A water pump device remote control method and system

Abstract

The application relates to the technical field of remote control, and provides a water pump equipment remote control method and system, which comprises the following steps: generating a pre-judgment emergency instruction containing operation parameters and priority identification based on external environment early warning information and system internal operation states; encoding and fragmenting the pre-judgment emergency instruction, and transmitting the pre-judgment emergency instruction through various communication channels; priority marking the pre-judgment emergency instruction in the transmission process, and adopting a transmission confirmation and retransmission mechanism; receiving the pre-judgment emergency instruction through various communication modules of a local control unit, and performing integrity checking, repetition checking and fragment recombination on the received pre-judgment emergency instruction; monitoring the communication link state by the local control unit, and identifying the priority of the pre-judgment emergency instruction when the communication link is interrupted, and preferentially executing high-priority instructions. The application can effectively deal with complex situations caused by the superposition of communication interruption and sudden environmental events, and ensures that high-priority emergency instructions are executed in time at critical moments.

Description

Technical Field

[0001] This application relates to the field of remote control technology for water pump equipment, and more specifically, to a method and system for remote control of water pump equipment. Background Technology

[0002] When faced with the combined effects of communication interruptions and sudden environmental events, the existing remote control systems for water pumps may experience autonomous operation mechanisms that deviate from actual needs, potentially leading to serious production accidents. For example, under a heavy rainfall warning, the frequency reduction command for drainage pumps issued by the central control station may fail to reach all local control units in a timely manner due to communication interruptions, causing some pond drainage pumps to continue operating at high frequencies, ultimately resulting in flooding and losses in aquaculture. Summary of the Invention

[0003] This application provides a method and system for remote control of water pump equipment to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application discloses a remote control method for a water pump device, comprising: External environmental early warning information is obtained through the central control station; Based on early warning information and the internal operating status of the system, predictive emergency instructions containing operating parameters and priority identifiers are generated. Predictive emergency commands are encoded and fragmented, and transmitted through multiple communication channels. During transmission, the predictive emergency commands are prioritized, and transmission confirmation and retransmission mechanisms are employed. The local control unit receives predictive emergency commands through various communication modules and performs integrity verification, repeatability checks, and fragment reconstruction on the received predictive emergency commands. The local control unit monitors the status of the communication link and, when the communication link is interrupted, identifies the priority of the predictive emergency commands and executes the higher priority commands first.

[0005] Secondly, this application also discloses a remote control system for a water pump, comprising: The information acquisition module is used to acquire early warning information about the external environment through the central control station; The instruction generation module is used to generate predictive emergency instructions containing operation parameters and priority identifiers based on early warning information and the internal operating status of the system. The instruction transmission module is used to encode and fragment predictive emergency instructions and transmit them through multiple communication channels. During the transmission process, the predictive emergency instructions are prioritized and a transmission confirmation and retransmission mechanism is adopted. The instruction receiving module is used to receive predictive emergency instructions through various communication modules of the local control unit, and to perform integrity verification, repeatability check and fragment reconstruction on the received predictive emergency instructions; The emergency execution module is used by the local control unit to monitor the status of the communication link and, when the communication link is interrupted, to identify the priority of the predictive emergency commands and execute the higher priority commands first.

[0006] Compared with the prior art, this application has at least the following beneficial effects: This application, by introducing predictive emergency commands, multi-channel transmission and priority marking, and an intelligent priority execution mechanism for the local control unit when communication is interrupted, ensures that critical emergency operations can be executed in a timely and accurate manner under extreme circumstances. This avoids catastrophic consequences caused by information delays or erroneous decisions, significantly improving the reliability, emergency response capability, and intelligence level of the remote control system for water pump equipment, and providing a safer and more efficient solution for aquaculture and other fields. Attached Figure Description

[0007] Figure 1 A flowchart illustrating a remote control method for a water pump device provided in this application; Figure 2 This is a schematic diagram of the structure of a remote control system for a water pump device provided in this application. Detailed Implementation

[0008] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0009] like Figure 1 As shown in the figure, this application proposes a remote control method for water pump equipment, including: External environmental early warning information is obtained through the central control station; Based on early warning information and the internal operating status of the system, predictive emergency instructions containing operating parameters and priority identifiers are generated. Predictive emergency commands are encoded and fragmented, and transmitted through multiple communication channels. During transmission, the predictive emergency commands are prioritized, and transmission confirmation and retransmission mechanisms are employed. The local control unit receives predictive emergency commands through various communication modules and performs integrity verification, repeatability checks, and fragment reconstruction on the received predictive emergency commands. The local control unit monitors the status of the communication link and, when the communication link is interrupted, identifies the priority of the predictive emergency commands and executes the higher priority commands first.

[0010] External environmental early warning information refers to warnings issued by meteorological departments, hydrological monitoring stations, or other professional institutions regarding natural disasters or abnormal situations that may affect the aquatic environment, such as heavy rainfall, typhoons, floods, and abnormally high temperatures. This information typically includes key data such as the warning level, affected area, and duration. The internal system operating status encompasses the real-time operating parameters of the pump equipment (such as operating frequency, power, and start / stop status), the communication module status of the local control unit, environmental data collected by sensors (such as water level, dissolved oxygen, and water temperature), and the health status of the equipment. Predictive emergency commands are instructions generated in advance by the central control station based on early warning information and the internal system status, guiding local pump equipment to perform emergency operations. Their core is predictability, i.e., intervention before the event occurs to avoid or mitigate potential risks. These commands include operating parameters (such as the operating frequency of the drainage pump and the start / stop time of the aerator pump) and priority indicators to indicate the urgency and importance of the command. The local control unit is an intelligent terminal deployed at the pump equipment site, responsible for receiving and executing commands from the central control station while simultaneously monitoring the local environment and equipment status. Multiple communication channels refer to different physical or logical channels used for command transmission, such as cellular networks (4G / 5G), satellite communication, LoRa, Wi-Fi, etc., which aim to improve the reliability and redundancy of communication.

[0011] The core of the remote control method for water pump equipment in this application is to ensure that critical predictive emergency commands can be received and executed by the local control unit in a timely and accurate manner in extreme situations such as communication link interruption.

[0012] Firstly, there are several ways to obtain external environmental early warning information. For example, the central control station can be configured with a dedicated information acquisition module. This module connects to the National Meteorological Administration's early warning release platform via an API interface to subscribe to and receive real-time meteorological early warning information for specific regions, such as heavy rainfall and typhoons. Alternatively, this module can use web scraping technology to periodically retrieve water level early warning data from the official websites of hydrological monitoring stations. As a preferred implementation method, the central control station can sign an agreement with a third-party professional environmental monitoring service provider, who can then directly push customized environmental early warning information to the central control station via a dedicated line or encrypted channel.

[0013] Secondly, the steps for generating predictive emergency commands containing operational parameters and priority identifiers based on early warning information and the system's internal operating status are also diverse in their implementation. For example, the central control station can have a built-in expert system that presets various environmental early warning scenarios and corresponding emergency response strategies. When a heavy rainfall warning is received, the expert system combines real-time water levels in ponds, pump operating loads, and other internal operating status data, and calculates the necessary adjustment frequency and duration of drainage pump operation through preset logical rules or machine learning models, generating corresponding operational parameters. Simultaneously, based on the urgency of the warning (e.g., a red warning) and its potential impact, the system automatically assigns a high-priority identifier to the command. As a preferred implementation, the system can employ a fuzzy logic-based decision engine, taking early warning information (e.g., rainfall amount, rainfall intensity) and internal operating status (e.g., current water level, pump health status) as input, deriving optimal operational parameters through fuzzy reasoning, and determining the command priority based on the confidence level of the reasoning results.

