An information interconnection method and system for solar water purification terminals

Abstract

The present invention discloses an information-based interconnection method and system for solar water purification terminals, comprising: obtaining status data of multiple water purification terminals; evaluating the energy efficiency and water quality compliance of each water purification terminal based on the status data to obtain an energy efficiency level and a water quality compliance index; extracting characteristic values ​​from the energy efficiency level and the water quality compliance index to generate a load capacity level and an output parameter threshold of each water purification terminal; issuing the task allocation plan and resource allocation instructions to the water purification terminal and receiving adjusted status data; updating the energy efficiency level and the water quality compliance index based on the adjusted status data, verifying network stability and generating an optimization plan; iteratively adjusting the optimization plan until a preset termination condition is met, generating a final task allocation plan and issuing it to each water purification terminal, thereby effectively solving the management problem of decentralized water purification terminals and significantly improving the overall performance of the system.

Description

Technical Field

[0001] The present invention relates to the field of water purification information technology, and in particular to an information interconnection method and system for solar water purification terminals. Background Art

[0002] Existing solar water purification solutions often operate on a single device, lacking inter-device coordination mechanisms. This independent operation model often leads to inefficiencies and uneven resource utilization when faced with large-scale demands or complex environments. This is especially true in scenarios with energy fluctuations or significant variations in water quality, making it difficult to ensure overall system stability and purification capacity.

[0003] The primary limitation of current solar water purification systems lies in the lack of interconnectivity between devices, leading to isolated operational data, illogical task allocation, and limited energy efficiency. Traditional systems typically focus solely on single-point optimization, ignoring the potential of networked operations and failing to dynamically adapt to changing demands and environmental conditions. This limitation makes it difficult to achieve scalable and efficient water purification systems in practical deployments.

[0004] Looking further, the core challenges lie in three technical factors: achieving data sharing, load balancing, and resource allocation across multiple terminals. The lack of a data sharing mechanism prevents terminals from understanding each other's operating status and water treatment requirements in real time, resulting in a lack of intelligent task allocation. Inadequate load balancing leads to overloaded operations on some terminals, while idle resources on others. Resource allocation challenges during energy shortages directly impact the system's stability and reliability under extreme conditions.

[0005] Therefore, how to achieve data sharing among multiple solar water purification terminals through an information-based interconnected platform, and intelligently allocate tasks and deploy resources based on cloud computing technology, has become a key issue in improving the stability and efficiency of the entire water purification network. Summary of the Invention

[0006] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide an information interconnection method and system for solar water purification terminals, which effectively solves the management problems of decentralized water purification terminals, significantly improves the overall performance of the system, provides safe and stable drinking water supply for remote areas, and promotes the realization of sustainable development goals.

[0007] To achieve the above object, the specific solutions of the present invention are as follows:

[0008] On one hand, the present invention provides an information interconnection method for solar water purification terminals, which specifically includes the following steps:

[0009] Acquire status data of multiple water purification terminals, wherein the status data includes at least energy parameters and water quality parameters; evaluate the energy efficiency and water quality compliance of each water purification terminal based on the status data to obtain an energy efficiency level and a water quality compliance index; extract characteristic values ​​from the energy efficiency level and the water quality compliance index to generate a load capacity level and an output parameter threshold of each water purification terminal; judge the energy status of each water purification terminal based on the load capacity level and the output parameter threshold, and generate a task allocation plan and a resource allocation instruction; issue the task allocation plan and the resource allocation instruction to the water purification terminal and receive the adjusted status data; update the energy efficiency level and the water quality compliance index based on the adjusted status data, verify network stability and generate an optimization plan; iteratively adjust the optimization plan until a preset termination condition is met, generate a final task allocation plan and issue it to each water purification terminal.

[0010] Furthermore, the energy efficiency and water quality compliance of each water purification terminal are evaluated based on the status data, including: using a preset energy efficiency evaluation model to calculate the ratio of the photovoltaic panel output power to the water purification flow, and generating the energy conversion efficiency level of each water purification terminal; classifying the turbidity, pH value and heavy metal concentration in the water quality sensor detection parameters through a support vector machine classification algorithm, and generating a water quality compliance index for each water purification terminal; and determining the operating status of each water purification terminal based on the energy conversion efficiency level and the water quality compliance index.

[0011] Furthermore, the extracting of characteristic values ​​from the energy efficiency level and the water quality compliance index includes: extracting the energy consumption per unit time and the charging and discharging efficiency of the energy storage battery from the energy efficiency level as energy efficiency characteristic values; extracting the pollutant concentration change rate and the index fluctuation amplitude from the water quality compliance index as water quality dynamic characteristic values; and using a real-time clustering analysis algorithm based on K-means to classify the energy efficiency characteristic values ​​and water quality dynamic characteristic values ​​to generate the current load capacity level and water quality output parameter threshold of each water purification terminal.

[0012] Furthermore, the energy status of each water purification terminal is judged according to the load capacity level and the output parameter threshold, including: calculating the ratio α of the current energy consumption rate of each water purification terminal to the remaining energy reserve; if the ratio α is greater than or equal to the preset threshold β, the corresponding water purification terminal is judged to be an energy-deficient terminal; based on the judgment result, a list of energy-deficient terminals is generated, and the geographical location and communication delay data of the energy-deficient terminals are extracted.

[0013] Furthermore, the generation of task allocation plans and resource allocation instructions includes: using a task allocation algorithm based on a load balancing model to generate a preliminary water purification task optimization plan, wherein the load balancing model aims to minimize the load difference between terminals; adjusting the preliminary water purification task optimization plan through an energy consumption optimization function, wherein the energy consumption optimization function aims to minimize the total energy consumption; generating resource allocation instructions based on the adjusted task optimization plan, wherein the resource allocation instructions include an energy allocation ratio and a task migration path.

[0014] Furthermore, the task allocation plan and resource allocation instructions are sent to the water purification terminal, including: performing supply and demand matching analysis on the remaining capacity of the energy-deficient terminal and the available resources of the neighboring terminal, the neighboring terminal being a water purification terminal whose geographical distance or communication delay meets preset conditions; using an improved Hungarian algorithm to generate a resource allocation instruction including an energy allocation ratio and a task migration path; and sending the resource allocation instruction to the corresponding water purification terminal through an information interconnection platform.

[0015] Furthermore, the updating of the energy efficiency level and water quality compliance index based on the adjusted status data includes: receiving the adjusted status data after each water purification terminal executes the resource allocation instruction; recalculating the energy conversion efficiency level and water quality compliance index based on the adjusted status data; and generating performance change data of each water purification terminal by comparing the energy efficiency level and water quality compliance index before and after adjustment.

[0016] Furthermore, the verification of network stability and generation of an optimization plan include: calculating the data transmission delay reduction rate and terminal response time optimization rate after task allocation and resource allocation; generating a stability threshold based on a linear regression model trained with past historical operation data; judging whether the data transmission delay reduction rate and terminal response time optimization rate reach the stability threshold, and if not, adjusting the task allocation parameters based on the load deviation rate and water quality fluctuation coefficient.

