Intelligent fish tank ecological management method and device based on water quality detection

Abstract

This application relates to the field of Internet of Things (IoT) technology, and in particular to a method, device, computer equipment, storage medium, and computer program product for intelligent aquarium ecological management based on water quality monitoring. The method includes: acquiring multi-dimensional water quality parameter data of the aquarium water body and constructing a correlation state vector of the current water body; calculating the dynamic correlation between various water quality parameters and extracting correlation evolution features reflecting the coordinated change trend between parameters; matching the correlation evolution features with a preset precursor pattern library to determine whether there are precursor patterns of ecological imbalance; when a precursor pattern is matched, determining the imbalance risk level and calculating the remaining intervention window time; based on the risk level and the remaining intervention window time, selecting one from a preset multi-level intervention strategy to execute a corresponding level of preventive adjustment action, and continuously monitoring changes in water quality parameters after execution to evaluate the intervention effect. This method enables proactive preventive intervention before imbalance occurs.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a smart aquarium ecological management method, device, computer equipment, storage medium, and computer program product based on water quality detection. Background Technology

[0002] In recent years, with the deep integration of IoT technology and aquaculture management, smart aquarium systems have gradually evolved from simple automated control to ecological and intelligent systems. Existing smart aquarium products generally integrate water quality sensors, intelligent lighting, automatic feeding, and remote control modules, enabling real-time monitoring and alarm functions for basic parameters such as water temperature, pH, and dissolved oxygen. This reduces manual management costs and improves the convenience of ornamental fish keeping to some extent.

[0003] However, existing water quality management technologies mainly rely on a single parameter threshold-based judgment logic. This means that when the measured value of a certain water quality parameter exceeds a preset safety threshold, an alarm is triggered or a corresponding emergency operation is executed. This management approach has significant technical flaws. First, water quality parameters have complex coupling relationships. For example, the pH level of the water directly affects the toxicity level of ammonia nitrogen, and temperature changes alter dissolved oxygen content and affect microbial metabolic rates. Isolated judgments of a single parameter cannot reflect the overall ecological health status of the water body. Second, threshold alarms are a typical delayed response mechanism. When key toxic indicators such as ammonia nitrogen and nitrite have significantly exceeded the standards, fish are often already in a state of stress, causing irreversible damage to their health. The system's response lags behind the process of water quality deterioration. Furthermore, due to differences in stocking density, feeding habits, filtration configuration, and microbial community maturity, the normal fluctuation range and variation patterns of water quality parameters vary among different aquariums. A universal judgment standard based on fixed thresholds is difficult to adapt to individual scenarios, easily leading to problems such as failure to report when necessary or frequent false alarms.

[0004] To address the aforementioned issues, some technologies have attempted to introduce multi-parameter comprehensive analysis methods, which assess the overall water quality level by weighting or fuzzy comprehensive evaluation of each parameter.

[0005] However, current aquarium ecological management methods have the following technical problems: Existing methods for controlling aquarium water quality still fall under the category of post-event evaluation, resulting in a lag in water quality control and requiring optimization. Summary of the Invention

[0006] Therefore, it is necessary to provide a smart aquarium ecological management method, device, computer equipment, computer-readable storage medium, and computer program product based on water quality detection that can proactively implement preventive interventions before imbalance occurs, addressing the aforementioned technical problems.

[0007] Firstly, this application provides a smart aquarium ecological management method based on water quality testing. The method includes: Acquire multi-dimensional water quality parameter data of the aquarium water body, and construct the current water body's associated state vector. The multi-dimensional water quality parameters include pH, temperature, ammonia nitrogen concentration, and nitrite concentration. Based on the associated state vector, the dynamic correlation between various water quality parameters is calculated, and the correlation evolution characteristics reflecting the coordinated change trend among the parameters are extracted. The correlation evolution features are matched with a preset precursor pattern library to determine whether there is a precursor pattern of ecological imbalance in the current water body. The precursor pattern library stores typical parameter correlation change patterns corresponding to various water quality imbalance events before they occur. When a precursor pattern is matched, the imbalance risk level is determined and the remaining intervention window time is calculated based on the type of the matched precursor pattern and the current evolution stage. Based on the risk level and the remaining intervention window time, select one of the preset multi-level intervention strategies to implement the corresponding level of preventive adjustment action, and continuously monitor changes in water quality parameters after implementation to evaluate the intervention effect.