[0014] Furthermore, the process of encoding and fragmenting predictive emergency commands, transmitting them through multiple communication channels, prioritizing the commands, and employing transmission confirmation and retransmission mechanisms can be implemented as follows: The central control station first compresses and encrypts the generated predictive emergency commands to improve transmission efficiency and security. Then, the encoded commands are divided into several data packets or fragments. During transmission, the system can simultaneously send these data packets via cellular networks (such as 4G / 5G) and satellite communication channels. Each data packet's header embeds a priority marker, such as a specific byte sequence, indicating that the packet belongs to a high-priority command. To ensure reliable transmission, the central control station starts a timer, waiting for confirmation signals from local control units. If no confirmation is received within a preset time, or if the received confirmation indicates that the data packet is lost, the central control station will retransmit the unconfirmed data packet.

[0015] Next, regarding the steps of receiving preemptive emergency commands through multiple communication modules of the local control unit, and performing integrity verification, duplicate checking, and fragment reassembly on the received preemptive emergency commands, the local control unit can be equipped with multiple communication modules, such as a 4G / 5G module and a LoRa module. When data packets are received through these modules, the processor of the local control unit first checks the checksum of each data packet to ensure data integrity. Simultaneously, the system maintains a list of received data packets and performs duplicate checks on newly received data packets to avoid processing duplicate data. All data packets that pass the integrity verification and duplicate checking are sorted and reassembled according to their internal sequence numbers, ultimately restoring the complete preemptive emergency command.

[0016] Finally, regarding the local control unit's monitoring of the communication link status and, in the event of a communication link interruption, prioritizing and executing high-priority emergency commands, the local control unit continuously monitors the communication link status with the central control station, for example, by periodically sending heartbeat packets and checking responses. When heartbeat packets are lost consecutively for more than a preset threshold, or the communication module reports a link disconnection, the local control unit determines that the communication link is interrupted. At this time, the local control unit immediately scans its internal memory for all received but not yet executed emergency commands. By parsing the priority identifiers in the commands, the local control unit can identify which commands are high-priority. For example, if there is a high-priority command in the memory to reduce the frequency of the drain pump to 20Hz and a normal-priority command to periodically turn on the oxygen pump, the local control unit will prioritize executing the drain pump frequency reduction command to deal with the emergency.

[0017] The remote control method for water pumps described in this application works by constructing a highly resilient and intelligent decision-making control system to cope with complex and ever-changing environmental challenges. When external environmental early warning information (such as heavy rainfall forecasts) is acquired by the central control station, the system combines this information with internal data such as the current operating status of the water pumps and pond environmental data to conduct comprehensive analysis and prediction. Based on this analysis, the central control station can generate forward-looking emergency commands. These commands not only include specific operating parameters (such as adjusting the operating frequency of the drainage pumps) but are also assigned priority indicators to distinguish their urgency.

[0018] To ensure these critical instructions are reliably delivered to the local control unit, the central control station performs meticulous encoding and fragmentation of the instructions and utilizes multiple communication channels for redundant transmission. During transmission, the priority marker of the instruction is embedded in the data packet, and transmission acknowledgment and retransmission mechanisms ensure the integrity and reliability of data transmission. Even if some channels fail, the instructions can still arrive through other channels.

[0019] Upon receiving these instructions, the local control unit's various communication modules work together to receive data streams from different channels. Subsequently, the local control unit performs rigorous integrity checks, repeatability checks, and fragment reassembly on the received instructions to ensure their accuracy and completeness.

[0020] Most importantly, the local control unit possesses the ability to monitor the communication link status. Upon detecting a communication link interruption with the central control station, the local control unit does not simply maintain its original operating state but immediately activates its internal emergency response mechanism. At this point, it identifies the priority of pre-emptive emergency commands and executes the highest priority commands first. This means that even in the event of a loss of connection with the central control station, the local control unit can autonomously adjust the operation of the water pump equipment based on pre-received high-priority pre-emptive commands, thereby effectively responding to sudden environmental events. For example, when heavy rainfall causes a communication interruption, the local control unit can prioritize executing previously received commands to reduce the frequency of drainage pumps, preventing pond flooding, thus translating the central control station's pre-emptive decisions into concrete actions and effectively solving the problem of decision failure in traditional systems during communication interruptions.

[0021] The core of this application lies in the introduction of priority identification and multi-channel transmission mechanisms for predictive emergency commands, granting the local control unit the ability to identify priorities and execute high-priority commands first when the communication link is interrupted. By encoding, fragmenting, and prioritizing predictive emergency commands, and transmitting them through multiple communication channels, supplemented by transmission acknowledgment and retransmission mechanisms, the reliability and anti-interference capability of command transmission are greatly improved. Even if the main communication link is interrupted, the backup channel can provide transmission redundancy to ensure the delivery of critical commands.

[0022] More importantly, when communication is interrupted, the local control unit no longer blindly executes old instructions, but can intelligently identify and prioritize high-priority predictive emergency instructions. This mechanism ensures that the central control station's predictive decisions are effectively executed at the most critical moments, thus avoiding decision failures and potential production accidents caused by communication interruptions. For example, in a scenario where communication is interrupted due to a heavy rainfall warning, the method of this application can ensure that the local control unit prioritizes the execution of high-priority instructions for reducing the frequency of drainage pumps, effectively preventing pond flooding and significantly reducing aquaculture risks and economic losses. This proactive, priority-based emergency response mechanism is not available in existing technologies, and it greatly enhances the intelligence level and emergency response capabilities of remote control systems for water pump equipment.

[0023] In some embodiments, the steps of encoding and fragmenting the predictive emergency instructions, transmitting them through multiple communication channels, prioritizing the predictive emergency instructions during transmission, and employing transmission confirmation and retransmission mechanisms further include: The central control station adjusts the redundancy coding strength and fragment size of the predictive emergency instructions based on their urgency and length. The central control station evaluates the real-time quality of each communication channel, including signal strength, data transmission delay, and packet loss rate. Based on real-time quality, the central control station selects one or more communication channel combinations to transmit predictive emergency command data packets; When the real-time quality of the main communication channel drops below a preset threshold, or when there is continuous data packet loss, the central control station will switch the transmission of predictive emergency commands to the backup communication channel. Priority weight values ​​are embedded in the predictive emergency instruction data packets, and the priority of the predictive emergency instructions is calculated in combination with the current timestamp; The local control unit confirms the received predictive emergency command fragments through a cross-channel collaborative confirmation mechanism; The central control station stopped retransmitting confirmed predictive emergency command segments.

[0024] Specifically, the central control station dynamically adjusts the redundancy coding strength and fragment size based on the urgency and length of the anticipated emergency commands. For example, for commands with high urgency and short content, a higher redundancy coding strength can be used to enhance anti-interference capabilities, and the fragment size can be reduced to speed up transmission and reduce the risk of fragment loss; while for commands with low urgency but long content, the redundancy coding strength can be appropriately reduced and the fragment size increased to improve transmission efficiency.

[0025] The central control station continuously evaluates the real-time quality of each communication channel. Real-time quality can be understood as an indicator of channel transmission performance, specifically including signal strength, data transmission delay, and packet loss rate. Signal strength reflects the physical connection quality of the communication link; data transmission delay represents the time required for data to travel from the sender to the receiver; and packet loss rate indicates the proportion of data that fails to reach the receiver due to various reasons during transmission. Based on these real-time quality parameters, the central control station can intelligently select one or more communication channel combinations for the transmission of predictive emergency command data packets. For example, it can select a channel with high current signal strength, low delay, and low packet loss rate as the primary transmission path, or select multiple channels for parallel transmission to improve reliability based on the importance of the command.