[0017] Furthermore, the optimization scheme is iteratively adjusted until a preset termination condition is met, including: adjusting the task allocation scheme and resource allocation instructions according to the network stability improvement value; terminating the adjustment if the change in the network stability improvement value is less than a preset threshold for multiple consecutive iterations; terminating the adjustment if the total number of iterations reaches a preset upper limit; generating a final task allocation scheme based on the final adjustment result and sending it to each water purification terminal.

[0018] Another aspect of the present invention provides an information interconnection system for solar water purification terminals, comprising:

[0019] a data acquisition module configured to obtain status data of a plurality of water purification terminals, wherein the status data includes at least energy parameters and water quality parameters;

[0020] an evaluation module configured to evaluate the energy efficiency and water quality compliance of each water purification terminal based on the status data to obtain an energy efficiency grade and a water quality compliance index;

[0021] a feature extraction module configured to extract feature values ​​from the energy efficiency level and the water quality compliance index to generate a load capacity level and an output parameter threshold for each water purification terminal;

[0022] a decision control module configured to determine the energy status of each water purification terminal according to the load capacity level and output parameter threshold, and generate a task allocation plan and resource allocation instructions;

[0023] a communication control module configured to issue the task allocation plan and resource allocation instructions to the water purification terminal and receive adjusted status data;

[0024] an optimization verification module configured to update the energy efficiency level and water quality compliance index according to the adjusted status data, verify network stability and generate an optimization plan;

[0025] The iterative execution module is configured to iteratively adjust the optimization plan until a preset termination condition is met, generate a final task allocation plan and send it to each water purification terminal.

[0026] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0027] This invention addresses the problems of low efficiency, unstable water quality, and poor system reliability in traditional decentralized solar water purification terminals. By integrating decentralized terminals into a collaborative intelligent network through a technical chain of centralized data management, intelligent multi-objective optimization, and dynamic closed-loop verification, this invention achieves a comprehensive improvement in energy efficiency, water quality stability, and system reliability, providing a feasible technical framework for the deployment of large-scale distributed water purification systems. Through intelligent upgrades, this invention effectively solves the management challenges of decentralized water purification terminals, significantly improves the overall performance of the system, provides a safe and stable supply of drinking water to remote areas, and promotes the realization of sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The figure is a flow chart of the information interconnection method of the solar water purification terminal of the present invention. DETAILED DESCRIPTION

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the scope of implementation of the present invention is not limited thereto.

[0030] like Figure 1As shown, the information interconnection method of a solar water purification terminal described in this embodiment may specifically include the following steps:

[0031] Step S101, obtaining status data of multiple water purification terminals, wherein the status data includes at least energy parameters and water quality parameters; evaluating the energy efficiency and water quality compliance of each water purification terminal based on the status data, and obtaining an energy efficiency grade and a water quality compliance index.

[0032] When obtaining status data for multiple water purification terminals, it is first necessary to clarify the specific content of energy parameters and water quality parameters. Energy parameters generally include the output power of photovoltaic panels and the remaining capacity of energy storage batteries. These data reflect the energy supply capacity and reserve status of the solar water purification terminal. The output power of photovoltaic panels directly determines the operating efficiency of the water purification system, while the remaining capacity of energy storage batteries is used to evaluate the system's ability to continue operating in insufficient sunlight. Water quality parameters include the purified water flow rate and indicators detected by water quality sensors, such as turbidity, pH value, dissolved oxygen, etc. These parameters are used to measure the treatment effect of the water purification system. Through the information interconnection platform, these data can be transmitted in real time with encryption to ensure data integrity and security. When evaluating the energy efficiency of each water purification terminal based on status data, it is necessary to conduct a comprehensive analysis based on the output power of photovoltaic panels and purified water flow rate.

[0033] For example, a terminal's photovoltaic panel output is 500 watts, and its purified water flow rate is 100 liters per hour. By comparing the energy consumption and purified water output of different terminals, the energy conversion efficiency of each terminal can be calculated. Assuming that Terminal A has an energy conversion efficiency of 80% and Terminal B only 60%, Terminal A has a higher energy efficiency rating. This evaluation process helps identify terminals with lower energy efficiency, providing a basis for further optimization. Water quality sensor detection parameters are key indicators in assessing water quality compliance.

[0034] For example, if water quality testing at a terminal shows a turbidity of 1 NTU, a pH of 7.5, and a dissolved oxygen level of 8 mg / L—all of which meet drinking water standards—then the terminal's water quality compliance index is high. If another terminal's turbidity exceeds 5 NTU or its pH falls outside the range of 6.5-8.5, its water quality compliance index is low. This assessment allows for timely identification of terminals with substandard water quality, enabling appropriate adjustments to ensure the water purification system's output meets requirements. This state-based data-based assessment method comprehensively reflects the operational status of water purification terminals, providing a scientific basis for resource allocation and system optimization.

[0035] For example, if a terminal's energy efficiency rating is low, its operating mode can be adjusted or energy storage equipment can be added. If the water quality index is low, the filter element can be checked or the water purification process can be adjusted. This dynamic assessment and adjustment can significantly improve the overall performance and reliability of the distributed water purification system while reducing operating costs.

[0036] Specifically, the acquisition of status data of multiple water purification terminals includes: receiving status data periodically transmitted by each water purification terminal through an information interconnection platform, the status data including photovoltaic panel output power, remaining capacity of energy storage battery, water purification flow rate and water quality sensor detection parameters; encrypting and storing the status data; extracting the status data from the information interconnection platform according to a preset time interval to generate a status data set for each water purification terminal.

[0037] Receiving status data periodically transmitted by each water purification terminal through an information-based interconnection platform is the foundation for remote monitoring and management. This information-based interconnection platform typically utilizes Internet of Things (IoT) technology to connect geographically distributed water purification terminals and enable real-time data transmission.

[0038] For example, a solar-powered water purification terminal in a certain region sends status data to the platform every hour, including photovoltaic panel output power, remaining battery capacity, water flow rate, and water quality sensor parameters. The photovoltaic panel output power reflects the efficiency of converting solar energy into electricity, the remaining battery capacity indicates the current energy storage system's power reserves, the water flow rate is used to assess the device's water purification capabilities, and the water quality sensor parameters directly reflect the safety of the water quality. The periodic transmission of this data ensures that managers have timely information on the device's operating status, providing data support for subsequent analysis and decision-making. Encrypting and storing status data is crucial to ensuring data security. Because status data includes device operating parameters and water quality information, data security is paramount.

[0039] For example, the platform can use the AES encryption algorithm to encrypt transmitted data, ensuring that it cannot be maliciously intercepted or tampered with during transmission. The encrypted data is stored in a cloud server or local database, and data backup is performed during storage to prevent data loss. Encryption and storage not only protect the privacy and integrity of the data but also provide a reliable data source for subsequent data analysis. Extracting status data from the information interconnection platform at preset time intervals to generate a status data set for each water purification terminal is a prerequisite for equipment status analysis and resource allocation.

[0040] For example, the platform can be configured to extract status data from the database every 24 hours and organize this data by terminal number to form a status data set for each terminal. These sets can be used to generate historical operation reports for the device or as basic data for training machine learning models.

