method of the Chinese herbal medicine intelligent drip irrigation control system based on end-cloud collaboration includes:

[0041] Test command transmission delay in a simulated network fluctuation environment to ensure that the delay meets real-time requirements under the preset packet loss rate conditions;

[0042] Count the local decision accuracy in complex terrain planting scenarios to ensure that the irrigation control error does not exceed the preset threshold when the network is interrupted.

[0043] A verification method for a Chinese herbal medicine intelligent drip irrigation control system based on end-cloud collaboration ensures performance through simulation testing and field deployment. In a network fluctuation simulation platform, command transmission latency and cache synchronization efficiency are tested. For example, if the primary link is interrupted, switching to the backup link must be completed within 5 seconds. In actual deployments in complex terrain, local decision accuracy and irrigation deviation rate are calculated. If the deviation exceeds the limit, model calibration is triggered. Test data is encrypted and transmitted back to the cloud, forming a closed "test-feedback-optimization" loop to continuously improve system adaptability.

[0044] The present invention provides a Chinese herbal medicine intelligent drip irrigation control system based on end-cloud collaboration.

[0045] Beneficial effects:

[0046] (1) This intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration improves the accuracy and real-time performance of drip irrigation control in Chinese herbal medicine planting environments through an end-cloud collaborative architecture and a hierarchical decision-making mechanism. The local AI inference engine on the edge device, combined with a dynamic learning module, can autonomously generate optimization instructions when the network fluctuates, reducing dependence on real-time communication with the cloud, and ensuring the continuity and reliability of irrigation operations in complex terrain, including mountainous areas and terraced fields. The cloud aggregates multi-node data through a federated learning framework, continuously optimizes the global model, and encrypts the feedback to the edge, ensuring the intelligent level of model iteration while avoiding the risk of sensitive data leakage through privacy protection technology.

[0047] (2) This intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration achieves adaptive control capabilities in dynamic environments through the deep integration of a multimodal verification mechanism and an emergency rule library. The two-way verification process between the cloud and the edge significantly reduces the risk of misjudgment and avoids over-irrigation or drought stress. The two-way verification process includes reverse scoring of historical data and sensor fusion verification. The emergency rule library performs offline control based on gradient thresholds and periodic strategies when the network is interrupted, and implements cloud-based calibration through log feedback and compensation instructions to ensure the accuracy of long-term offline operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the overall framework of the present invention;

[0049] Figure 2 This is a control logic timing diagram of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] See also Figure 1 and Figure 2 The present invention provides a technical solution: a Chinese herbal medicine intelligent drip irrigation control system based on end-cloud collaboration, comprising:

[0052] Edge devices, deployed in Chinese herbal medicine planting areas, include multimodal sensor modules, communication modules, and irrigation actuators. They collect soil moisture, light intensity, and meteorological data in real time and control drip irrigation valves through irrigation actuators.

[0053] The cloud server is connected to the edge device through a wide area network and is used to store historical irrigation data, train the global optimization model, and issue control instructions;

[0054] The dynamic edge collaborative control module is integrated between the edge device and the cloud server, and includes an edge autonomous unit, a cloud optimization unit and a redundant communication unit; the edge autonomous unit generates preliminary control instructions based on local sensor data, the cloud optimization unit aggregates multi-node data through a federated learning framework to optimize the global model, and the redundant communication unit configures the main link and the backup link, and dynamically switches the communication channel according to the network signal strength.

[0055] It should be further explained that, during the specific implementation process, in the intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration, the edge device collects soil moisture, light intensity and meteorological data in real time through multimodal sensors, and inputs the data into the AI ​​inference engine. The engine generates preliminary control instructions based on the irrigation strategies stored in the historical rule base, where the irrigation strategies include water demand tables for different growth stages. For example, when soil moisture is lower than the historical average for the same period and the meteorological forecast shows no rainfall in the future, the engine matches the irrigation rules for the high water demand stage, automatically extending the drip irrigation time and increasing the water threshold. The dynamic learning module adjusts the decision weight based on local execution effect feedback. If the humidity recovery after a certain irrigation does not meet expectations, the application priority of the rule in similar scenarios is reduced, and the adaptability of the local model is gradually optimized. The local execution effect feedback includes the humidity recovery rate after irrigation.

[0056] The edge device also includes:

[0057] AI inference engine with built-in transfer learning algorithm generates irrigation duration and water volume thresholds based on local sensor data and historical rule base;

[0058] The emergency rule base stores soil moisture gradient thresholds and periodic water replenishment strategies, triggering offline irrigation control when the network is interrupted.