[0026] When the real-time quality of the primary communication channel drops below a preset threshold, or when continuous data packet loss occurs, the central control station switches the transmission of predictive emergency commands to the backup communication channel. This ensures that even if the primary channel performance is poor, emergency commands can continue to be transmitted through other available channels, avoiding transmission interruption.

[0027] Priority weight values ​​are embedded in the predictive emergency instruction data packets, and their priority is calculated by combining this with the current timestamp. The priority weight values ​​can be pre-set based on the type of instruction (e.g., flood warning, equipment failure) and its urgency, while the timestamp reflects the timeliness of the instruction. This ensures that high-priority instructions receive priority during transmission and processing.

[0028] The local control unit confirms the received predictive emergency command segments through a cross-channel collaborative confirmation mechanism. This means that the local control unit not only confirms reception on a single channel but also integrates segments from different channels to ensure command integrity. Once the local control unit successfully confirms the received command segments, the central control station will stop retransmitting these confirmed segments, thereby effectively reducing network bandwidth consumption and improving transmission efficiency.

[0029] This application introduces a mechanism for dynamically adjusting redundancy coding strength and fragment size, enabling the transmission of predictive emergency commands to be optimized based on their characteristics (urgency and content length). For example, for high-urgency commands, increasing redundancy coding and reducing fragmentation effectively improves data resilience and transmission speed in harsh communication environments, ensuring timely delivery of commands. Simultaneously, the central control station continuously evaluates the real-time quality of each communication channel, including signal strength, data transmission latency, and packet loss rate, allowing the system to monitor changes in the communication environment in real time. Based on these real-time evaluation results, the system can intelligently select the optimal communication channel combination for data transmission, avoiding transmission failures or delays due to poor channel quality. Furthermore, when the performance of the primary communication channel degrades, the system can quickly switch to a backup channel, forming an adaptive communication link guarantee mechanism that greatly enhances the robustness of emergency command transmission. In addition, by embedding priority weight values ​​in data packets and combining them with timestamps to calculate priority, it ensures that high-priority emergency commands are processed and transmitted first when multiple commands are transmitted concurrently, guaranteeing the timeliness of critical commands. The cross-channel collaborative confirmation mechanism of the local control unit not only improves the reliability of command segment reception, but also optimizes the efficiency of network resource utilization by timely feedback to the central control station, enabling it to stop unnecessary retransmissions.

[0030] In some embodiments, the step of the aforementioned local control unit monitoring the communication link status and, when the communication link is interrupted, identifying the priority of predictive emergency commands and prioritizing the execution of high-priority commands further includes: During the execution of high-priority emergency commands, the local control unit continuously collects environmental data such as pond water level, dissolved oxygen concentration, and water temperature. The local control unit calculates the rate of water level rise and the trend of dissolved oxygen changes based on the collected environmental data; The local control unit compares the calculated rate of water level rise and dissolved oxygen change trend with the preset emergency threshold. When the water level rises faster than a preset threshold, or the dissolved oxygen concentration falls below a preset threshold, the local control unit triggers an adjustment of emergency operation parameters. The local control unit recalculates the operating frequency and duration of the drainage pump and the oxygenation pump based on the current water level, the rate of water level rise, the dissolved oxygen concentration, and the preset pump operating characteristics. The local control unit adjusts the control signals of the drainage pump and oxygenation pump based on the recalculated operating frequency and duration to optimize emergency operations.

[0031] Specifically, during the execution of high-priority emergency commands, the local control unit continuously collects environmental data such as pond water level, dissolved oxygen concentration, and water temperature to obtain real-time and comprehensive environmental information. These environmental parameters are key indicators for assessing the ecological health and potential risks of the pond. Among them, pond water level is directly related to flood control and drainage needs, dissolved oxygen concentration is crucial for the survival of aquatic organisms, and water temperature affects the overall metabolic activities of the aquatic ecosystem. Continuous data collection ensures that the system always has the latest environmental dynamics during emergency response.

[0032] The local control unit calculates the rate of water level rise and dissolved oxygen trends based on collected environmental data. Relying solely on instantaneous environmental data may not accurately determine the severity and direction of an emergency. For example, even if the current water level has not reached the warning line, a rapid rise indicates potential risk. Similarly, a rapid decline in dissolved oxygen is a better indicator of acute threats to aquatic life than a single low value. Therefore, calculating these dynamic trends allows for a more accurate assessment of the current environmental evolution, providing a more forward-looking basis for subsequent decision-making.

[0033] The local control unit compares the calculated rate of water level rise and dissolved oxygen change trends with preset emergency thresholds. These emergency thresholds are set based on the actual conditions of the pond, aquaculture needs, and historical experience, representing the critical points where emergency measures are required. By comparing with these thresholds, the system can automatically determine whether the current environmental conditions have reached or exceeded the level of urgency requiring intervention.

[0034] When the water level rises faster than a preset threshold, or the dissolved oxygen concentration falls below a preset threshold, the local control unit triggers adjustments to emergency operating parameters. This means that once environmental conditions deteriorate and reach a warning level, the system will no longer adhere to the original emergency command parameters but will instead activate a dynamic adjustment mechanism to adapt to new environmental challenges.

[0035] The local control unit recalculates the operating frequency and duration of the drainage pump and aerator pump based on the current water level, rate of rise, dissolved oxygen concentration, and preset pump operating characteristics. This step is crucial for optimizing emergency operations. It comprehensively considers multi-dimensional real-time environmental data (current status and trends) as well as the performance parameters of the pumps themselves to ensure that the calculated operating parameters most effectively address the current emergency. For example, when the water level rises rapidly, it may be necessary to increase the operating frequency and duration of the drainage pump; while when dissolved oxygen drops sharply, the operating intensity of the aerator pump needs to be increased.

[0036] The local control unit adjusts the control signals of the drainage pump and oxygenation pump based on the recalculated operating frequency and duration to optimize emergency operations. In this way, the pump equipment no longer simply executes a fixed command, but can intelligently respond to real-time environmental changes, thereby achieving more precise and efficient emergency management.

[0037] This application effectively addresses the lack of real-time adaptability in emergency command execution in the basic solution by introducing a continuous environmental data acquisition and dynamic parameter adjustment mechanism during communication link interruptions when the local control unit prioritizes the execution of high-priority emergency commands. Specifically, when communication is interrupted, the local control unit not only executes predictive emergency commands but also continuously monitors key environmental parameters such as pond water level, dissolved oxygen concentration, and water temperature. By analyzing this real-time data, the system calculates the rate of water level rise and dissolved oxygen change trends, thereby dynamically assessing the evolution of the emergency situation. Once these dynamic indicators are compared with preset emergency thresholds and the environmental conditions are found to be continuously deteriorating or exceeding safe limits, the local control unit triggers adjustments to emergency operation parameters. At this time, the system comprehensively considers the current water level, rate of water level rise, dissolved oxygen concentration, and the operating characteristics of the water pumps, intelligently recalculating the optimal operating frequency and duration of the drainage pump and aerator pump. Thus, the local control unit can promptly adjust the pump control signals, ensuring that emergency operations accurately match the constantly changing environmental demands, avoiding response delays or over-intervention caused by rigid command parameters, thereby significantly improving the effectiveness and accuracy of emergency response.