[0008] In one embodiment, the step of calculating the dynamic correlation between various water quality parameters based on the associated state vector and extracting the correlation evolution features reflecting the coordinated change trend among the parameters includes: The rate of change of each water quality parameter within a preset time window is obtained, and the change in the correlation coefficient between the water quality parameters is obtained. Obtain the offset trend of a single water quality parameter relative to its own historical stable range.

[0009] In one embodiment, the method further includes: Collect multi-parameter time-series data within a preset period before historical water quality imbalance events occur; The change trajectory of the correlation between the water quality parameters in the corresponding precursor stage of the imbalance event is marked, and the change trajectory is classified and encoded into several types of imbalance precursor templates. The precursor pattern library is constructed based on the imbalance precursor templates.

[0010] In one embodiment, when a precursor pattern is matched, determining the imbalance risk level and calculating the remaining intervention window time based on the type of the matched precursor pattern and the current evolutionary stage includes: The severity coefficient is determined based on the imbalance type corresponding to the matched precursor pattern; The urgency coefficient is determined based on the time distance between the current evolutionary stage and the outbreak of imbalance. The risk level is then output by combining the hazard coefficient and the urgency coefficient.

[0011] In one embodiment, the multi-level intervention strategy includes: The first-level strategy is to increase the monitoring frequency while maintaining the current operating mode. The second-level strategy involves fine-tuning actions, such as adjusting the filter flow or illumination duration. The third level of strategy involves implementing preventative interventions, such as activating backup filtration units or increasing aeration. The fourth-level strategy involves implementing protective interventions, such as suspending feeding, triggering a water change notification, and switching to emergency operation mode.

[0012] In one embodiment, the method further includes: In response to the end of the water quality imbalance event, the complete evolution trajectory of the water quality imbalance event from the first matching with the precursor pattern to the actual outbreak of the imbalance is obtained, and the evolution trajectory is compared with the precursor pattern; If the deviation between the precursor pattern and the evolutionary trajectory is less than a preset threshold, then the matching weight of the precursor pattern is strengthened. If the deviation between the precursor pattern and the evolutionary trajectory is greater than or equal to the preset threshold, a new precursor template is generated based on the evolutionary trajectory and stored in the precursor pattern library.

[0013] Secondly, this application also provides an intelligent aquarium ecological management device based on water quality testing. The device includes: The water quality parameter module is used to acquire multi-dimensional water quality parameter data of the aquarium water and construct the current associated state vector of the water body. The multi-dimensional water quality parameters include pH, temperature, ammonia nitrogen concentration and nitrite concentration. The evolution feature module is used to calculate the dynamic correlation between various water quality parameters based on the associated state vector, and extract the associated evolution features that reflect the coordinated change trend between parameters. The pattern matching module is used to match the correlation evolution features with a preset precursor pattern library to determine whether there is a precursor pattern of ecological imbalance in the current water body. The precursor pattern library stores typical parameter correlation change patterns corresponding to various water quality imbalance events before they occur. The intervention window module is used to determine the imbalance risk level and calculate the remaining intervention window time when a precursor pattern is matched, based on the type of the matched precursor pattern and the current evolution stage. The prevention and adjustment module is used to select one of the preset multi-level intervention strategies to perform the corresponding level of preventive adjustment action based on the risk level and the remaining intervention window time, and to continuously monitor the changes in water quality parameters after execution to evaluate the intervention effect.