[0059] It should be further explained that, during the specific implementation process, in the intelligent drip irrigation control system for traditional Chinese medicine based on end-cloud collaboration, the AI ​​inference engine on the edge device side dynamically optimizes the local decision model through a transfer learning algorithm. Specifically, the engine receives soil moisture, light intensity, and meteorological data collected by multimodal sensors in real time, and combines the irrigation records and crop growth stage information stored in the historical rule base to generate preliminary irrigation duration and water volume thresholds. For example, when it is detected that the soil moisture is lower than the preset threshold and the light intensity is continuously high, the engine will give priority to calling the irrigation plan under similar environments in the historical rules, and adjust the decision weight through fine-tuning algorithms to adapt to the actual water demand characteristics of the current crop. If the network connection is normal, the engine will upload the locally generated instructions and sensor data to the cloud server synchronously to participate in the federated learning optimization of the global model; if the network signal is unstable or interrupted, the engine will rely entirely on the local model and rule base to ensure the continuous generation of irrigation instructions.

[0060] At the same time, the emergency rule library built into the edge device automatically activates upon network interruption. This rule library pre-stores various emergency control strategies, such as the soil moisture gradient threshold method and periodic water replenishment strategy. When the network interruption lasts for longer than a preset threshold of 30 minutes, the system matches the nearest moisture gradient interval based on real-time sensor data and triggers the corresponding irrigation action. For example, if soil moisture drops to the low to mid-range of the gradient threshold, short-term, high-frequency drip irrigation is initiated; if moisture falls within the critical drought range, long-term, deep water replenishment is implemented. After network restoration, the emergency rule library uploads local execution logs to the cloud server. The cloud server compares the log data with the global optimization model and generates compensation instructions to correct possible irrigation deviations. The local execution logs contain irrigation time, water volume, and environmental data. This mechanism not only ensures basic control capabilities during network interruptions, but also avoids the accumulation of errors caused by long-term offline periods through cloud-based calibration.

[0061] The hierarchical decision-making architecture of the dynamic edge collaborative control module includes:

[0062] The edge autonomous unit generates preliminary instructions by fusing real-time sensor data with a preset rule base through an AI inference engine, and adjusts decision weights based on a local dynamic learning module;

[0063] The cloud optimization unit periodically receives local model parameters and execution logs uploaded by each edge node, trains the global optimization model, and sends it to the edge device through an encrypted channel.

[0064] It should be further explained that, during the specific implementation process, in the intelligent drip irrigation control system for traditional Chinese medicine based on end-cloud collaboration, the hierarchical decision-making architecture achieves dynamic control through deep interaction between the edge autonomous unit and the cloud optimization unit. The edge autonomous unit uses the AI ​​inference engine to generate preliminary irrigation instructions based on real-time soil moisture, light intensity, and meteorological data, combined with preset irrigation strategies in the historical rule library, such as water demand tables for different growth stages. For example, when it is detected that the current soil moisture is lower than the historical average for the same period and the meteorological forecast shows no future rainfall, the engine will prioritize matching irrigation rules for high water demand stages, automatically extending the drip irrigation duration and increasing the water threshold. At the same time, the dynamic learning module adjusts the decision weight based on local execution effect feedback. For example, if the humidity recovery after a certain irrigation does not meet expectations, the application priority of the rule in similar scenarios will be reduced, gradually optimizing the adaptability of the local model. Among them, the local execution effect feedback includes the humidity recovery rate after irrigation.

[0065] The cloud-based optimization unit periodically receives local model parameters and irrigation logs from multiple edge nodes, including instruction execution results and environmental change data. It aggregates data through a federated learning framework and trains a global optimization model. For example, the cloud analyzes the differences in irrigation effects of different nodes under the same climatic conditions, extracts common rules, and generates universal optimization parameters, which are then sent to each edge device through an encrypted channel. After receiving the updated model, the edge gradually replaces inefficient rules in the local model, such as adjusting fixed thresholds to dynamic interval thresholds to adapt to regional microclimate differences. When the network signal is unstable, the cloud-based optimization unit adopts an asynchronous update mechanism, allowing the edge to receive model patches in batches after communication is restored, avoiding interruptions to model iterations due to network fluctuations. This layered architecture not only ensures the real-time decision-making capabilities of the edge, but also continuously improves the overall control accuracy of the system through global optimization in the cloud.