[0041] For example, by analyzing the past 30 days of photovoltaic panel output power data, it is possible to predict future solar power generation and optimize the charging and discharging strategies of energy storage batteries. This allows managers to more efficiently monitor and manage water purification terminals, ensuring stable operation and improving resource utilization efficiency.

[0042] Specifically, the energy efficiency and water quality compliance of each water purification terminal are evaluated based on the status data, including: using a preset energy efficiency evaluation model to calculate the ratio of the photovoltaic panel output power to the water purification flow rate, and generating the energy conversion efficiency level of each water purification terminal; classifying the turbidity, pH value and heavy metal concentration in the water quality sensor detection parameters through a support vector machine classification algorithm, and generating a water quality compliance index for each water purification terminal; and determining the operating status of each water purification terminal based on the energy conversion efficiency level and the water quality compliance index.

[0043] A preset energy efficiency evaluation model calculates the ratio of photovoltaic panel output power to purified water flow rate, generating an energy conversion efficiency rating for each water purification terminal. The core of the energy efficiency evaluation model is to assess energy efficiency by quantifying the relationship between photovoltaic panel output power and purified water flow rate. The output power of photovoltaic panels is affected by factors such as light intensity and temperature, while the purified water flow rate depends on the operating status of the water pump and the efficiency of the filtration system.

[0044] For example, a terminal's photovoltaic panels output 500 watts on a sunny day, and its water purification flow rate is 100 liters per hour, resulting in an energy efficiency ratio of 5 watts per liter. Based on the preset energy efficiency grading standards, this ratio falls into the "high efficiency" category. This calculation method provides a visual reflection of the terminal's energy utilization, providing a basis for optimizing energy allocation. The turbidity, pH, and heavy metal concentration parameters detected by the water quality sensor are classified using a support vector machine classification algorithm to generate a water quality compliance index for each water purification terminal. A support vector machine is a classification algorithm based on statistical learning theory that separates data into different categories by constructing a hyperplane. The detection parameters of the water quality sensor are important indicators for assessing water quality. Turbidity reflects the level of suspended solids in the water, pH indicates the acidity and alkalinity of the water, and heavy metal concentration directly affects water safety.

[0045] For example, if the test parameters for a terminal are turbidity 2 NTU, pH 7.5, and heavy metal concentration 0.01 mg / L, the support vector machine classifies it as "high quality" based on the trained model. This classification method can quickly and accurately assess water quality, providing a scientific basis for water quality management. The operating status of each water purification terminal is determined based on the energy conversion efficiency level and water quality compliance index. The energy conversion efficiency level and water quality compliance index are two key indicators for evaluating terminal operating status. The energy conversion efficiency level reflects the terminal's energy utilization, while the water quality compliance index reflects the terminal's water quality treatment capacity.

[0046] For example, if a terminal's energy conversion efficiency rating is "highly efficient" and its water quality compliance index is "high quality," its operating status can be judged as "good." By comprehensively analyzing these two indicators, we can comprehensively assess the terminal's operating status, promptly identify and resolve potential issues, and ensure stable operation and efficient management of the terminal.

[0047] Step S102 : extracting characteristic values ​​from the energy efficiency level and the water quality compliance index to generate the load capacity level and output parameter threshold of each water purification terminal.

[0048] When extracting characteristic values ​​from energy efficiency ratings, we first need to analyze the stability of energy consumption, the volatility of energy conversion efficiency, and the performance of peak energy consumption. Energy efficiency ratings are typically determined by a combination of historical and real-time monitoring data. For example, if a terminal's energy conversion efficiency consistently remains between 85% and 90% with minimal fluctuations, its energy efficiency rating is considered high. By inputting these characteristic values ​​into a clustering algorithm, a load capacity rating can be generated.

[0049] For example, if a terminal has a high energy efficiency rating and low peak energy consumption, its load capacity rating may be classified as advanced, allowing it to handle more tasks. In extracting the characteristic values ​​of the water quality compliance index, we focus on the stability of water quality and the compliance rate of key indicators.

[0050] For example, if a terminal's water quality compliance index remains above 95% for a long period of time, and key indicators such as turbidity and pH fluctuate within a small range, its water quality output parameter threshold can be set at a higher level. By analyzing these characteristic values, the terminal's output parameter threshold can be generated. For example, the water quality output parameter threshold for a terminal is set to turbidity ≤ 0.5 NTU and pH between 6.5 and 7.5. When generating the load capacity level of each water purification terminal, it is necessary to comprehensively consider the characteristic values ​​of the energy efficiency level and the water quality compliance index.

[0051] For example, if a terminal has a high energy efficiency rating and a high water quality compliance index, its load capacity may be classified as high, allowing it to handle more water purification tasks simultaneously. Conversely, if a terminal has a low energy efficiency rating and a medium water quality compliance index, its load capacity may be classified as medium, and its processing capacity may need to be limited when assigning tasks. The generation of output parameter thresholds needs to be adjusted based on the characteristic value of the water quality compliance index.

[0052] For example, if a terminal's water quality compliance index is high, its output parameter thresholds can be set to more stringent standards, such as turbidity ≤ 0.3 NTU and a pH value between 6.8 and 7.2. If a terminal's water quality compliance index is low, its output parameter thresholds may need to be relaxed, such as turbidity ≤ 1.0 NTU and a pH value between 6.0 and 8.0, to ensure the stability and safety of water quality output. Through the above steps, characteristic values ​​are extracted from the energy efficiency level and water quality compliance index to generate the load capacity level and output parameter thresholds for each terminal, providing a scientific basis for task allocation and energy management.

[0053] For example, terminals with a high load capacity level can be assigned high-energy consumption tasks first, while terminals with a higher output parameter threshold can undertake tasks with higher water quality requirements, thereby achieving optimal resource allocation and efficient operation of the system.

[0054] Specifically, the extracting of characteristic values ​​from the energy efficiency level and the water quality compliance index includes: extracting the energy consumption per unit time and the charging and discharging efficiency of the energy storage battery from the energy efficiency level as energy efficiency characteristic values; extracting the pollutant concentration change rate and the index fluctuation amplitude from the water quality compliance index as water quality dynamic characteristic values; and using a real-time clustering analysis algorithm based on K-means to classify the energy efficiency characteristic values ​​and water quality dynamic characteristic values ​​to generate the current load capacity level and water quality output parameter threshold of each water purification terminal.

[0055] Energy efficiency metric values, such as energy consumption per unit time and the charge / discharge efficiency of energy storage batteries, are extracted from the energy efficiency rating to quantify energy utilization efficiency and energy storage system performance. Energy consumption per unit time reflects the intensity of energy demand during equipment operation and is a key indicator for evaluating energy efficiency.

[0056] For example, if a water purification terminal consumes 5 kWh of electricity per hour during operation, this data can be directly used to analyze its energy consumption pattern. The charge and discharge efficiency of energy storage batteries describes the efficiency of energy conversion during the charge and discharge process, usually expressed as a percentage.

[0057] For example, a 90% charge / discharge efficiency for a certain energy storage battery means 10% energy loss during the charge / discharge process. By extracting these two eigenvalues, we can comprehensively assess energy utilization efficiency and the performance of the energy storage system, providing foundational data for subsequent load capacity analysis. Extracting the pollutant concentration change rate and index fluctuation amplitude from the water quality compliance index as dynamic water quality eigenvalues ​​is used to monitor water quality trends and stability. The pollutant concentration change rate reflects the rate of increase or decrease in pollutant concentration in water and is an important indicator for assessing dynamic changes in water quality.