[0038] In some embodiments, the step of continuously collecting environmental data such as pond water level, dissolved oxygen concentration, and water temperature during the execution of high-priority emergency commands by the aforementioned local control unit includes: The local control unit redundantly collects data on pond water level, dissolved oxygen concentration, and water temperature using multiple physically distributed sensors of the same type. The local control unit performs real-time consistency checks on the raw data from various sensors of the same type. When a significant deviation is found between the data from sensors of the same type, it is marked as a data anomaly. For the marked data anomalies, the local control unit selects the corresponding sensor data calibration strategy based on the preset environmental event type; When the environmental event is heavy rainfall, the local control unit combines the water surface fluctuation intensity information to perform multi-point sampling averaging and trend smoothing processing on the water level sensor data; When the environmental event is an abnormally high temperature, the local control unit combines the water stratification information and performs a depth-weighted average processing on the dissolved oxygen and temperature sensor data; The local control unit uses the calibrated environmental data as the basis for dynamically adjusting subsequent emergency operation parameters.

[0039] Specifically, redundant data acquisition of pond water level, dissolved oxygen concentration, and water temperature is achieved through multiple physically distributed sensors of the same type, aiming to improve the reliability and accuracy of data collection. When one sensor malfunctions or experiences localized interference, other sensors of the same type can still provide valid data, thus avoiding data loss or errors caused by single-point failures. These sensors can be deployed at different depths and areas of the pond to obtain more comprehensive environmental information. The local control unit performs real-time consistency checks on the raw data from each sensor of the same type, comparing data of the same type from different sensors. For example, the readings of multiple water level sensors at the same time should be consistent within a reasonable range. When significant deviations are found between the data, the data is marked as abnormal, indicating possible sensor malfunction, localized environmental anomalies, or data transmission errors. In practical applications, for the marked data anomalies, the local control unit selects the corresponding sensor data calibration strategy based on preset environmental event types. Environmental event types can include heavy rainfall, abnormally high temperatures, and water pollution. Different event types may have different effects on sensor data, thus requiring targeted calibration methods. Furthermore, when the environmental event is heavy rainfall, the water surface will fluctuate violently, potentially causing unstable water level sensor readings. At this point, the local control unit combines information on water surface fluctuation intensity to perform multi-point sampling averaging and trend smoothing on the water level sensor data. Multi-point sampling averaging effectively eliminates errors caused by instantaneous fluctuations, while trend smoothing helps identify the true trend of water level changes, rather than short-term random disturbances. Furthermore, when the environmental event is an abnormally high temperature, the water body may exhibit significant stratification, leading to substantial differences in dissolved oxygen concentration and water temperature at different depths. In this case, the local control unit combines water stratification information to perform depth-weighted averaging on the dissolved oxygen and temperature sensor data. This means that sensor data at different depths will be assigned different weights based on their representativeness or importance in the water body, thus more accurately reflecting the dissolved oxygen and temperature conditions of the pond as a whole or in specific key areas. Finally, the local control unit uses the calibrated and processed environmental data as the basis for dynamic adjustments to subsequent emergency operating parameters. This means that only verified and corrected reliable data will be used to calculate the rate of water level rise, dissolved oxygen change trends, and subsequent pump operating frequency and duration, ensuring the scientific validity and effectiveness of emergency decisions.

[0040] This application effectively addresses the accuracy and reliability issues that may arise from single or unprocessed sensor data by introducing redundant acquisition, real-time consistency checks, and calibration strategies based on environmental event types. Specifically, redundant acquisition from multiple physically located, similar sensors provides multi-source data, ensuring data continuity and availability even when a single sensor malfunctions or experiences localized anomalies. The real-time consistency check mechanism acts as the first line of defense, promptly identifying and flagging abnormal data to prevent misuse. Furthermore, specific calibration strategies are employed for different environmental events (such as heavy rainfall and abnormally high temperatures). For example, multi-point sampling averaging and trend smoothing are used for water level data, and depth-weighted averaging is applied to dissolved oxygen and temperature data. This effectively eliminates interference from specific environmental factors on sensor readings, resulting in environmental parameters closer to the true values. It is precisely these refined data processing and calibration processes that enable the local control unit to acquire highly accurate and reliable environmental data, providing a solid data foundation for the dynamic adjustment of subsequent emergency operating parameters.

[0041] In some embodiments, the step of continuously collecting environmental data such as pond water level, dissolved oxygen concentration, and water temperature during the execution of high-priority emergency commands by the aforementioned local control unit further includes: The local control unit deploys multiple water level sensors, dissolved oxygen sensors, temperature sensors, and vibration sensors in different areas of the pond; The local control unit detects the vibration intensity generated by the pump body when the drainage pump or oxygenation pump is started; When the vibration intensity exceeds a preset threshold, the local control unit initiates the interference cancellation program; Interference cancellation procedures include: The local control unit constructs the area affected by water surface fluctuations based on the pump's operating status and the pond's physical characteristics; The local control unit performs multi-point sampling and averaging of water level sensor data outside the area affected by water surface fluctuations, and combines it with historical water level data before the pump operates to estimate the true water level of the pond; The local control unit estimates the range of the pump's impact on water bubbles based on the pump type and operating frequency. The local control unit performs a weighted average of dissolved oxygen sensor data outside the bubble's influence range and combines it with historical dissolved oxygen data before the pump starts operating to estimate the true dissolved oxygen concentration in the pond. The local control unit performs a layered weighted average of temperature sensor data at different depths based on the pump's effect on water mixing, and combines this with historical temperature data before the pump started operating to estimate the true water temperature of the pond. The local control unit will use the actual water level, actual dissolved oxygen concentration, and actual water temperature data obtained after interference elimination processing as the basis for dynamic adjustment of subsequent emergency operation parameters.

[0042] Specifically, the local control unit deploys multiple water level sensors, dissolved oxygen sensors, temperature sensors, and vibration sensors in different areas of the pond. The purpose is to comprehensively and redundantly monitor the pond environment through the coordinated operation of multiple points and types of sensors, providing a data foundation for subsequent interference elimination. Among these, the vibration sensor is specifically used to monitor the real-time operating status of the drainage pump or aerator pump and the intensity of its generated mechanical vibration, serving as a direct basis for determining whether the pump has started and its potential interference with environmental data.

[0043] When the local control unit detects the start of the drainage pump or aerator pump, it continuously monitors the vibration intensity generated by the pump operation. Once the vibration intensity exceeds a preset threshold, it indicates that the pump operation has significantly interfered with the acquisition of pond environmental data. At this point, the local control unit will immediately initiate the interference cancellation program. This preset threshold can be experimentally set and calibrated based on the power of different pumps, their installation location, and the physical characteristics of the pond to ensure that interference cancellation is triggered promptly when the pump begins to have a significant impact on sensor data.

[0044] The interference cancellation program aims to eliminate interference from pump operation on environmental data and estimate the true environmental parameters of the pond through intelligent algorithms and multi-sensor data fusion. Specifically: First, the local control unit constructs a model of the area affected by water surface fluctuations based on the current operating status of the pump (e.g., whether it is a drainage pump or an aerator pump, its operating power, flow rate, etc.) and the physical characteristics of the pond (e.g., pond size, shape, water depth, water flow pattern, etc.). This model can predict the range and intensity of the disturbance to the water surface caused by the pump operation.

[0045] Secondly, for water level data, the local control unit selects water level sensor data located outside the area affected by water surface fluctuations for multi-point sampling and averaging to obtain relatively stable water level measurements. Simultaneously, by combining historical water level data before pump operation and using trend analysis or predictive models, the actual water level of the pond is estimated, thereby effectively avoiding water level measurement errors in areas directly disturbed by the pump.