[0066] The redundant communication unit includes:

[0067] The main link uses NB-IoT to achieve wide-area coverage;

[0068] The backup link uses LoRa self-organizing network to form a short-range communication network;

[0069] When the signal strength of the main link is lower than the preset threshold, it automatically switches to the backup link, and after the network is restored, the cached data is synchronized to the cloud according to the timeliness weight.

[0070] It's important to further clarify that, in the specific implementation of the intelligent drip irrigation control system for traditional Chinese medicine based on end-cloud collaboration, redundant communication units dynamically switch between primary and backup links to ensure communication continuity during network fluctuations or outages. The primary link utilizes NB-IoT technology for wide-area coverage, transmitting sensor data and control commands in real time. When the system detects that the signal strength on the primary link consistently falls below a preset threshold, such as -110dBm, it automatically triggers a switchover to the backup link. The backup link utilizes LoRa self-organizing network technology to build a short-range communication network, forwarding data between nodes through multi-hop relays. For example, in terraced rice fields, adjacent edge devices relay data to the nearest network point via LoRa modules. Once the network is restored, the system asynchronously synchronizes cached data to the cloud based on its timeliness and priority. For example, high-frequency environmental data collected in real time is marked as highly time-sensitive, prioritizing data nearing its expiration date. Persistently stored model parameters are then uploaded in batches to minimize transient overload on the cloud.

[0071] In scenarios where the network is temporarily disrupted, such as when signal strength intermittently falls below a threshold but is not completely interrupted, the system adopts a hybrid transmission mode. This involves issuing critical control commands in real time via the backup link, while non-critical data is temporarily stored in a local cache and uploaded in batches after the primary link is restored. Critical control commands include emergency water replenishment signals, while non-critical data includes periodic environmental records. If the primary link outage lasts longer than a preset threshold (30 minutes), the backup link initiates an energy optimization strategy, reducing data transmission frequency and compressing data packets. For example, the soil moisture sampling interval is increased from every minute to every five minutes, while only transmitting critical fields, such as the moisture range code. Upon network recovery, the system first verifies the stability of the primary link. If signal strength remains above the threshold for a set period of time, such as five consecutive minutes, it gradually switches back to the primary link, simultaneously verifying and retransmitting the cached data to ensure data integrity. Furthermore, the communication unit has a built-in link status self-check module that periodically checks the availability of the primary and backup links. If a backup link node failure is detected, such as an unresponsive LoRa relay node, the data transmission path is replanned, for example, bypassing the failed node and directly connecting to the next available relay node.

[0072] The dynamic edge collaborative control module also includes:

[0073] Intelligent cache queue, including real-time cache module and persistent cache module;

[0074] The real-time cache module temporarily stores high-frequency environmental data and marks the timeliness weight;

[0075] The persistent cache module stores key control instructions and model parameters, and prioritizes uploading high-timeliness weighted data after network recovery.

[0076] It is important to further explain that, in the specific implementation, within the intelligent drip irrigation control system for traditional Chinese medicine based on end-cloud collaboration, an intelligent cache queue utilizes hierarchical storage and priority management to achieve efficient data synchronization in scenarios with network fluctuations. The real-time cache module temporarily stores frequently collected soil moisture, light intensity, and meteorological data, dynamically weighting the data based on its timeliness. For example, the current humidity change rate is assigned a high weight, while historical data from an hour ago is assigned a low weight. When the network signal is restored, the system prioritizes uploading high-weighted data to ensure the accuracy of real-time control. For example, soil moisture drop records within the past 10 minutes are prioritized, while routine monitoring data from stable environments is delayed. The persistent cache module stores key control instructions and model parameters, such as the global optimization model issued by federated learning and the emergency rule base at the edge. This data remains locally accessible during network outages and is uploaded in batches after network recovery to reduce cloud load. If insufficient bandwidth is detected during the initial network recovery, the system automatically activates data compression techniques, such as converting humidity data from floating-point to interval encoding to reduce the amount of data transmitted in a single transmission.

[0077] Under extreme conditions of persistent network instability, the intelligent cache queue activates an overflow protection mechanism. This includes overwriting older data in ascending order of weight when the real-time cached data reaches its storage limit, while retaining records of critical events with the highest weight, such as irrigation command execution failure alerts. For the persistent cache module, the system employs a differential synchronization strategy, uploading only incremental data since the last synchronization. For example, only newly added model parameter adjustment records are transmitted, rather than repeatedly sending the entire rule base. Furthermore, the cache queue has a built-in verification mechanism to verify data integrity before synchronization, such as using hash value comparisons to detect data tampering or loss during transmission. If verification fails, the system automatically triggers a data retransmission process and marks the anomalous node for further investigation. In the event of a complete network outage, the persistent cache module works in conjunction with the emergency rule base to ensure the continued generation and execution of local control commands. It also records detailed operation logs and, upon network restoration, performs a two-way calibration with the cloud to correct possible timestamp deviations or data conflicts.