[0014] In one embodiment, the evolutionary feature module is further configured to: The rate of change of each water quality parameter within a preset time window is obtained, and the change in the correlation coefficient between the water quality parameters is obtained. Obtain the offset trend of a single water quality parameter relative to its own historical stable range.

[0015] In one embodiment, the apparatus further includes a pattern library building module for: Collect multi-parameter time-series data within a preset period before historical water quality imbalance events occur; The change trajectory of the correlation between the water quality parameters in the corresponding precursor stage of the imbalance event is marked, and the change trajectory is classified and encoded into several types of imbalance precursor templates. The precursor pattern library is constructed based on the imbalance precursor templates.

[0016] In one embodiment, the intervention window module is further configured to: The severity coefficient is determined based on the imbalance type corresponding to the matched precursor pattern; The urgency coefficient is determined based on the time distance between the current evolutionary stage and the outbreak of imbalance. The risk level is then output by combining the hazard coefficient and the urgency coefficient.

[0017] In one embodiment, the multi-level intervention strategy includes: The first-level strategy is to increase the monitoring frequency while maintaining the current operating mode. The second-level strategy involves fine-tuning actions, such as adjusting the filter flow or illumination duration. The third level of strategy involves implementing preventative interventions, such as activating backup filtration units or increasing aeration. The fourth-level strategy involves implementing protective interventions, such as suspending feeding, triggering a water change notification, and switching to emergency operation mode.

[0018] In one embodiment, the device further includes an event feedback module for: In response to the end of the water quality imbalance event, the complete evolution trajectory of the water quality imbalance event from the first matching with the precursor pattern to the actual outbreak of the imbalance is obtained, and the evolution trajectory is compared with the precursor pattern; If the deviation between the precursor pattern and the evolutionary trajectory is less than a preset threshold, then the matching weight of the precursor pattern is strengthened. If the deviation between the precursor pattern and the evolutionary trajectory is greater than or equal to the preset threshold, a new precursor template is generated based on the evolutionary trajectory and stored in the precursor pattern library.

[0019] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the intelligent aquarium ecological management method based on water quality detection as described in any embodiment of the first aspect.

[0020] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the intelligent aquarium ecological management method based on water quality detection as described in any embodiment of the first aspect.

[0021] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of a smart aquarium ecological management method based on water quality detection as described in any embodiment of the first aspect.

[0022] The above-described intelligent aquarium ecological management method, device, computer equipment, storage medium, and computer program product based on water quality detection, derived from the technical features in the embodiments, can achieve the following beneficial effects to address the technical problems in the background art: This application provides a smart aquarium ecological management method based on water quality monitoring. In implementation, by constructing a multi-parameter correlated state vector, water quality management is elevated from single-parameter threshold judgment to a holistic situational awareness based on multi-parameter collaborative evolution. This method can capture early signals of abnormal shifts in the correlation between parameters before water imbalance, enabling early identification and warning of ecological imbalance. By matching real-time correlated evolution characteristics with a precursor pattern library, risk can be determined and intervention windows calculated even before key indicators such as ammonia nitrogen and nitrite exceed standards. This transforms traditional post-event alarms into preventative management with sufficient response time, effectively avoiding irreversible damage to fish. A tiered intervention strategy selects appropriate adjustment actions based on risk level and remaining window time, balancing timeliness and appropriateness of intervention to avoid over- or under-intervention. Continuous monitoring and evaluation of the effect after implementation forms a closed loop, enabling the system to transition from passive response to proactive prediction, significantly improving the ecological safety of the smart aquarium. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the first process of an intelligent aquarium ecological management method based on water quality detection in one embodiment; Figure 2 This is a schematic diagram of the second process of a smart aquarium ecological management method based on water quality detection in another embodiment; Figure 3 This is a schematic diagram of the third process of a smart aquarium ecological management method based on water quality detection in another embodiment; Figure 4 This is a schematic diagram of the fourth process of a smart aquarium ecological management method based on water quality detection in another embodiment; Figure 5 This is a schematic diagram of the fifth process of a smart aquarium ecological management method based on water quality detection in another embodiment; Figure 6 This is a structural block diagram of an intelligent aquarium ecological management device based on water quality detection in one embodiment; Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0026] In one embodiment, such as Figure 1 As shown, a smart aquarium ecological management method based on water quality detection is provided. This embodiment illustrates the method's application to a terminal, but it is understood that the method can also be applied to a server, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps: Step 102: Obtain multi-dimensional water quality parameter data of the aquarium water and construct the current water body's associated state vector. The multi-dimensional water quality parameters include pH, temperature, ammonia nitrogen concentration, and nitrite concentration.