[0046] Secondly, regarding dissolved oxygen data, the local control unit estimates the range of influence of the pump on the water bubbles based on the pump type (e.g., an aerator pump produces a large number of bubbles) and operating frequency. Subsequently, the local control unit performs a weighted average on the dissolved oxygen sensor data outside the bubble influence range to reduce the impact of localized bubbles on dissolved oxygen measurements. Similarly, by combining historical dissolved oxygen data before the pump started operating, the true dissolved oxygen concentration of the pond is estimated.

[0047] Finally, regarding water temperature data, considering that pump operation may cause water mixing and affect the temperature distribution at different depths, the local control unit performs a stratified weighted average of the temperature sensor data at different depths based on the degree of pump mixing. This aims to more accurately reflect the true temperature of different water layers and, combined with historical temperature data before pump operation, to estimate the true water temperature of the pond.

[0048] Therefore, the local control unit uses the water level, dissolved oxygen concentration, and water temperature data, after the aforementioned interference elimination processing, as the basis for dynamically adjusting subsequent emergency operation parameters. This means that even during pump operation, the system can obtain highly accurate and reliable environmental data, providing a solid foundation for precise emergency decision-making.

[0049] This application achieves real-time sensing of the pump's operating status and its disturbances by deploying multiple types of sensors, particularly vibration sensors, in different areas of the pond. When the vibration intensity exceeds a preset threshold, the system can promptly identify potential interference from the pump to environmental data collection and initiate a dedicated interference cancellation program. This program intelligently filters and utilizes sensor data from areas not directly affected by water surface fluctuations and air bubbles by constructing the affected areas and bubble ranges. It combines this data with historical data from before pump operation, performing multi-point sampling averaging, weighted averaging, and stratified weighted averaging to effectively filter out instantaneous disturbances and local deviations caused by pump operation. It is precisely this mechanism of multi-sensor collaboration, intelligent interference identification, and targeted data processing that enables the system to calculate the pond's true water level, dissolved oxygen concentration, and water temperature even under the complex environment of pump operation, overcoming the limitations of poor data accuracy associated with single or simple sensor deployments during pump operation.

[0050] The following is a specific example to illustrate this.

[0051] Suppose that during a heavy rainfall event, the local control unit receives a high-priority emergency command to activate a drainage pump to prevent the pond water level from rising too high. Once the drainage pump starts, the vibrations it generates are detected in real time by multiple vibration sensors within the pond. The local control unit detects that the vibration intensity is rapidly increasing and exceeding a preset threshold, then determines that the drainage pump has begun to interfere with environmental data acquisition and immediately initiates an interference cancellation procedure.

[0052] Specifically, the local control unit first constructs a water surface fluctuation influence area centered on the drainage pump, based on the model and power of the drainage pump and the geometry of the pond. Then, the system selects multiple water level sensors located outside this influence area for real-time data acquisition and performs multi-point sampling averaging. Simultaneously, by combining historical water level data from the hour prior to the drainage pump's activation, a linear regression model is used to calculate the current true water level of the pond.

[0053] Regarding dissolved oxygen concentration, since the operation of the drainage pump may cause localized turbulence in the water and generate a small number of bubbles, the local control unit estimates the range of influence of these bubbles. The system then performs a weighted average of dissolved oxygen sensor data outside this range and combines it with historical dissolved oxygen data before the drainage pump started to calculate the true dissolved oxygen concentration in the pond.

[0054] Regarding water temperature, considering that the operation of the drainage pump may promote the mixing of the upper and lower layers of water, the local control unit will perform a layered weighted average of the temperature sensor data at different depths, and combine it with historical temperature data to calculate the true water temperature of the pond.

[0055] Ultimately, the local control unit uses this more accurate water level, dissolved oxygen concentration, and water temperature data, processed to eliminate interference, as the basis for adjusting the operating frequency and duration of the drainage pumps. For example, if the actual water level is still rising rapidly, the system may further increase the operating frequency of the drainage pumps; if the dissolved oxygen concentration shows a downward trend during drainage, the system may activate the aerator pump to supplement oxygenation during drainage breaks. In this way, even under complex pump operating conditions, it ensures that the adjustment of emergency operating parameters is based on real and reliable environmental data, thereby achieving more precise and efficient emergency management.

[0056] In some embodiments, the step of deploying multiple water level sensors, dissolved oxygen sensors, temperature sensors, and vibration sensors in different areas of the pond by the local control unit further includes: The local control unit pre-calibrates the raw measurement data of each sensor based on the environmental differences of the sensor deployment location; The local control unit continuously monitors the measured values ​​of each sensor and performs real-time consistency checks on them; When a significant deviation is detected between data from sensors of the same type, the local control unit adjusts the weights of each sensor based on the magnitude and duration of the deviation. The local control unit combines the pond's geographical information and water flow distribution to construct a virtual environment partition; The local control unit performs a weighted average of the environmental parameters within each zone based on the calibration data and weights of the sensors within that zone. The local control unit smooths the environmental parameters of adjacent zones and generates an overall environmental parameter distribution map of the pond. The local control unit uses the overall environmental parameter distribution map of the pond as the basis for dynamic adjustment of subsequent emergency operation parameters.

[0057] Specifically, when deploying sensors, the local control unit records the exact location information of each sensor, including but not limited to geographic coordinates, water depth, whether it is exposed to sunlight, and whether there is a water flow nearby. Based on these environmental differences, the raw measurement data of each sensor is pre-calibrated to compensate for systematic biases that may arise due to different deployment environments. For example, the readings of a temperature sensor deployed in deep water may need to be corrected according to the water depth gradient.

[0058] The local control unit continuously monitors the measurements from all sensors of the same type in real time. For example, data from all water level sensors are compared in real time to check their consistency. When significant deviations are found between data from sensors of the same type, such as a water level sensor reading being much higher or lower than other sensors of the same type, the local control unit dynamically adjusts the weights of these sensors based on the magnitude and duration of the deviation. The larger the deviation or the longer the duration, the lower the weight of that sensor may be, or even its data may be temporarily excluded, to reduce the impact of abnormal data on the overall judgment.

[0059] To more comprehensively reflect the pond's environmental conditions, the local control unit combines the pond's geographical information (such as shape, size, and depth distribution) and water flow distribution (such as the location of inlet and outlet, and water flow caused by pump operation) to construct a virtual environmental partition. This virtual environmental partition divides the pond into several areas with relatively uniform environmental characteristics.

[0060] Furthermore, the local control unit calculates a weighted average of the environmental parameters within each zone based on the pre-calibration data and dynamically adjusted weights of the sensors contained within that zone, thereby obtaining more accurate environmental parameter values ​​for that zone. For example, if a zone contains three water level sensors with different weights, the local control unit will calculate the weighted average water level for that zone based on these weights.

[0061] Furthermore, to eliminate potential data jumps caused by partition boundaries and generate a continuous, smooth overall pond environmental parameter distribution map, the local control unit smooths the environmental parameters of adjacent partitions. For example, interpolation algorithms or moving averages can be used to ensure a natural transition between environmental parameters across different partitions. The resulting overall pond environmental parameter distribution map intuitively and accurately reflects the real-time distribution of water level, dissolved oxygen concentration, and water temperature throughout the pond, serving as a reliable basis for dynamic adjustments to subsequent emergency operation parameters.

[0062] This application effectively addresses the environmental differences, local anomalies, and data inconsistencies that may exist in multi-sensor data in the basic scheme by introducing mechanisms such as sensor pre-calibration, real-time consistency checks, dynamic weight adjustment, virtual environment zoning, and weighted averaging and smoothing of zoning data. Specifically, the pre-calibration mechanism compensates for systematic errors caused by different sensor deployment environments, ensuring the initial accuracy of each sensor's data; the real-time consistency check and dynamic weight adjustment mechanisms can promptly detect and mitigate the impact of abnormal or drifting sensor data, thereby improving data reliability; and the virtual environment zoning, weighted averaging within zoning, and smoothing of adjacent zoning can integrate discrete sensor data into a continuous and comprehensive distribution map of the pond's overall environmental parameters, more accurately reflecting the pond's true environmental conditions. It is precisely because of the synergistic effect of these mechanisms that the local control unit can obtain more accurate and reliable environmental data, providing a solid foundation for the dynamic adjustment of subsequent emergency operation parameters.