[0078] The hierarchical decision-making architecture also includes:

[0079] Multimodal data verification unit, deployed in the cloud and on edge devices;

[0080] The cloud verification module generates a command rationality score by comparing the historical irrigation effect database with real-time weather forecast data;

[0081] The edge verification module verifies the matching degree of irrigation amount through correlation analysis between soil moisture and leaf transpiration rate. If the deviation exceeds the limit, it triggers local re-decision.

[0082] It should be further explained that, during implementation, within the intelligent drip irrigation control system for traditional Chinese medicine based on edge-cloud collaboration, a multimodal data verification unit ensures the legitimacy and execution accuracy of control instructions through a dual verification mechanism on both the cloud and edge. The cloud-based verification module reverse-scores irrigation instructions generated on the edge by accessing a database of historical irrigation results, which includes crop growth indicators under different humidity ranges, and real-time weather forecast data, which includes future rainfall probabilities and temperature trends. For example, if an edge instruction recommends high-volume irrigation during a heavy rain warning, the cloud-based verification module compares irrigation results under similar weather conditions with historical data. If such an operation is found to have previously resulted in root rot, the instruction is flagged as high-risk and a correction recommendation is generated and sent to the edge. The edge-based verification module verifies the execution results through multi-sensor data fusion analysis. For example, it correlates soil moisture sensor readings with leaf transpiration rate monitoring data. For example, if soil moisture significantly increases after irrigation but leaf transpiration rate does not increase simultaneously, the irrigation volume may be deemed excessive or unevenly distributed, triggering a local re-decision process.

[0083] In stable network scenarios, the cloud and edge verification modules synchronize data every 30 minutes. The cloud dynamically adjusts the scoring threshold based on global model updates. If the network signal fluctuates, the edge automatically increases the verification frequency, for example, to every 10 minutes, prioritizing local sensor data for rapid verification. If the cloud verification score repeatedly falls below the preset threshold, the system automatically freezes the current control model and initiates an emergency calibration mode. This involves the cloud sending basic rule templates, such as conservative irrigation thresholds, to the edge, triggering a manual intervention prompt. The edge then performs multiple rounds of local sensor data sampling and cross-validation, for example, by combining redundant humidity probe data with neighboring node monitoring results to eliminate interference from single-point sensor failures. If the edge verification module detects persistent deviations, such as a mismatch between humidity and transpiration rate after three consecutive irrigations, it switches to a backup decision model, such as differentiated irrigation rules based on crop variety, and flags the anomalous data for subsequent cloud analysis. Conflicting data discovered during the verification process is recorded in an anomaly log and uploaded to the cloud via a priority channel after network recovery, triggering targeted optimization iterations of the federated learning model. Conflicting data includes data that passed the cloud-based score but failed edge verification.

[0084] The working logic of the emergency rule base is:

[0085] When the duration of network interruption exceeds the preset threshold, the preset soil moisture gradient threshold method is called to generate emergency instructions;

[0086] After the network is restored, the cloud server generates compensation instructions based on the differences between the emergency execution log and the global model to correct the irrigation deviation.

[0087] It should be further explained that, in the specific implementation process, in the intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration, the emergency rule library ensures the continuity and reliability of irrigation control when the network is interrupted or the communication is unstable. When the system detects that the duration of the network interruption exceeds the preset threshold, such as 30 minutes, it automatically switches to offline mode and calls the pre-stored soil moisture gradient threshold rule library. For example, if the real-time monitoring shows that the soil moisture drops to the low gradient range, such as the humidity value is lower than 30%, the system triggers a short-term high-frequency drip irrigation strategy and performs water replenishment according to the preset irrigation time and water volume threshold; if the humidity is in the critical drought range, such as lower than 15%, long-term deep irrigation is started, and the single irrigation cycle is extended to avoid rapid evaporation of water. The emergency rule library also integrates periodic water replenishment strategies, such as automatically detecting humidity and performing water replenishment every 4 hours when there is no network connection to prevent crops from being damaged by sudden drought.