[0027] Step 104: Based on the associated state vector, calculate the dynamic correlation between each water quality parameter and extract the correlation evolution characteristics that reflect the coordinated change trend between parameters.

[0028] Step 106: Match the correlation evolution features with a preset precursor pattern library to determine whether there is a precursor pattern of ecological imbalance in the current water body. The precursor pattern library stores typical parameter correlation change patterns corresponding to various water quality imbalance events before they occur.

[0029] Step 108: When a precursor pattern is matched, determine the imbalance risk level and calculate the remaining intervention window time based on the type of the matched precursor pattern and the current evolutionary stage.

[0030] Step 1010: Based on the risk level and the remaining intervention window time, select one of the preset multi-level intervention strategies to execute the corresponding level of preventive adjustment action, and continuously monitor the changes in water quality parameters after execution to evaluate the intervention effect.

[0031] In the above-mentioned intelligent aquarium ecological management method based on water quality testing, by reasonably deducing the technical features in the embodiments, the beneficial effect of solving the technical problems raised in the background art is achieved: This application provides a smart aquarium ecological management method based on water quality monitoring. In implementation, by constructing a multi-parameter correlated state vector, water quality management is elevated from single-parameter threshold judgment to a holistic situational awareness based on multi-parameter collaborative evolution. This method can capture early signals of abnormal shifts in the correlation between parameters before water imbalance, enabling early identification and warning of ecological imbalance. By matching real-time correlated evolution characteristics with a precursor pattern library, risk can be determined and intervention windows calculated even before key indicators such as ammonia nitrogen and nitrite exceed standards. This transforms traditional post-event alarms into preventative management with sufficient response time, effectively avoiding irreversible damage to fish. A tiered intervention strategy selects appropriate adjustment actions based on risk level and remaining window time, balancing timeliness and appropriateness of intervention to avoid over- or under-intervention. Continuous monitoring and evaluation of the effect after implementation forms a closed loop, enabling the system to transition from passive response to proactive prediction, significantly improving the ecological safety of the smart aquarium.

[0032] In one embodiment, such as Figure 2 As shown, step 104 includes: Step 202: Obtain the rate of change of each water quality parameter within a preset time window, and obtain the change in the correlation coefficient between the water quality parameters.

[0033] Step 204: Obtain the offset trend of a single water quality parameter relative to its own historical stable range.

[0034] In this embodiment, by acquiring the rate of change of each parameter within the time window, the change in the correlation coefficient between parameters, and the offset trend of a single parameter relative to its own historical stable range, a multi-dimensional characterization of the co-evolutionary features between parameters is achieved. This captures the linkage between parameters while also taking into account the drift of a single parameter relative to its own baseline, making the identification of precursor patterns more comprehensive and sensitive. It avoids the problem of missed detection caused by relying solely on the correlation between parameters while ignoring the gradual drift of a single parameter.

[0035] In one embodiment, such as Figure 3 As shown, the method further includes: Step 302: Collect multi-parameter time-series data within a preset period before the occurrence of historical water quality imbalance events.

[0036] Step 304: Mark the change trajectory of the correlation between the water quality parameters in the corresponding precursor stage of the imbalance event, classify and encode the change trajectory into several types of imbalance precursor templates, and construct the precursor pattern library based on the imbalance precursor templates.