[0063] The following is a specific example to illustrate this.

[0064] Imagine a large aquaculture pond where depth, light, and water flow conditions vary significantly across different areas. A local control unit deploys water level sensors, dissolved oxygen sensors, and temperature sensors at multiple locations within the pond, including the shallow water area, deep water area, near the inlet, and near the outlet.

[0065] First, the local control unit pre-calibrates the raw measurement data of these sensors based on their deployment location information. For example, the temperature sensor deployed in a sunlit area may have a reading that is too high due to solar radiation, and the local control unit will correct it according to a preset radiation compensation model; the dissolved oxygen sensor deployed in a fast-flowing area may need to have its reading calibrated according to the water flow rate.

[0066] Secondly, during system operation, the local control unit continuously monitors the data from all water level sensors. If it detects a sudden and significant drop in the reading of a water level sensor located in a shallow water area, while the readings of other water level sensors remain stable, the local control unit will mark it as abnormal data and reduce the weight of that sensor based on the magnitude and duration of the deviation.

[0067] Next, the local control unit, combining the pond's CAD drawings and water flow simulation data, divides the pond into several virtual zones, such as shallow zone A, deep zone B, and inlet zone C. For each zone, the local control unit collects pre-calibration data from all sensors within that zone and, based on its dynamically adjusted weights, calculates the weighted average water level, dissolved oxygen concentration, and water temperature for that zone.

[0068] Finally, to obtain a continuous distribution of environmental parameters across the entire pond, the local control unit smooths the environmental parameters of adjacent zones (e.g., shallow water zone A and deep water zone B), generating an overall distribution map of water level, dissolved oxygen, and temperature for the entire pond. This map clearly shows the environmental parameter status of each area of ​​the pond, such as which area is experiencing a rapid rise in water level and which area has low dissolved oxygen concentration. The local control unit uses this high-precision, high-reliability environmental parameter distribution map as its core basis to dynamically adjust the operating strategies of the drainage pumps and aerators. For example, it activates drainage pumps in areas with the fastest water level rises or increases the operating frequency of aerators in areas with the lowest dissolved oxygen, thereby achieving more precise and effective emergency control.

[0069] In some embodiments, the step of pre-calibrating the raw measurement data of each sensor based on environmental differences at the sensor deployment location further includes: The local control unit periodically triggers the sensor self-test program; The self-test program sends a reference signal to the sensor and records the sensor's response output; The local control unit compares the response output with the standard response curve of the sensor at the factory and calculates the response deviation; When the response deviation exceeds the preset threshold, the local control unit adjusts the sensor's pre-calibration parameters according to the magnitude and type of the response deviation to compensate for the drift of the sensor's own characteristics. The local control unit applies the adjusted pre-calibration parameters to subsequent raw measurement data.

[0070] Specifically, a sensor self-test program refers to a pre-defined diagnostic process designed to assess the sensor's current operating status and measurement performance. This program can be automatically triggered by the local control unit based on preset time intervals (e.g., every 24 hours, week, or month) or specific events (e.g., system restart, detection of abnormal data). The reference signal can be understood as a known and stable physical quantity input used to simulate the sensor's measurement environment under standard conditions. For example, for a water level sensor, a known liquid level can be provided; for a dissolved oxygen sensor, it can be exposed to a dissolved oxygen solution of a known concentration; for a temperature sensor, it can be placed in an environment with a known temperature. The accuracy of this reference signal is crucial for accurately evaluating sensor performance. In practical applications, the response output refers to the electrical or digital signal generated by the sensor after receiving the reference signal, reflecting the sensor's measurement result for that known input. The standard response curve is a curve or data table describing the ideal output characteristics of the sensor under different input conditions, established after rigorous testing and calibration at the factory. By comparing the sensor's current response output with this standard response curve, the response deviation—the difference between the sensor's current measurement value and the ideal measurement value—can be accurately calculated. When the response deviation exceeds a preset threshold, it indicates that the sensor may be experiencing performance drift. At this point, the local control unit dynamically adjusts the sensor's pre-calibration parameters based on the magnitude and type of the deviation (e.g., linear drift, zero-point drift, or sensitivity drift). These parameters can be gain coefficients, offsets, or other correction factors, designed to compensate for drift in the sensor's inherent characteristics, enabling the sensor to output raw data closer to the true value in subsequent measurements. Ultimately, these adjusted pre-calibration parameters are applied to all subsequent raw measurement data acquired by the sensor, ensuring long-term data accuracy and reliability.

[0071] This application effectively solves the problem of decreased data accuracy caused by sensor characteristic drift by introducing a periodic sensor self-test mechanism. Specifically, the local control unit periodically triggers the sensor self-test program by sending a known reference signal to the sensor and recording the sensor's response output after receiving the signal. Subsequently, this response output is precisely compared with the standard response curve established at the sensor's factory, thereby quantifying the sensor's current response deviation. Since the drift of the sensor's inherent characteristics causes its response output to deviate from the standard response curve, calculating the response deviation allows for accurate identification and assessment of the degree of sensor performance degradation. When the detected response deviation exceeds a preset threshold, the local control unit can intelligently adjust the sensor's pre-calibration parameters according to the specific magnitude and type of the deviation. This dynamic adjustment mechanism enables the system to compensate for sensor characteristic drift caused by aging, environmental influences, and other factors in real time, ensuring that the sensor maintains high measurement accuracy throughout long-term operation. By applying the adjusted pre-calibration parameters to subsequent raw measurement data, this solution fundamentally improves the accuracy and reliability of environmental data acquisition, providing a more solid data foundation for subsequent emergency operation parameter adjustments.

[0072] In some embodiments, the step of pre-calibrating the raw measurement data of each sensor based on environmental differences at the sensor deployment location further includes: The local control unit periodically acquires information on the current season and the stage of the breeding cycle; The local control unit adjusts the pre-calibration parameters of water turbidity and salinity based on the current seasonal information; The local control unit adjusts the pre-calibrated parameters of dissolved oxygen and temperature based on information from the stage of the aquaculture cycle; The local control unit applies the adjusted pre-calibration parameters to subsequent raw measurement data.

[0073] Specifically, periodically acquiring current seasonal and breeding cycle stage information means that the local control unit can have a built-in clock module and calendar function to determine the current season, such as spring, summer, autumn, and winter, using a preset date range. Simultaneously, breeding cycle stage information can be obtained through user input, preset breeding plans, or based on historical data analysis, such as the seedling stage, growth stage, and maturity stage. This information can be updated regularly to ensure the timeliness and accuracy of pre-calibration parameters.

[0074] The adjustment of pre-calibration parameters for turbidity and salinity based on current seasonal information can be understood as the pond's water characteristics changing with the seasons. For example, the rainy season may lead to increased turbidity, while seasons with high evaporation may result in increased salinity. By incorporating seasonal information, the local control unit can seasonally compensate for the readings of the turbidity and salinity sensors to eliminate the influence of seasonal factors on the measurement results.