[0058] For example, if a pollutant concentration at a water purification terminal drops from 10 mg / L to 5 mg / L during treatment, the rate of change can be calculated as a decrease of 5 mg / minute. The index fluctuation range describes the fluctuation range of the water quality compliance index and is a key parameter for assessing water quality stability.

[0059] For example, a water quality compliance index fluctuates between 80 and 90 over a single day, indicating that the water quality is generally stable but exhibits slight fluctuations. By extracting these two characteristic values, we can monitor the changes and stability of water quality in real time, providing a basis for setting thresholds for water quality output parameters.

[0060] A real-time K-means clustering analysis algorithm is used to classify energy efficiency and water quality dynamics to scientifically categorize the operating states of water purification terminals. The K-means algorithm calculates the similarity between eigenvalues ​​and divides the data into multiple clusters, each representing a different operating state.

[0061] For example, using energy consumption per unit time and the charge and discharge efficiency of energy storage batteries as input, the K-means algorithm can classify terminals into categories such as high energy consumption and low efficiency, or low energy consumption and high efficiency. Similarly, using the rate of change of pollutant concentration and the amplitude of index fluctuation as input, the K-means algorithm can classify water quality status into stable compliance and fluctuating compliance. Through real-time cluster analysis, the current load capacity level and water quality output parameter thresholds of each terminal can be generated, providing a scientific basis for optimizing system operation.

[0062] For example, a terminal may be classified as low-energy-consumption, high-efficiency, indicating high energy efficiency and the potential for further optimization of its load capacity. A terminal's water quality may be classified as fluctuating, indicating fluctuations in water quality and requiring adjustment of its output parameter thresholds to ensure stable water quality. Through these steps, key feature values ​​can be extracted from the energy efficiency rating and water quality compliance index, and classified using the K-means algorithm. Ultimately, the load capacity rating and water quality output parameter thresholds for each terminal are generated. This approach not only enables real-time monitoring of the system's operating status but also provides a scientific basis for system optimization, thereby improving energy efficiency and water quality stability.

[0063] For example, real-time cluster analysis of a distributed water purification system revealed that the energy efficiency characteristic values ​​of some terminals fell into the low-energy, high-efficiency category, but their water quality dynamic characteristic values ​​fell into the fluctuating, meeting-standard category. This indicated that these terminals performed well in terms of energy efficiency, but required further optimization of water quality stability. By adjusting the output parameter thresholds, it was possible to improve water quality stability while maintaining energy efficiency, thereby achieving overall system optimization.

[0064] Step S103: judging the energy status of each water purification terminal according to the load capacity level and the output parameter threshold, and generating a task allocation plan and resource allocation instructions.

[0065] When determining the energy status of each water purification terminal based on load capacity levels and output parameter thresholds, it's first necessary to clearly define the load capacity level. Load capacity levels are typically categorized based on the terminal's hardware configuration, processing power, and current operating status, such as low load, medium load, and high load. Output parameter thresholds refer to the allowable ranges for key water quality monitoring indicators, such as turbidity, pH, and dissolved oxygen. By monitoring these parameters in real time, it's possible to determine whether the terminal is operating stably.

[0066] For example, a terminal's turbidity value is 0.5 NTU, which is below the threshold of 1 NTU, indicating that it is in good operating condition. At the same time, its load capacity level is medium, indicating that its current processing capacity is moderate. When generating a task allocation plan, it is necessary to comprehensively consider the terminal's energy status and task requirements.

[0067] For example, suppose a region has three water purification terminals, A, B, and C. Terminal A has an energy reserve of 80% and an energy consumption rate of 2% per hour; Terminal B has an energy reserve of 60% and an energy consumption rate of 3% per hour; and Terminal C has an energy reserve of 40% and an energy consumption rate of 4% per hour. Based on this data, we can calculate the ratio α of each terminal's energy consumption rate to its remaining energy reserve: α for Terminal A is 0.025, α for Terminal B is 0.05, and α for Terminal C is 0.1. Clearly, Terminal A has the best energy status, while Terminal C has the worst. Therefore, when assigning tasks, high-priority tasks are prioritized for Terminal A, medium-priority tasks for Terminal B, and low-priority tasks for Terminal C to optimize overall energy efficiency. Resource allocation instructions are generated based on the task allocation plan and the real-time status of the terminals.

[0068] For example, when terminal A completes its current task, its load capacity level may drop from high load to medium load. At this time, resources can be dynamically adjusted to reallocate some tasks from terminal B to terminal A. At the same time, if the energy reserve of terminal C is less than 30%, the system can issue instructions to reduce its task volume or transfer its tasks to other terminals to avoid operational interruptions due to insufficient energy. This dynamic allocation mechanism can ensure that each terminal operates efficiently under limited energy conditions while meeting the stability requirements of water quality output. Through the above steps, the system can monitor the energy status of each terminal in real time, and dynamically generate task allocation plans and resource allocation instructions based on the load capacity level and output parameter threshold. This method not only improves energy utilization efficiency, but also ensures the stability and reliability of water quality output, providing strong support for the optimized operation of distributed water purification systems.

[0069] Specifically, the energy status of each water purification terminal is judged according to the load capacity level and the output parameter threshold, including: calculating the ratio α of the current energy consumption rate of each water purification terminal to the remaining energy reserve; if the ratio α is greater than or equal to the preset threshold β, the corresponding water purification terminal is judged to be an energy-insufficient terminal; based on the judgment result, a list of energy-insufficient terminals is generated, and the geographical location and communication delay data of the energy-insufficient terminals are extracted.

[0070] In distributed water purification systems, determining energy status is crucial for ensuring stable system operation. First, it's important to clarify the meaning of load capacity levels and output parameter thresholds. The load capacity level refers to the processing capacity of the water purification terminal under its current operating conditions, while the output parameter threshold represents the minimum water quality output standard required for the system to function properly. These two parameters enable the evaluation of the terminal's energy consumption. Calculating the ratio α of the current energy consumption rate to the remaining energy reserve is the core step in determining energy status. The energy consumption rate refers to the amount of energy consumed by the terminal per unit time, while the remaining energy reserve represents the total amount of energy currently remaining in the terminal.

[0071] For example, if a water purification terminal consumes 2 kilowatts of energy per hour and has 10 kilowatts of remaining energy, its ratio α is 0.2. This ratio reflects the terminal's energy shortage. The preset threshold β is a critical value set by the system to determine whether the terminal is in an energy-deficient state.

[0072] For example, the system sets β to 0.3. When a terminal's α value reaches or exceeds 0.3, it is identified as energy-deficient. This identification process must be performed in real time to ensure that the system can promptly detect potential energy issues. Generating a list of energy-deficient terminals is the basis for subsequent resource allocation. The system aggregates information on all terminals identified as energy-deficient into a single list for centralized management and processing. This list includes not only the terminal's identification information but also its geographic location and communication latency data.