[0037] In this embodiment, by collecting and labeling multi-parameter time-series data before the occurrence of historical imbalance events, the change trajectory of the correlation between each parameter is extracted and classified and coded into imbalance precursor templates. This ensures that the precursor templates in the pattern library are all derived from the actual evolution path of real imbalance cases, have clear event correspondence and reproducibility, and improve the accuracy and reliability of precursor pattern matching.

[0038] In one embodiment, such as Figure 4 As shown, step 108 includes: Step 402: Determine the severity coefficient based on the imbalance type corresponding to the matched precursor pattern.

[0039] Step 404: Determine the urgency coefficient based on the time distance between the current evolutionary stage and the outbreak of imbalance, and output the risk level by combining the hazard coefficient and the urgency coefficient.

[0040] In this embodiment, the severity of the imbalance type is reflected by the hazard degree coefficient, and the time slack between the current moment and the outbreak of the imbalance is quantified by the urgency coefficient. The risk level is output by combining the two, so that the risk assessment takes into account both the magnitude of the potential event’s harm and the available reaction time. This avoids risk misjudgment caused by making a single-dimensional judgment based solely on the degree of harm or the time distance, and improves the scientific nature of risk classification and the rationality of intervention decisions.

[0041] In one embodiment, the multi-level intervention strategy includes: The first-level strategy is to increase the monitoring frequency while maintaining the current operating mode. The second-level strategy involves fine-tuning actions, such as adjusting the filter flow or illumination duration. The third level of strategy involves implementing preventative interventions, such as activating backup filtration units or increasing aeration. The fourth-level strategy involves implementing protective interventions, such as suspending feeding, triggering a water change notification, and switching to emergency operation mode.

[0042] In this embodiment, by setting four levels of intervention strategies from mild to severe, a complete treatment gradient is covered, from enhanced monitoring to active regulation to protective intervention. This allows the system to select intervention actions with matching intensity based on different risk levels and remaining window time. This avoids unnecessary disturbance to the aquarium ecosystem due to excessive operation when the risk is low, while having sufficient means to ensure biosafety when the risk is urgent, thus balancing the effectiveness of intervention and the stability of the ecosystem.

[0043] In one embodiment, such as Figure 5 As shown, the method further includes: Step 502: In response to the end of the water quality imbalance event, obtain the complete evolution trajectory of the water quality imbalance event from the first matching to the precursor pattern to the actual outbreak of the imbalance, and compare the evolution trajectory with the precursor pattern.

[0044] Step 504: If the deviation between the precursor pattern and the evolutionary trajectory is less than a preset threshold, then strengthen the matching weight of the precursor pattern; Step 506: If the deviation between the precursor pattern and the evolutionary trajectory is greater than or equal to the preset threshold, then a new precursor template is generated based on the evolutionary trajectory and stored in the precursor pattern library.

[0045] In this embodiment, after each imbalance event, the actual evolution trajectory is compared and verified with the matched precursor pattern. Based on the magnitude of the deviation, weight enhancement or new templates are added to the library, enabling the precursor pattern library to continuously obtain feedback from actual operation and update itself. As the running time increases, the adaptation accuracy of the pattern library to specific aquariums continuously improves, effectively overcoming the problem of insufficient matching accuracy of general pattern libraries in personalized scenarios, and realizing the continuous evolution of the system's ecological management capabilities.

[0046] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0047] Based on the same inventive concept, this application also provides a water quality detection-based intelligent aquarium ecological management device for implementing the above-mentioned intelligent aquarium ecological management method based on water quality detection. The solution provided by this device is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more embodiments of the water quality detection-based intelligent aquarium ecological management device provided below can be found in the limitations of the water quality detection-based intelligent aquarium ecological management method described above, and will not be repeated here.