[0075] Adjusting the pre-calibration parameters of dissolved oxygen and temperature based on information from different stages of the aquaculture cycle is crucial. Specifically, the metabolic activity intensity and environmental requirements of aquatic organisms vary at different stages of aquaculture. For example, maintaining water temperature and dissolved oxygen during the seedling stage may require higher precision and stricter ranges. By incorporating information from different stages of the aquaculture cycle, the local control unit can fine-tune the pre-calibration parameters of the dissolved oxygen and temperature sensors to better suit the actual needs of the current aquaculture stage, thereby improving the accuracy of relevant environmental parameter measurements.

[0076] This application enables the local control unit to dynamically perceive macroscopic trends in the pond environment by periodically acquiring current seasonal information and information on the stage of the aquaculture cycle. Key environmental parameters such as turbidity, salinity, dissolved oxygen, and temperature are significantly affected by seasons and aquaculture stages. For example, high water temperatures and a tendency for dissolved oxygen to decrease in summer, slow water exchange and potential salinity accumulation in winter, and the varying demands for dissolved oxygen and temperature by biological activity at different aquaculture stages all contribute to the limitations of traditional static pre-calibration methods. This application incorporates these dynamic factors into the pre-calibration model, enabling more refined and adaptive corrections to sensor data, thereby compensating for sensor characteristic drift and measurement biases caused by environmental factors. This dynamic adjustment mechanism ensures that sensor data maintains high accuracy and reliability across different time scales and aquaculture contexts, providing a solid data foundation for subsequent emergency operational parameter adjustments.

[0077] In some embodiments, the step of pre-calibrating the raw measurement data of each sensor based on environmental differences at the sensor deployment location includes: The local control unit generates an environmental impact factor for each sensor based on environmental parameters such as the geographic coordinates of the sensor deployment location, water depth, light intensity, and water flow speed. The local control unit applies environmental impact factors to the sensor's raw measurement data, performing preliminary corrections. The local control unit periodically collects environmental data from multiple reference points within the pond and compares it with the preliminary corrected measurement data from the corresponding position sensors to calculate the calibration error. The local control unit adjusts the pre-calibration parameters of each sensor based on the calibration error; The local control unit applies the adjusted pre-calibration parameters to subsequent raw measurement data.

[0078] Specifically, the local control unit generates an environmental impact factor for each sensor based on environmental parameters such as the geographic coordinates of the sensor's deployment location, water depth, light intensity, and water flow velocity. Geographic coordinates can be used to identify the macroscopic characteristics of the area where the sensor is located, such as its proximity to inlets, outlets, or aeration zones; water depth directly affects water pressure and light attenuation, significantly impacting dissolved oxygen and temperature sensor measurements; light intensity affects water temperature and algal photosynthesis, thus affecting dissolved oxygen; and water flow velocity affects water mixing and pollutant diffusion, perturbing sensor readings. These environmental parameters are comprehensively considered, and a comprehensive environmental impact factor is calculated using a preset physical model or empirical formula. This factor quantifies the degree to which the complex environment of a specific deployment location affects the sensor's measurements.

[0079] The local control unit applies environmental impact factors to the sensor's raw measurement data for initial correction. This initial correction aims to adjust the raw measurements based on the inherent characteristics of the sensor's environment, eliminating or mitigating systematic biases caused by environmental differences. For example, in areas with significant water depth, the impact of water pressure on certain sensors can be compensated for using environmental impact factors.

[0080] The local control unit periodically collects environmental data from multiple reference points within the pond and compares it with the preliminary corrected measurement data from the corresponding location sensors to calculate the calibration error. Reference points are benchmarks that have undergone high-precision calibration or manual measurement verification, and their data are considered authentic. By comparing the preliminary corrected data from the sensors with the actual data from these reference points, a precise calibration error can be obtained, reflecting the difference between the preliminary corrected data and the true value.

[0081] Based on the calibration error, the local control unit adjusts the pre-calibration parameters for each sensor. This step is central to dynamic calibration; by feeding the calibration error back into the adjustment of the pre-calibration parameters, the system can adaptively optimize the sensor's calibration model. For example, if the initial correction data for a sensor consistently exceeds the reference point data, its pre-calibration parameters will be adjusted to correct future measurements downwards.

[0082] The local control unit applies the adjusted pre-calibration parameters to subsequent raw measurement data. This means the calibration process is continuous and iterative; each adjustment brings the sensor's measurement results closer to the real-world environmental values, thus ensuring the long-term accuracy and reliability of data acquisition.

[0083] This application introduces multi-dimensional environmental parameters (geographic coordinates, water depth, light intensity, and water flow velocity) to generate environmental impact factors, thereby providing a more comprehensive and detailed characterization of the complexity of the sensor's microenvironment. This addresses the issue of insufficient accuracy in the pre-calibration method of the basic scheme due to inadequate consideration of environmental factors. By applying these environmental impact factors to the raw measurement data for preliminary correction, systematic biases caused by specific environmental conditions can be effectively eliminated, ensuring high accuracy of the data before entering the fine calibration stage. Furthermore, by periodically comparing with high-precision reference point data and calculating calibration errors, this application introduces a feedback mechanism, enabling the pre-calibration parameters to be dynamically adjusted based on the difference between actual measurements and true values. This iterative optimization process ensures that the sensor maintains high accuracy during long-term operation, effectively addressing the impact of sensor drift and environmental changes. Therefore, this application's solution improves the accuracy and reliability of environmental data acquisition from the source, providing a solid data foundation for subsequent emergency operation parameter adjustments.

[0084] like Figure 2 As shown in the illustration, this application also discloses a remote control system for a water pump, comprising: Information acquisition module 1 is used to acquire early warning information about the external environment through the central control station; The instruction generation module 2 is used to generate predictive emergency instructions containing operation parameters and priority identifiers based on early warning information and the internal operating status of the system. The instruction transmission module 3 is used to encode and fragment the predictive emergency instructions and transmit them through multiple communication channels. During the transmission process, the predictive emergency instructions are prioritized and a transmission confirmation and retransmission mechanism is adopted. The instruction receiving module 4 is used to receive predictive emergency instructions through various communication modules of the local control unit, and to perform integrity verification, repeatability check and fragment reconstruction on the received predictive emergency instructions. Emergency execution module 5 is used by the local control unit to monitor the status of the communication link and, when the communication link is interrupted, to identify the priority of the predictive emergency commands and execute the high-priority commands first.

[0085] The system provided in this application effectively solves the problem that emergency commands cannot be executed in a timely manner when communication is interrupted, thereby improving the level of intelligent management and risk resistance of aquaculture.

[0086] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for remote control of a water pump, characterized in that, include: External environmental early warning information is obtained through the central control station; Based on early warning information and the internal operating status of the system, predictive emergency instructions containing operating parameters and priority identifiers are generated. Predictive emergency commands are encoded and fragmented, and transmitted through multiple communication channels. During transmission, the predictive emergency commands are prioritized, and transmission confirmation and retransmission mechanisms are employed. The local control unit receives predictive emergency commands through various communication modules and performs integrity verification, repeatability checks, and fragment reconstruction on the received predictive emergency commands. The local control unit monitors the status of the communication link and, when the communication link is interrupted, identifies the priority of the predictive emergency commands and executes the higher priority commands first.

2. The remote control method for a water pump according to claim 1, characterized in that, The steps of encoding and fragmenting the predictive emergency commands, transmitting them through multiple communication channels, prioritizing the predictive emergency commands during transmission, and employing transmission confirmation and retransmission mechanisms also include: The central control station adjusts the redundancy coding strength and fragment size of the predictive emergency instructions based on their urgency and length. The central control station assesses the real-time quality of each communication channel; Based on real-time quality, the central control station selects one or more communication channel combinations to transmit predictive emergency command data packets; When the real-time quality of the main communication channel drops below a preset threshold, or when there is continuous data packet loss, the central control station will switch the transmission of predictive emergency commands to the backup communication channel. Priority weight values ​​are embedded in the predictive emergency instruction data packets, and the priority of the predictive emergency instructions is calculated in combination with the current timestamp; The local control unit confirms the received predictive emergency command fragments through a cross-channel collaborative confirmation mechanism; The central control station stopped retransmitting confirmed predictive emergency command segments.