[0073] For example, if a terminal experiencing energy shortage is located in Area A and has a communication delay of 20 milliseconds, this information will help the system quickly locate the problem terminal and plan resource allocation strategies. The extraction of geographic location and communication delay data is to ensure efficient resource allocation. Geographic location information helps the system determine the physical location of the terminal, while communication delay data is used to assess the communication efficiency between the terminal and its neighbors.

[0074] For example, the system can use geolocation information to identify multiple available resource terminals near a terminal experiencing energy shortages. It then uses communication delay data to select the terminal with the fastest response time for resource allocation. This series of steps effectively prevents operational interruptions in the distributed water purification system due to energy shortages. By monitoring and assessing energy status in real time, the system can proactively identify and resolve issues, ensuring the continuity and stability of the water treatment process. Furthermore, the generation of a list of energy-deficient terminals and the extraction of geolocation and communication delay data provide a scientific basis for resource allocation, further improving the overall efficiency of the system.

[0075] Specifically, the generation of task allocation plans and resource allocation instructions includes: using a task allocation algorithm based on a load balancing model to generate a preliminary water purification task optimization plan, and the load balancing model aims to minimize the load difference between terminals; adjusting the preliminary water purification task optimization plan through an energy consumption optimization function, and the energy consumption optimization function aims to minimize total energy consumption; generating resource allocation instructions based on the adjusted task optimization plan, and the resource allocation instructions include an energy allocation ratio and a task migration path.

[0076] A preliminary optimization plan for the water purification task is generated using a task allocation algorithm based on a load balancing model. The load balancing model aims to minimize the load difference between terminals. Specifically:

[0077] Defining the load variance function ,in represents the current load value of the i-th water purification terminal, represents the average load value of all water purification terminals, and N represents the total number of water purification terminals. The load balancing model minimizes D by adjusting the task load of each water purification terminal.

[0078] In actual applications, the load balancing model monitors the current load status of each water purification terminal and allocates tasks to terminals with lower loads, ensuring that the load of each terminal is as close to the average level as possible.

[0079] For example, consider three water purification terminals with loads of 60%, 70%, and 50%, respectively. The load balancing model will migrate some tasks from the more heavily loaded terminal to the less heavily loaded one, keeping the loads of all three terminals close to 60%, thus preventing any single terminal from being overloaded and impacting overall system performance. This allows the system to efficiently utilize the processing power of all terminals while preventing any single terminal from experiencing failure or performance degradation due to excessive load.

[0080] The preliminary water purification task optimization plan is adjusted by using an energy consumption optimization function, wherein the energy consumption optimization function aims to minimize total energy consumption, specifically:

[0081] Define the total energy consumption function ,in represents the energy consumption per unit time of the i-th water purification terminal, represents the task execution time of the i-th water purification terminal. Under the premise of satisfying the load balancing constraint, the task allocation scheme is further adjusted by optimizing the value of E.

[0082] When adjusting task allocation, the energy consumption optimization function will comprehensively consider the energy consumption of each terminal and the energy consumption cost of task migration.

[0083] For example, if one terminal has a low load but high energy consumption, while another terminal has a slightly higher load but lower energy consumption, the optimization function will prioritize tasks to the terminal with the lower energy consumption. The optimization function also considers the communication energy consumption during task migration, ensuring that communication latency along the migration path does not exceed 50ms. Through this adjustment, the system can minimize overall energy consumption while maintaining task execution efficiency, thereby achieving green energy conservation goals.

[0084] A resource allocation instruction is generated based on the adjusted task optimization plan, and the resource allocation instruction includes an energy allocation ratio and a task migration path. Specifically, the energy allocation ratio is calculated based on the remaining energy reserves and demand of each water purification terminal. The task migration path is determined by a shortest path algorithm (such as the Dijkstra algorithm or the A* algorithm) to ensure that communication delays are minimized during the resource allocation process.

[0085] The generation of resource allocation instructions needs to comprehensively consider the terminal's energy supply capacity and water quality treatment capacity.

[0086] For example, if one terminal has sufficient energy and strong water treatment capabilities, while another terminal has insufficient energy or limited treatment capabilities, the system will prioritize assigning tasks to the terminal with sufficient energy and matching treatment capabilities, and generate a specific energy allocation ratio and task migration path.

[0087] For example, a task can be migrated from energy-sufficient terminal A to energy-sufficient terminal B, with a designated energy allocation ratio of 70% for terminal B, while ensuring that the communication delay along the migration path does not exceed 50ms. In this way, the system can optimize energy utilization and resource allocation while ensuring task execution efficiency, maximizing overall performance.

[0088] Step S104: Send the task allocation plan and resource allocation instructions to the water purification terminal and receive the adjusted status data; update the energy efficiency level and water quality compliance index according to the adjusted status data, verify network stability and generate an optimization plan.

[0089] When issuing task allocation plans and resource allocation instructions to water purification terminals, it is first necessary to dynamically adjust the task priority of each terminal based on the current photovoltaic panel output power and the remaining capacity of the energy storage battery.

[0090] For example, if a terminal's photovoltaic panel output power is 500 watts and its energy storage battery has 80% remaining capacity, high-energy-consuming tasks, such as high-flow water purification, can be prioritized. Meanwhile, for a terminal with a photovoltaic panel output power of only 200 watts and a remaining battery capacity of 30%, low-energy-consuming tasks, such as low-flow water purification, can be assigned. This dynamic task allocation effectively utilizes solar energy resources and avoids energy waste. Upon receiving adjusted status data, the system monitors each terminal's water purification flow rate and water quality sensor parameters in real time.

[0091] For example, if a terminal's adjusted water flow rate is 10 liters per minute, the turbidity detected by the water quality sensor is 0.5 NTU, and the pH value is 7.2, these data will be used to update the energy efficiency rating and water quality compliance index. By comparing the data before and after the adjustment, the effectiveness of the resource allocation instructions can be evaluated and provide a basis for subsequent optimization. When updating the energy efficiency rating, the system recalculates the energy utilization efficiency of each terminal based on changes in the output power of the photovoltaic panels and the remaining capacity of the energy storage batteries.

[0092] For example, after adjustment, a terminal's photovoltaic panel output power is 600 watts, the remaining capacity of the energy storage battery is 90%, and the energy efficiency level is upgraded from B to A. This real-time updated energy efficiency level reflects the terminal's energy utilization under different conditions, providing a reference for subsequent resource allocation. When updating the water quality compliance index, the system reassesses the water quality treatment effect of each terminal based on changes in water quality sensor detection parameters.

[0093] For example, after adjustment, the turbidity at a terminal is 0.3 NTU, the pH value is 7.0, and the water quality compliance index increases from 80 to 90. This real-time updated water quality compliance index reflects the terminal's water quality treatment capacity under different conditions and provides a basis for subsequent optimization.

[0094] When verifying network stability, the system monitors each terminal's data transmission delay and packet loss rate. For example, a terminal's data transmission delay of 50 milliseconds and a packet loss rate of 0.1% indicate a stable network connection. Verifying network stability ensures that each terminal's status data is transmitted to the central control system in a timely and accurate manner, providing reliable data support for subsequent optimization. When generating optimization plans, the system comprehensively analyzes each terminal's energy efficiency level, water quality compliance index, and network stability, and proposes targeted improvement measures.