[0048] In one embodiment, such as Figure 6 As shown, an intelligent aquarium ecological management device based on water quality detection is provided, including: a water quality parameter module, an evolutionary characteristic module, a pattern matching module, an intervention window module, and a prevention and regulation module, wherein: The water quality parameter module is used to acquire multi-dimensional water quality parameter data of the aquarium water and construct the current associated state vector of the water body. The multi-dimensional water quality parameters include pH, temperature, ammonia nitrogen concentration and nitrite concentration. The evolution feature module is used to calculate the dynamic correlation between various water quality parameters based on the associated state vector, and extract the associated evolution features that reflect the coordinated change trend between parameters. The pattern matching module is used to match the correlation evolution features with a preset precursor pattern library to determine whether there is a precursor pattern of ecological imbalance in the current water body. The precursor pattern library stores typical parameter correlation change patterns corresponding to various water quality imbalance events before they occur. The intervention window module is used to determine the imbalance risk level and calculate the remaining intervention window time when a precursor pattern is matched, based on the type of the matched precursor pattern and the current evolution stage. The prevention and adjustment module is used to select one of the preset multi-level intervention strategies to perform the corresponding level of preventive adjustment action based on the risk level and the remaining intervention window time, and to continuously monitor the changes in water quality parameters after execution to evaluate the intervention effect.

[0049] In one embodiment, the evolutionary feature module is further configured to: The rate of change of each water quality parameter within a preset time window is obtained, and the change in the correlation coefficient between the water quality parameters is obtained. Obtain the offset trend of a single water quality parameter relative to its own historical stable range.

[0050] In one embodiment, the apparatus further includes a pattern library building module for: Collect multi-parameter time-series data within a preset period before historical water quality imbalance events occur; The change trajectory of the correlation between the water quality parameters in the corresponding precursor stage of the imbalance event is marked, and the change trajectory is classified and encoded into several types of imbalance precursor templates. The precursor pattern library is constructed based on the imbalance precursor templates.

[0051] In one embodiment, the intervention window module is further configured to: The severity coefficient is determined based on the imbalance type corresponding to the matched precursor pattern; The urgency coefficient is determined based on the time distance between the current evolutionary stage and the outbreak of imbalance. The risk level is then output by combining the hazard coefficient and the urgency coefficient.

[0052] In one embodiment, the multi-level intervention strategy includes: The first-level strategy is to increase the monitoring frequency while maintaining the current operating mode. The second-level strategy involves fine-tuning actions, such as adjusting the filter flow or illumination duration. The third level of strategy involves implementing preventative interventions, such as activating backup filtration units or increasing aeration. The fourth-level strategy involves implementing protective interventions, such as suspending feeding, triggering a water change notification, and switching to emergency operation mode.

[0053] In one embodiment, the device further includes an event feedback module for: In response to the end of the water quality imbalance event, the complete evolution trajectory of the water quality imbalance event from the first matching with the precursor pattern to the actual outbreak of the imbalance is obtained, and the evolution trajectory is compared with the precursor pattern; If the deviation between the precursor pattern and the evolutionary trajectory is less than a preset threshold, then the matching weight of the precursor pattern is strengthened. If the deviation between the precursor pattern and the evolutionary trajectory is greater than or equal to the preset threshold, a new precursor template is generated based on the evolutionary trajectory and stored in the precursor pattern library.

[0054] The various modules in the aforementioned intelligent aquarium ecological management device based on water quality detection can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0055] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a smart aquarium ecological management method based on water quality detection. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0056] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0057] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0058] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0059] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0060] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0061] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0062] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A smart aquarium ecological management method based on water quality detection, characterized in that, The method includes: Acquire multi-dimensional water quality parameter data of the aquarium water body, and construct the current water body's associated state vector. The multi-dimensional water quality parameters include pH, temperature, ammonia nitrogen concentration, and nitrite concentration. Based on the associated state vector, the dynamic correlation between various water quality parameters is calculated, and the correlation evolution characteristics reflecting the coordinated change trend among the parameters are extracted. The correlation evolution features are matched with a preset precursor pattern library to determine whether there is a precursor pattern of ecological imbalance in the current water body. The precursor pattern library stores typical parameter correlation change patterns corresponding to various water quality imbalance events before they occur. When a precursor pattern is matched, the imbalance risk level is determined and the remaining intervention window time is calculated based on the type of the matched precursor pattern and the current evolution stage. Based on the risk level and the remaining intervention window time, select one of the preset multi-level intervention strategies to implement the corresponding level of preventive adjustment action, and continuously monitor the changes in water quality parameters after implementation to evaluate the intervention effect.