[0088] All emergency operations are recorded in a local execution log, including irrigation time, water volume, humidity curves, and ambient temperature data. After network restoration, the system first prioritizes the logs, such as drought warning events, and asynchronously uploads them to the cloud server via redundant communication links. The cloud compares the actual irrigation data in the logs with the predictions from the global optimization model, identifying deviations and generating compensation instructions. For example, if a region is under-irrigated due to conservative local decision-making during the emergency irrigation period, the cloud will issue incremental irrigation instructions based on meteorological data, including a subsequent rainfall forecast. If humidity levels in a local area exceed the specified limit due to over-irrigation, irrigation in that region will be suspended for the next cycle, triggering a drainage recommendation. After the compensation instructions are sent to the edge via an encrypted channel, the system automatically updates the threshold parameters in the local rule base, for example, adjusting the low-gradient threshold from 30% to 25%, to align with the cloud-optimized global policy. Furthermore, the cloud performs aggregate analysis of multiple emergency logs to identify common anomaly patterns, such as misjudgments caused by sensor data drift, and feeds these into the federated learning framework to optimize the robustness of the global model. This mechanism not only ensures the ability to autonomously control the system during network interruptions, but also avoids the cumulative errors that may be caused by long-term offline operations through dynamic calibration in the cloud.

[0089] The data synchronization methods of the intelligent cache queue include:

[0090] The data in the real-time cache module is sorted in descending order of timeliness weight, with priority given to uploading environmental data that is close to expiration date;

[0091] The data in the persistent cache module is appended with a checksum during synchronization to ensure transmission integrity.

[0092] It should be further explained that, during the specific implementation process, in the intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration, the data synchronization mechanism of the intelligent cache queue ensures the efficiency and integrity of data transmission through time-weighted sorting and checksum technology. The data in the real-time cache module is dynamically marked with time-weights according to the collection time and urgency. For example, the sudden drop in soil moisture at the current moment is marked with the highest weight, while routine monitoring data under a stable environment is marked with a lower weight. When the network is restored, the system prioritizes uploading high-time-weight data in descending order of weight, for example, prioritizing the transmission of humidity anomaly records within the past 10 minutes, rather than uploading all data in chronological order, to ensure timely response to key control instructions. If the network bandwidth is limited, such as a weak signal in the initial recovery period, the system automatically enables data compression and fragmented transmission strategies. For example, the humidity data is converted from continuous floating-point values ​​to interval encoding, such as low, medium, and high, to reduce the amount of data transmitted at a single time while retaining the semantic information of key fields.

[0093] The data in the persistent cache module is attached with a checksum when it is uploaded, such as a CRC checksum or a hash value. For example, a unique hash value is generated and embedded in the data packet header before the model parameters are synchronized. After the cloud receives the data, it first verifies whether the hash value matches. If the verification fails, a retransmission request is triggered to avoid damage to the model parameters due to transmission errors. In scenarios where the network fluctuates frequently, the persistent data adopts an incremental synchronization strategy, and only uploads the changes since the last successful synchronization. For example, only the newly added emergency rule adjustment records are transmitted, rather than repeatedly sending the complete rule base. If a data conflict is detected during the synchronization process, such as inconsistency between the local and cloud model versions, the system automatically enables the version comparison algorithm, retains the version with the latest timestamp, and marks the conflicting nodes for manual intervention.

[0094] For extreme network outage scenarios, the persistent cache module works in conjunction with the real-time cache module. This includes: when storage space approaches its upper limit, the system overwrites old data in ascending order of weight, but always retains the highest-weighted critical events, such as irrigation failure alarms or sensor failure records. For example, if cache space is insufficient, the system prioritizes deleting low-weight routine monitoring data from a week ago, while retaining all high-weighted abnormal events from the past 24 hours. After the network is restored, the system performs data synchronization in stages, first uploading high-weight real-time data and verifying its integrity, then retransmitting persistent data in batches while monitoring the cloud feedback status. If a batch of data fails to synchronize, such as due to secondary network fluctuations, subsequent transmissions are suspended and retry is attempted after the link stabilizes. In addition, the persistent cache module regularly self-checks data integrity, such as by scanning checksums to verify whether the stored data has been tampered with or damaged. If an anomaly is found, the data is restored from the backup copy and a fault log is generated for operation and maintenance analysis.

[0095] The workflow of the federated learning framework is:

[0096] The cloud server periodically receives local model parameters and irrigation effect feedback from each edge node;

[0097] Differential privacy technology is used to aggregate data and train the global model, which is then encrypted and sent to the edge device to achieve model iteration and privacy protection.