[0095] For example, for terminals with low energy efficiency ratings, we can recommend increasing the photovoltaic panel area or replacing high-efficiency energy storage batteries. For terminals with low water quality compliance, we can recommend replacing more advanced water purification filters. For terminals with poor network stability, we can recommend optimizing network connections or adding relay equipment. These optimization solutions can improve overall system efficiency and water quality treatment results.

[0096] Specifically, the task allocation plan and resource allocation instructions are issued to the water purification terminal, including: performing a supply and demand matching analysis between the remaining capacity of the energy-deficient terminal and the available resources of the neighboring terminal, wherein the neighboring terminal is a water purification terminal whose geographical distance or communication delay meets the preset conditions; specifically:

[0097] Define the supply-demand matching function ,in Indicates the amount of available resources of the neighboring terminal, Indicates the demand of energy-deficient terminals. , then the neighboring terminal is determined to be a candidate for allocating resources, where is the preset matching threshold.

[0098] The improved Hungarian algorithm is used to generate resource allocation instructions including energy allocation ratio and task migration path; specifically:

[0099] Introducing dynamic weight matrix: Based on the traditional Hungarian algorithm, dynamic weight parameters are added ,in represents the geographical distance between the i-th energy-deficient terminal and the j-th neighboring terminal, Indicates the communication delay between the two.

[0100] Add constraints: Add upper and lower limits on the energy allocation ratio during the matching process to ensure the safety and rationality of the allocation process.

[0101] The resource allocation instruction is sent to the corresponding water purification terminal via the information interconnection platform. The resource allocation instruction includes at least the following contents: the energy allocation ratio of each energy-deficient terminal; the specific node information of each task migration path; the estimated allocation completion time and communication delay.

[0102] To conduct a supply-demand matching analysis on the remaining capacity of the energy-deficient terminal and the available resources of the neighboring terminals, it is first necessary to clarify the specific values ​​of the remaining capacity of the energy-deficient terminal and the available resources of the neighboring terminals.

[0103] For example, suppose a terminal with insufficient energy has a remaining capacity of 500Wh, while its neighboring terminal has available resources of 2000Wh. A matching analysis is needed to determine whether this energy demand can be met. The key to this matching analysis is determining whether the geographical distance and communication latency of the neighboring terminals meet predefined conditions. These conditions are typically set as a geographical distance of ≤5km or a communication latency of ≤30ms.

[0104] For example, if the geographic distance between adjacent terminals is 4 km and the communication delay is 25 ms, the preset conditions are met and a supply-demand matching analysis can be performed. The purpose of this matching analysis is to ensure the feasibility and efficiency of resource allocation and avoid allocation failures or resource waste due to excessive distance or high communication delay. Using the modified Hungarian algorithm to generate resource allocation instructions that include energy allocation ratios and task migration paths requires an understanding of its basic principles and application scenarios. The modified Hungarian algorithm is an optimization matching algorithm that can find optimal solutions to many-to-many resource matching problems.

[0105] For example, in an energy allocation scenario, assuming there are three energy-deficient terminals and five neighboring terminals, the improved Hungarian algorithm can calculate which neighboring terminals each energy-deficient terminal should receive energy from, as well as the specific allocation ratio and task migration path. The allocation ratio refers to the proportion of energy that the energy-deficient terminal receives from neighboring terminals relative to its total demand. For example, if a terminal requires 1000Wh of energy, the allocation ratio might be 40% (400Wh) from neighboring terminal A and 60% (600Wh) from neighboring terminal B. The task migration path refers to the specific energy allocation path. For example, the path from neighboring terminal A to the energy-deficient terminal is A→C→D. The advantage of the improved Hungarian algorithm is that it comprehensively considers energy availability and water quality treatment capacity, ensuring that the allocated energy not only meets demand but also matches the terminal's water quality treatment capacity, thereby improving resource utilization efficiency. The information interconnection platform sends resource allocation instructions to the corresponding water purification terminal, but the functions and role of the information interconnection platform must be clearly defined. The information interconnection platform is a resource management platform based on network communication technology that can monitor and schedule the operating status of water purification terminals in real time.

[0106] For example, after analyzing the supply and demand matching between an energy-deficient terminal and neighboring terminals, the information interconnection platform will send the generated resource allocation instructions to the relevant terminals. The instructions sent typically include the energy allocation ratio, task migration path, and specific execution time.

[0107] For example, a command sent to neighboring terminal A might include "provide 400Wh of energy to terminal B, with the migration path A→C→D, and the execution time at 2:00 PM." The real-time and efficient nature of the information-based interconnected platform ensures the accurate execution of resource allocation instructions. By receiving status data from terminals, the platform can dynamically adjust allocation strategies to achieve optimal resource allocation. The above steps and technical topics together constitute the complete process for generating and issuing resource allocation instructions. Through the synergistic effects of supply and demand matching analysis, the improved Hungarian algorithm, and the information-based interconnected platform, it can effectively address the resource allocation issues faced by energy-deficient terminals and improve the operational efficiency and stability of distributed water purification systems.

[0108] Specifically, updating the energy efficiency level and water quality compliance index based on the adjusted status data includes: receiving the adjusted status data after each water purification terminal executes the resource allocation instruction; recalculating the energy conversion efficiency level and water quality compliance index based on the adjusted status data; and generating performance change data of each water purification terminal by comparing the energy efficiency level and water quality compliance index before and after adjustment.

[0109] Receiving adjustment status data from each water purification terminal after executing resource allocation instructions is the first step in updating energy efficiency ratings and water quality compliance indices. This adjustment status data includes key parameters such as water treatment volume, energy consumption, and water quality test results.

[0110] For example, after executing a resource allocation command, a terminal's water treatment capacity increased from 1,000 liters per hour to 1,200 liters, energy consumption decreased from 1.2 kWh per hour to 1.0 kWh, and water quality testing showed a decrease in turbidity from 1.0 NTU to 0.8 NTU. This data provides the basis for subsequent calculations. Recalculating the energy conversion efficiency rating and water quality compliance index based on the adjusted status data is a key step. The energy conversion efficiency rating is measured by the amount of water treated per unit of energy consumed. For example, before the adjustment, a terminal could process 833 liters of water per kilowatt-hour; after the adjustment, this increased to 1,200 liters, raising its efficiency rating from C to B. The water quality compliance index is calculated based on multiple indicators, including turbidity, pH, and dissolved oxygen. For example, before the adjustment, the water quality compliance index for a terminal was 85, but after the adjustment, it increased to 90, indicating a significant improvement in water quality. Comparing the energy efficiency rating and water quality compliance index before and after the adjustment generates performance change data for each water purification terminal, which is key to evaluating the effectiveness of resource allocation.

[0111] For example, if a terminal's energy efficiency rating improves from C to B, and its water quality compliance index increases from 85 to 90, this indicates that resource allocation instructions have effectively improved terminal performance. This performance change data can be used to optimize subsequent resource allocation strategies, such as prioritizing more resources for terminals with lower energy efficiency or strengthening water quality monitoring and regulation at terminals with lower water quality compliance indices. The purpose of updating energy efficiency ratings and water quality compliance indices is to achieve closed-loop control of the system. By continuously receiving adjustment status data and recalculating key indicators, the system can dynamically adjust resource allocation strategies to ensure that each terminal is always operating in optimal conditions.