2. The method according to claim 1, characterized in that, The process of calculating the dynamic correlation between various water quality parameters based on the associated state vector and extracting correlation evolution features reflecting the coordinated change trend among the parameters includes: The rate of change of each water quality parameter within a preset time window is obtained, and the change in the correlation coefficient between the water quality parameters is obtained. Obtain the offset trend of a single water quality parameter relative to its own historical stable range.

3. The method according to claim 1, characterized in that, The method further includes: Collect multi-parameter time-series data within a preset period before historical water quality imbalance events occur; The change trajectory of the correlation between the water quality parameters in the corresponding precursor stage of the imbalance event is marked, and the change trajectory is classified and encoded into several types of imbalance precursor templates. The precursor pattern library is constructed based on the imbalance precursor templates.

4. The method according to claim 1, characterized in that, When a precursor pattern is matched, determining the imbalance risk level and calculating the remaining intervention window time based on the type of the matched precursor pattern and the current evolutionary stage includes: The severity coefficient is determined based on the imbalance type corresponding to the matched precursor pattern; The urgency coefficient is determined based on the time distance between the current evolutionary stage and the outbreak of imbalance. The risk level is then output by combining the hazard coefficient and the urgency coefficient.

5. The method according to any one of claims 1 to 4, characterized in that, The multi-level intervention strategy includes: The first-level strategy is to increase the monitoring frequency while maintaining the current operating mode. The second-level strategy involves fine-tuning actions, such as adjusting the filter flow or illumination duration. The third level of strategy involves implementing preventative interventions, such as activating backup filtration units or increasing aeration. The fourth-level strategy involves implementing protective interventions, such as suspending feeding, triggering a water change notification, and switching to emergency operation mode.

6. The method according to claim 1, characterized in that, The method further includes: In response to the end of the water quality imbalance event, the complete evolution trajectory of the water quality imbalance event from the first matching to the precursor pattern to the actual outbreak of the imbalance is obtained, and the evolution trajectory is compared with the precursor pattern; If the deviation between the precursor pattern and the evolutionary trajectory is less than a preset threshold, then the matching weight of the precursor pattern is strengthened. If the deviation between the precursor pattern and the evolutionary trajectory is greater than or equal to the preset threshold, a new precursor template is generated based on the evolutionary trajectory and stored in the precursor pattern library.

7. A smart aquarium ecological management device based on water quality detection, characterized in that, The device includes: The water quality parameter module is used to acquire multi-dimensional water quality parameter data of the aquarium water and construct the current associated state vector of the water body. The multi-dimensional water quality parameters include pH, temperature, ammonia nitrogen concentration and nitrite concentration. The evolution feature module is used to calculate the dynamic correlation between various water quality parameters based on the associated state vector, and extract the associated evolution features that reflect the coordinated change trend between parameters. The pattern matching module is used to match the correlation evolution features with a preset precursor pattern library to determine whether there is a precursor pattern of ecological imbalance in the current water body. The precursor pattern library stores typical parameter correlation change patterns corresponding to various water quality imbalance events before they occur. The intervention window module is used to determine the imbalance risk level and calculate the remaining intervention window time when a precursor pattern is matched, based on the type of the matched precursor pattern and the current evolution stage. The prevention and adjustment module is used to select one of the preset multi-level intervention strategies to perform the corresponding level of preventive adjustment action based on the risk level and the remaining intervention window time, and to continuously monitor the changes in water quality parameters after execution to evaluate the intervention effect.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images