[0098] It should be further explained that, in the specific implementation process, in the intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration, the federated learning framework realizes the dynamic optimization of the global model through distributed data aggregation and privacy protection mechanism. The cloud server periodically receives local model parameters and irrigation effect feedback data uploaded by each edge device, such as the soil moisture change rate after irrigation and the crop growth status monitoring results, and anonymizes the data, such as removing the node location identifier and generalizing the timestamp information. Subsequently, the cloud uses differential privacy technology to aggregate multi-node data, such as adding random noise when updating the model parameters to ensure that the data of a single node cannot be reversed while retaining the accuracy of the global trend. The trained global optimization model is sent to each edge device through an encrypted channel. The edge end decrypts it locally and integrates it into the existing decision model. For example, the dynamic threshold rule sent by the cloud replaces the original fixed threshold to adapt to regional climate differences.

[0099] When the network signal is stable, the federated learning framework performs model iterations at a fixed interval, such as once daily. If high communication latency is detected for some nodes, asynchronous update mode is enabled, allowing lagging nodes to retransmit data and receive incremental model patches in subsequent cycles. For example, if an edge device is unable to participate in the current training round due to a network outage, the cloud will still generate a global model based on the received data and distribute it. Once the device recovers, it will upload its local data, and the cloud will then generate targeted model difference patches for synchronization. For scenarios with strict data privacy requirements, such as commercial planting data, the system supports customizable privacy protection levels. For example, by adjusting the noise intensity of differential privacy or limiting the scope of data aggregation, it achieves a dynamic balance between model accuracy and privacy security. If the cloud detects data anomalies from a node, such as repeated significant deviations from model predictions due to irrigation feedback, a data credibility assessment process is triggered. This involves comparing environmental data from neighboring nodes with irrigation results to determine whether the anomalous node's data distortion is due to sensor failure or human interference, and based on this, decides whether to include it in the next round of federated learning training. Furthermore, a version control mechanism is implemented when models are released, with multiple historical versions retained at the edge. If a new model performs poorly in real-world applications, such as a sudden increase in irrigation errors, it can be quickly rolled back to the previous stable version, with exception logs reported for cloud-based analysis and optimization. This process ensures the continuous evolution of the global model while also ensuring system reliability and compliance through privacy protection and fault tolerance mechanisms.

[0100] The verification method of the intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration includes:

[0101] Test command transmission delay in a simulated network fluctuation environment to ensure that the delay meets real-time requirements under the preset packet loss rate conditions;

[0102] Count the local decision accuracy in complex terrain planting scenarios to ensure that the irrigation control error does not exceed the preset threshold when the network is interrupted.

[0103] It should be further explained that, during the specific implementation process, the command delay is tested in a network fluctuation simulation platform to ensure that the delay is less than 2 seconds when the packet loss rate is 30%; the local decision accuracy is calculated in complex terrain planting scenarios to ensure that the irrigation amount error does not exceed ±5% when the network is interrupted.

[0104] In the intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration, the verification method ensures the real-time performance and control accuracy of the system through multi-scenario simulation and actual deployment testing. In the network fluctuation simulation platform, the system simulates signal interference of different intensities, such as intermittent packet loss or bandwidth limitation, and tests the delay performance of command transmission, including: when the packet loss rate exceeds the preset threshold, the system automatically switches to the backup communication link and records the switching time, and verifies whether the priority synchronization mechanism of the cache queue effectively guarantees the timely issuance of key commands. For example, when simulating a complete interruption of the main link, the system needs to complete the backup link switching within 5 seconds, and the humidity anomaly data marked as high-weight in the real-time cache should be uploaded first after the network is restored. During the test, if it is detected that the command delay exceeds the real-time requirements, such as the accumulation of delays due to link switching failure, the system automatically triggers the self-test process of the redundant communication unit, re-plans the data transmission path or restarts the faulty module.

[0105] In actual deployments in complex terrain planting scenarios, the system uses multi-node collaboration to verify the accuracy of local decisions. This includes: When a network is interrupted, edge devices generate irrigation instructions based on an emergency rule library and local sensor data, and record environmental changes after execution, such as the soil moisture recovery curve within 24 hours after irrigation. Once the network is restored, the system compares the local logs with the predicted data from the global cloud model to calculate the irrigation amount deviation rate.