[0112] For example, if a terminal's energy efficiency level or water quality compliance index declines, the system can immediately issue new resource allocation instructions to prevent further performance deterioration. This closed-loop control mechanism can significantly improve the overall efficiency and stability of the water purification system.

[0113] Specifically, the verification of network stability and generation of optimization solutions include: calculating the data transmission delay reduction rate and terminal response time optimization rate after task allocation and resource allocation; generating a stability threshold based on a linear regression model trained with past historical operation data; and determining whether the data transmission delay reduction rate and terminal response time optimization rate reach the stability threshold. If not, the task allocation parameters are adjusted based on the load deviation rate and the water quality fluctuation coefficient.

[0114] Calculate the data transmission delay reduction rate and terminal response time optimization rate after task allocation and resource allocation. The data transmission delay reduction rate is calculated by comparing the difference between the initial average delay and the adjusted average delay, and then dividing the difference by the initial average delay to obtain the percentage value.

[0115] For example, if the initial average latency is 100 milliseconds and the adjusted average latency is 80 milliseconds, the data transmission latency reduction rate is (100-80) / 100 × 100% = 20%. The terminal response time optimization rate is calculated by comparing the difference between the initial terminal response time and the adjusted terminal response time, and then dividing it by the initial terminal response time to obtain the percentage value.

[0116] For example, if the initial terminal response time is 200 milliseconds and the adjusted terminal response time is 150 milliseconds, the terminal response time optimization rate is (200 - 150) / 200 × 100% = 25%. The stability threshold is generated using a linear regression model trained based on historical operational data. The linear regression model is a mathematical model established by analyzing the relationships between variables such as task allocation, resource allocation, data transmission latency, and terminal response time in historical data.

[0117] For example, historical data shows that network stability reaches its peak when the data transmission delay reduction rate reaches 15% and the terminal response time optimization rate reaches 20%. Therefore, these two values ​​can be used as stability thresholds. A determination is then made as to whether the data transmission delay reduction rate and the terminal response time optimization rate have reached these stability thresholds.

[0118] For example, if the data transmission delay reduction rate is 10% and the terminal response time optimization rate is 18%, both the 15% and 20% stability thresholds are not met. If the stability thresholds are not met, the task allocation parameters are adjusted based on the load deviation rate and water quality fluctuation coefficient. The load deviation rate refers to the degree of deviation between the actual load and the expected load, and the water quality fluctuation coefficient refers to the magnitude of the change in water quality parameters.

[0119] For example, if the load deviation rate is 5% and the water quality fluctuation coefficient is 3%, the task allocation strategy can be adjusted based on these two parameters, such as increasing or reducing the task load for some terminals to further optimize network stability. This generates an updated water purification task optimization plan.

[0120] For example, based on the adjusted task allocation parameters, the water purification tasks of each terminal are reallocated to ensure that data transmission delay and terminal response time are further reduced, thereby improving overall network stability.

[0121] Step S105 , iteratively adjusting the optimization plan until a preset termination condition is met, generating a final task allocation plan and sending it to each water purification terminal.

[0122] By iteratively adjusting the optimization plan until the preset termination conditions are met, the final task allocation plan is generated and distributed to each water purification terminal. During the iterative adjustment process, the operating efficiency and water quality compliance of each terminal are first analyzed based on the current operating status of the water purification terminal and water quality test data.

[0123] For example, if a terminal's water purification efficiency falls below a preset threshold, this could be due to dust accumulation on the solar panels, reducing energy conversion efficiency, or a clogged filter, slowing water purification. In this case, the system will adjust the task allocation plan for that terminal accordingly, such as reducing its water purification workload or increasing maintenance reminders. Through multiple iterations, the system gradually optimizes the task allocation for each terminal, ensuring that the overall network's energy efficiency and water quality stability meet preset targets. As the preset termination conditions are met, the system dynamically evaluates the effectiveness of each iteration.

[0124] For example, the effectiveness of an optimization plan can be determined by comparing water quality parameters and energy consumption data before and after adjustments. If, after a particular adjustment, water quality compliance improves from 95% to 97%, but energy consumption increases by 10%, the system will further consider whether further adjustments are necessary. Termination conditions may include water quality compliance remaining stable above 98% and energy consumption fluctuations of less than 5%. Only when all conditions are met will the system generate a final task allocation plan and distribute it to each terminal. When generating the final task allocation plan, the system comprehensively considers each terminal's load capacity, geographical location, and water quality requirements.

[0125] For example, if a terminal in a certain area is remote and has limited solar energy resources, the system will assign it fewer water purification tasks while increasing the tasks for neighboring terminals to ensure stable water supply across the entire area. This dynamic allocation maximizes energy efficiency while ensuring water quality meets standards and reduces the risk of individual terminal failures. When issuing task allocation plans, the system utilizes efficient data transmission protocols to ensure that each terminal receives and executes the new plan promptly.

[0126] For example, through IoT technology, the system sends optimization plans in encrypted data packets to each terminal, which then makes immediate adjustments upon receiving the data. This real-time delivery mechanism enables rapid response to environmental changes, such as sudden weather changes or fluctuations in water quality, ensuring that the water purification system is always operating optimally.

[0127] Specifically, the optimization plan is iteratively adjusted until the preset termination conditions are met, including: adjusting the task allocation plan and resource allocation instructions according to the network stability improvement value; terminating the adjustment if the change in the network stability improvement value is less than the preset threshold for multiple consecutive iterations; terminating the adjustment if the total number of iterations reaches the preset upper limit; generating the final task allocation plan based on the final adjustment result and sending it to each water purification terminal.

[0128] During the iterative optimization process, the task allocation plan and resource allocation instructions must first be dynamically adjusted based on the network stability improvement value. The network stability improvement value is a key indicator used to measure the degree of optimization of the network operation status. Its value changes reflect the effectiveness of task allocation and resource allocation.

[0129] For example, in one iteration, the network stability improvement value increased from 5% to 8%, indicating that the current task allocation scheme and resource allocation instructions have a positive impact on network stability. At this point, the system will continue to optimize the task allocation scheme, for example by adjusting the data transmission priority of each terminal or reallocating computing resources, to further improve network stability. If the change in the network stability improvement value is less than a preset threshold (such as 2%) and occurs for multiple (such as three) consecutive iterations, the system will terminate the adjustment. This is because the small change in the improvement value indicates that the optimization scheme is close to optimal, and further adjustments are unlikely to bring significant results.

[0130] For example, during a particular iteration, if the network stability improvement value changes by 1.5%, 1.8%, and 1.7%, respectively, and remains less than 2% for three consecutive times, the system will determine that the optimization has reached a stable state and cease iterative adjustments. This mechanism avoids ineffective resource consumption and improves system efficiency. The system also terminates adjustments if the total number of iterations reaches a preset upper limit. This is to prevent the optimization process from falling into an infinite loop or failing to converge for a long time.