3. The remote control method for a water pump according to claim 1, characterized in that, The step of the local control unit monitoring the communication link status and identifying the priority of predictive emergency commands when the communication link is interrupted, and prioritizing the execution of high-priority commands, further includes: The local control unit continuously collects environmental data during the execution of high-priority emergency commands; The local control unit calculates the rate of water level rise and the trend of dissolved oxygen changes based on the collected environmental data; The local control unit compares the calculated rate of water level rise and dissolved oxygen change trend with the preset emergency threshold. When the water level rises faster than a preset threshold, or the dissolved oxygen concentration falls below a preset threshold, the local control unit triggers an adjustment of emergency operation parameters. The local control unit recalculates the operating frequency and duration of the drainage pump and the oxygenation pump based on the current water level, the rate of water level rise, the dissolved oxygen concentration, and the preset pump operating characteristics. The local control unit adjusts the control signals of the drainage pump and oxygenation pump based on the recalculated operating frequency and duration to optimize emergency operations.

4. The remote control method for a water pump according to claim 3, characterized in that, The steps for the local control unit to continuously collect environmental data during the execution of high-priority emergency commands include: The local control unit redundantly collects data on pond water level, dissolved oxygen concentration, and water temperature using multiple physically distributed sensors of the same type. The local control unit performs real-time consistency checks on the raw data of each type of sensor. When a deviation between the data of the same type of sensor is found to exceed a preset value, it is marked as a data anomaly. For the identified data anomalies, the local control unit selects the corresponding sensor data calibration strategy based on the preset environmental event type; When the environmental event is heavy rainfall, the local control unit combines the water surface fluctuation intensity information to perform multi-point sampling averaging and trend smoothing processing on the water level sensor data; When the environmental event is an abnormally high temperature, the local control unit combines the water stratification information and performs a depth-weighted average processing on the dissolved oxygen and temperature sensor data; The local control unit uses the calibrated environmental data as the basis for dynamically adjusting subsequent emergency operation parameters.

5. The remote control method for a water pump according to claim 3, characterized in that, The step of continuously collecting environmental data by the local control unit during the execution of high-priority emergency commands also includes: The local control unit deploys multiple water level sensors, dissolved oxygen sensors, temperature sensors, and vibration sensors in different areas of the pond; The local control unit detects the vibration intensity generated by the pump body when the drainage pump or oxygenation pump is started; When the vibration intensity exceeds a preset threshold, the local control unit initiates the interference cancellation program; Interference cancellation procedures include: The local control unit constructs the area affected by water surface fluctuations based on the pump's operating status and the pond's physical characteristics; The local control unit performs multi-point sampling and averaging of water level sensor data outside the area affected by water surface fluctuations, and combines it with historical water level data before the pump operates to estimate the true water level of the pond; The local control unit estimates the range of the pump's impact on water bubbles based on the pump type and operating frequency. The local control unit performs a weighted average of dissolved oxygen sensor data outside the bubble's influence range and combines it with historical dissolved oxygen data before the pump starts operating to estimate the true dissolved oxygen concentration in the pond. The local control unit performs a layered weighted average of temperature sensor data at different depths based on the pump's effect on water mixing, and combines this with historical temperature data before the pump started operating to estimate the true water temperature of the pond. The local control unit will use the actual water level, actual dissolved oxygen concentration, and actual water temperature data obtained after interference elimination processing as the basis for dynamic adjustment of subsequent emergency operation parameters.

6. The remote control method for a water pump according to claim 5, characterized in that, The steps of deploying multiple water level sensors, dissolved oxygen sensors, temperature sensors, and vibration sensors in different areas of the pond by the local control unit also include: The local control unit pre-calibrates the raw measurement data of each sensor based on the environmental differences of the sensor deployment location; The local control unit continuously monitors the measured values ​​of each sensor and performs real-time consistency checks on them; When a discrepancy is detected between data from sensors of the same type, the local control unit adjusts the weights of each sensor based on the magnitude and duration of the discrepancy. The local control unit combines the pond's geographical information and water flow distribution to construct a virtual environment partition; The local control unit performs a weighted average of the environmental parameters within each zone based on the calibration data and weights of the sensors within that zone. The local control unit smooths the environmental parameters of adjacent zones and generates an overall environmental parameter distribution map of the pond. The local control unit uses the overall environmental parameter distribution map of the pond as the basis for dynamic adjustment of subsequent emergency operation parameters.

7. A remote control method for a water pump according to claim 6, characterized in that, The step of pre-calibrating the raw measurement data of each sensor based on the environmental differences of the sensor deployment location, as described in the local control unit, further includes: The local control unit periodically triggers the sensor self-test program; The self-test program sends a reference signal to the sensor and records the sensor's response output; The local control unit compares the response output with the standard response curve of the sensor at the factory and calculates the response deviation; When the response deviation exceeds the preset threshold, the local control unit adjusts the sensor's pre-calibration parameters according to the magnitude and type of the response deviation to compensate for the drift of the sensor's own characteristics. The local control unit applies the adjusted pre-calibration parameters to subsequent raw measurement data.

8. A remote control method for a water pump according to claim 6, characterized in that, The step of pre-calibrating the raw measurement data of each sensor based on the environmental differences of the sensor deployment location, as described in the local control unit, further includes: The local control unit periodically acquires information on the current season and the stage of the breeding cycle; The local control unit adjusts the pre-calibration parameters of water turbidity and salinity based on the current seasonal information; The local control unit adjusts the pre-calibrated parameters of dissolved oxygen and temperature based on information from the stage of the aquaculture cycle; The local control unit applies the adjusted pre-calibration parameters to subsequent raw measurement data.

9. A remote control method for a water pump according to claim 6, characterized in that, The step of pre-calibrating the raw measurement data of each sensor based on the environmental differences of the sensor deployment location by the local control unit includes: The local control unit generates an environmental impact factor for each sensor based on the geographic coordinates of the sensor deployment location, water depth, light intensity, and water flow velocity. The local control unit applies environmental impact factors to the sensor's raw measurement data, performing preliminary corrections. The local control unit periodically collects environmental data from multiple reference points within the pond and compares it with the preliminary corrected measurement data from the corresponding position sensors to calculate the calibration error. The local control unit adjusts the pre-calibration parameters of each sensor based on the calibration error; The local control unit applies the adjusted pre-calibration parameters to subsequent raw measurement data.

10. A remote control system for a water pump, characterized in that, include: The information acquisition module is used to acquire early warning information about the external environment through the central control station; The instruction generation module is used to generate predictive emergency instructions containing operation parameters and priority identifiers based on early warning information and the internal operating status of the system. The instruction transmission module is used to encode and fragment predictive emergency instructions and transmit them through multiple communication channels. During the transmission process, the predictive emergency instructions are prioritized and a transmission confirmation and retransmission mechanism is adopted. The instruction receiving module is used to receive predictive emergency instructions through various communication modules of the local control unit, and to perform integrity verification, repeatability check and fragment reconstruction on the received predictive emergency instructions; The emergency execution module is used by the local control unit to monitor the status of the communication link and, when the communication link is interrupted, to identify the priority of the predictive emergency commands and execute the higher priority commands first.

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