[0106] For example, if a node performs irrigation in offline mode and the humidity increase is less than 10% of the expected value of the cloud model, it is judged as a slight deviation; if the deviation exceeds 20%, the model calibration process is triggered, and the cloud sends correction parameters and updates the local rule base. The test results are classified and counted according to preset thresholds, including: for key indicators, such as humidity control error, the system requires that more than 90% of the test samples meet the preset accuracy range; for non-key indicators, such as data transmission delay, a certain percentage of fluctuation tolerance is allowed. If the local decision accuracy fails to meet the standard in multiple consecutive tests, such as two out-of-tolerance tests in three tests, the system automatically activates the backup decision model and generates a fault diagnosis report, prompting the operation and maintenance personnel to check the sensor calibration or communication link stability. All test data is transmitted back to the cloud through an encrypted channel for iterative optimization of the federated learning model, forming a "test-feedback-optimization" closed loop to continuously improve the system's adaptability in complex environments.

[0107] A method for intelligent drip irrigation control of Chinese herbal medicine based on end-cloud collaboration includes the following steps:

[0108] Step S1: The edge device collects soil moisture, light intensity, and meteorological data in real time through multimodal sensors, generates preliminary irrigation instructions based on the historical rule base, and dynamically optimizes local decision weights through the AI ​​inference engine.

[0109] Step S2: The system monitors the communication status of the main link NB-IoT. If the signal strength is lower than the preset threshold, it automatically switches to the backup link LoRa self-organizing network to ensure the continuity of command transmission;

[0110] Step S3: The intelligent cache queue stores data in a hierarchical manner according to timeliness weight. After network recovery, high-weight real-time data, such as humidity drop records, is uploaded first, and persistent model parameters are uploaded asynchronously.

[0111] Step S4: The cloud server aggregates the local model parameters and irrigation logs of multiple nodes through the federated learning framework, trains the global optimization model, and encrypts it before sending it to the edge device.

[0112] Step S5: The multimodal data verification unit verifies the rationality of the instruction in both the cloud and the edge. If the cloud compares historical data and determines a high risk or the edge sensor fusion analysis finds an execution deviation, a re-decision or compensation instruction is triggered;

[0113] Step S6: When the network interruption exceeds the preset duration, the emergency rule library calls the soil moisture gradient threshold rule to perform offline irrigation and records the operation log. After the network is restored, it is uploaded to the cloud to generate a difference compensation instruction;

[0114] Step S7: Test instruction latency and cache synchronization efficiency on a network fluctuation simulation platform to verify the primary and backup link switching time and data priority upload logic;

[0115] Step S8: Calculate the local decision accuracy in actual deployment on complex terrain. If the irrigation amount deviation exceeds the limit, the cloud sends the model calibration parameters and updates the local rule base;

[0116] Step S9: The federated learning framework periodically iterates the global model, supports asynchronous updates and privacy protection, and retains historical versions at the edge to facilitate rapid rollback when the new model fails.

[0117] Step S10: Encrypt and transmit the test data back to the cloud to form a "test-feedback-optimization" closed loop, continuously improving the control accuracy and robustness of the system in a dynamic environment.

[0118] Through a collaborative end-to-end cloud architecture and a hierarchical decision-making mechanism, the precision and real-time performance of drip irrigation control in traditional Chinese medicine cultivation environments have been improved. The local AI inference engine on the edge device, combined with a dynamic learning module, can autonomously generate optimization instructions during network fluctuations, reducing reliance on real-time cloud communications and ensuring the continuity and reliability of irrigation operations in complex terrain, including mountainous areas and terraced fields. The cloud aggregates multi-node data through a federated learning framework, continuously optimizes the global model, and encrypts and feeds it back to the edge, ensuring the intelligent level of model iteration while mitigating the risk of sensitive data leakage through privacy protection technology.

[0119] The collaborative design of redundant communication links and intelligent cache queues effectively copes with scenarios where network signals are unstable or interrupted, ensuring efficient synchronization of critical data and low-latency transmission of instructions. This enhances the robustness of the overall system, keeps irrigation volume errors within preset thresholds, improves water resource utilization, and reduces crop losses caused by uneven irrigation.

[0120] Through the deep integration of a multimodal verification mechanism and an emergency rule base, adaptive control capabilities are achieved in dynamic environments. A bidirectional verification process between the cloud and the edge significantly reduces the risk of misjudgment and avoids over-irrigation or drought stress. This bidirectional verification process includes reverse scoring of historical data and sensor fusion verification. The emergency rule base performs offline control based on gradient thresholds and periodic policies in the event of network outages, and implements cloud-based calibration through log feedback and compensation instructions to ensure the accuracy of long-term offline operations.

[0121] Actual deployment verification has demonstrated high local decision-making accuracy and improved medicinal herb active ingredient content in unstable network environments. Furthermore, the federated learning framework supports asynchronous updates and version rollback mechanisms, balancing model optimization efficiency with system fault tolerance. Combined with a closed-loop "test-feedback-optimization" model, it continuously improves adaptability in complex planting scenarios, providing reliable technical support for intelligent and sustainable agricultural development.

[0122] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0123] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A Chinese herbal medicine intelligent drip irrigation control system based on end-cloud collaboration, characterized by: include: Edge devices, deployed in Chinese herbal medicine planting areas, include multimodal sensor modules, communication modules, and irrigation actuators. They collect soil moisture, light intensity, and meteorological data in real time and control drip irrigation valves through irrigation actuators. The cloud server is connected to the edge device through a wide area network and is used to store historical irrigation data, train the global optimization model, and issue control instructions; The dynamic edge collaborative control module is integrated between the edge device and the cloud server, including the edge autonomy unit, the cloud optimization unit and the redundant communication unit; The edge autonomous unit generates preliminary control instructions based on local sensor data, the cloud optimization unit aggregates multi-node data through a federated learning framework to optimize the global model, and the redundant communication unit configures the main link and the backup link, and dynamically switches the communication channel according to the network signal strength.

2. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 1 is characterized by: The edge device also includes: AI inference engine with built-in transfer learning algorithm generates irrigation duration and water volume thresholds based on local sensor data and historical rule base; The emergency rule base stores soil moisture gradient thresholds and periodic water replenishment strategies, triggering offline irrigation control when the network is interrupted.

3. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 1 is characterized by: The hierarchical decision-making architecture of the dynamic edge collaborative control module includes: The edge autonomous unit generates preliminary instructions by fusing real-time sensor data with a preset rule base through an AI inference engine, and adjusts decision weights based on a local dynamic learning module; The cloud optimization unit periodically receives local model parameters and execution logs uploaded by each edge node, trains the global optimization model, and sends it to the edge device through an encrypted channel.

4. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 1 is characterized by: The redundant communication unit comprises: The main link uses NB-IoT to achieve wide-area coverage; The backup link uses LoRa self-organizing network to form a short-range communication network; When the signal strength of the main link is lower than the preset threshold, it automatically switches to the backup link, and after the network is restored, the cached data is synchronized to the cloud according to the timeliness weight.

5. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 1 is characterized by: The dynamic edge collaborative control module also includes: Intelligent cache queue, including real-time cache module and persistent cache module; The real-time cache module temporarily stores high-frequency environmental data and marks the timeliness weight; The persistent cache module stores key control instructions and model parameters, and prioritizes uploading high-timeliness weighted data after network recovery.

6. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 3 is characterized by: The hierarchical decision-making architecture further includes: Multimodal data verification unit, deployed in the cloud and on edge devices; The cloud verification module generates a command rationality score by comparing the historical irrigation effect database with real-time weather forecast data; The edge verification module verifies the matching degree of irrigation amount through correlation analysis between soil moisture and leaf transpiration rate. If the deviation exceeds the limit, it triggers local re-decision.

7. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 2 is characterized by: The working logic of the emergency rule base is: When the duration of network interruption exceeds the preset threshold, the preset soil moisture gradient threshold method is called to generate emergency instructions; After the network is restored, the cloud server generates compensation instructions based on the differences between the emergency execution log and the global model to correct the irrigation deviation.

8. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 5 is characterized by: The data synchronization method of the intelligent cache queue includes: The data in the real-time cache module is sorted in descending order of timeliness weight, with priority given to uploading environmental data that is close to expiration date; The data in the persistent cache module is appended with a checksum during synchronization to ensure transmission integrity.

9. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 1 is characterized by: The workflow of the federated learning framework is as follows: The cloud server periodically receives local model parameters and irrigation effect feedback from each edge node; Differential privacy technology is used to aggregate data and train the global model, which is then encrypted and sent to the edge device to achieve model iteration and privacy protection.

10. The intelligent drip irrigation control system for Chinese herbal medicine based on end-cloud collaboration according to claim 1 is characterized by: The verification method of the Chinese herbal medicine intelligent drip irrigation control system based on end-cloud collaboration includes: Test command transmission delay in a simulated network fluctuation environment to ensure that the delay meets real-time requirements under the preset packet loss rate conditions; Count the local decision accuracy in complex terrain planting scenarios to ensure that the irrigation control error does not exceed the preset threshold when the network is interrupted.

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