[0131] For example, if the maximum number of iterations is set to 10, and if the stability improvement threshold is still not met after the 10th iteration, the system will forcibly terminate the optimization process and generate a final task allocation plan based on the current results. This mechanism ensures the timeliness of the optimization process and prevents over-optimization from affecting system operation. Finally, the system generates a final task allocation plan based on the adjustment results and distributes it to each water purification terminal.

[0132] For example, after multiple iterations, the system determined an optimal task allocation scheme that prioritizes high-priority tasks for terminals with better network performance while dynamically adjusting the task load based on each terminal's resource load. This scheme effectively improves overall network stability and task execution efficiency, ensuring the efficient operation of the water purification system. Through this iterative adjustment process, the system gradually optimizes task allocation and resource allocation, ultimately achieving both improved network stability and task execution efficiency.

[0133] The embodiment of the present invention further provides an information interconnection system for solar water purification terminals, specifically comprising:

[0134] a data acquisition module configured to obtain status data of a plurality of water purification terminals, wherein the status data includes at least energy parameters and water quality parameters;

[0135] an evaluation module configured to evaluate the energy efficiency and water quality compliance of each water purification terminal based on the status data to obtain an energy efficiency grade and a water quality compliance index;

[0136] a feature extraction module configured to extract feature values ​​from the energy efficiency level and the water quality compliance index to generate a load capacity level and an output parameter threshold for each water purification terminal;

[0137] a decision control module configured to determine the energy status of each water purification terminal according to the load capacity level and output parameter threshold, and generate a task allocation plan and resource allocation instructions;

[0138] a communication control module configured to issue the task allocation plan and resource allocation instructions to the water purification terminal and receive adjusted status data;

[0139] an optimization verification module configured to update the energy efficiency level and water quality compliance index according to the adjusted status data, verify network stability and generate an optimization plan;

[0140] The iterative execution module is configured to iteratively adjust the optimization plan until a preset termination condition is met, generate a final task allocation plan and send it to each water purification terminal.

[0141] The above description is only a preferred embodiment of the present invention. Therefore, any equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the protection scope of the patent application of the present invention.

Claims

1. A solar water purification terminal information interconnection method, characterized in that: The steps include: Acquiring status data of a plurality of water purification terminals, wherein the status data includes at least energy parameters and water quality parameters; Evaluate the energy efficiency and water quality compliance of each water purification terminal based on the status data to obtain an energy efficiency grade and a water quality compliance index; Extracting characteristic values ​​from the energy efficiency level and water quality compliance index to generate a load capacity level and a water quality output parameter threshold for each water purification terminal; According to the load capacity level and water quality output parameter threshold, the energy status of each water purification terminal is judged, and a task allocation plan and resource allocation instructions are generated; Sending the task allocation plan and resource allocation instructions to the water purification terminal and receiving the adjusted status data; updating the energy efficiency level and water quality compliance index according to the adjusted status data, verifying network stability and generating an optimization plan; By iteratively adjusting the optimization plan until the preset termination conditions are met, a final task allocation plan is generated and sent to each water purification terminal; The extracting characteristic values ​​from the energy efficiency level and the water quality compliance index includes: Extracting the energy consumption per unit time and the energy storage battery charging and discharging efficiency from the energy efficiency level as energy efficiency characteristic values; Extracting the pollutant concentration change rate and index fluctuation amplitude from the water quality compliance index as water quality dynamic characteristic values; A real-time clustering analysis algorithm based on K-means is used to classify the energy efficiency characteristic values ​​and the water quality dynamic characteristic values ​​to generate the current load capacity level and water quality output parameter threshold of each water purification terminal; The step of determining the energy status of each water purification terminal according to the load capacity level and the water quality output parameter threshold includes: Calculate the ratio α of the current energy consumption rate and the remaining energy reserve of each water purification terminal; If the ratio α is greater than or equal to a preset threshold β, the corresponding water purification terminal is determined to be an energy-deficient terminal; Generate a list of energy-deficient terminals according to the determination result, and extract geographic location and communication delay data of the energy-deficient terminals; Generating a task allocation plan and resource allocation instructions includes: A preliminary optimization plan for the water purification task is generated using a task allocation algorithm based on a load balancing model, where the load balancing model aims to minimize the load difference between terminals. Adjusting the preliminary water purification task optimization plan through an energy consumption optimization function, wherein the energy consumption optimization function aims to minimize total energy consumption; A resource allocation instruction is generated according to the adjusted task optimization plan, wherein the resource allocation instruction includes an energy allocation ratio and a task migration path.

2. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: The evaluating of the energy efficiency and water quality compliance of each water purification terminal according to the status data includes: A preset energy efficiency evaluation model is used to calculate the ratio of photovoltaic panel output power to water purification flow rate, generating the energy conversion efficiency level of each water purification terminal; The turbidity, pH value and heavy metal concentration in the water quality sensor detection parameters are classified using the support vector machine classification algorithm to generate the water quality compliance index for each water purification terminal; The operating status of each water purification terminal is determined according to the energy conversion efficiency level and the water quality compliance index.

3. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: The sending of the task allocation plan and resource allocation instructions to the water purification terminal includes: Performing a supply-demand matching analysis between the remaining capacity of the energy-deficient terminal and the available resources of neighboring terminals, wherein the neighboring terminals are water purification terminals whose geographical distance or communication delay meets preset conditions; The improved Hungarian algorithm is used to generate resource allocation instructions including energy allocation ratio and task migration path; The resource allocation instruction is sent to the corresponding water purification terminal through the information interconnection platform.

4. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: The updating of the energy efficiency level and the water quality compliance index according to the adjusted status data includes: Receive adjustment status data after each water purification terminal executes resource allocation instructions; Recalculate the energy conversion efficiency level and water quality compliance index based on the adjusted status data; By comparing the energy efficiency levels and water quality compliance indexes before and after adjustment, performance change data for each water purification terminal is generated.

5. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: Verifying network stability and generating an optimization solution include: Calculate the data transmission delay reduction rate and terminal response time optimization rate after task allocation and resource allocation; A stability threshold is generated by a linear regression model trained based on past historical operating data; It is determined whether the data transmission delay reduction rate and the terminal response time optimization rate reach the stability threshold; if not, the task allocation parameters are adjusted based on the load deviation rate and the water quality fluctuation coefficient.

6. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: The iterative adjustment of the optimization scheme until a preset termination condition is satisfied includes: Adjust task allocation plans and resource allocation instructions based on the network stability improvement value; If the change in the network stability improvement value is less than the preset threshold for multiple consecutive iterations, the adjustment is terminated; If the total number of iterations reaches the preset upper limit, the adjustment is terminated; The final task allocation plan is generated based on the final adjustment results and distributed to each water purification terminal.

7. An information interconnection system for solar water purification terminals, characterized in that: include: a data acquisition module configured to obtain status data of a plurality of water purification terminals, wherein the status data includes at least energy parameters and water quality parameters; an evaluation module configured to evaluate the energy efficiency and water quality compliance of each water purification terminal based on the status data to obtain an energy efficiency grade and a water quality compliance index; a feature extraction module configured to extract feature values ​​from the energy efficiency level and the water quality compliance index to generate a load capacity level and a water quality output parameter threshold for each water purification terminal; The extracting characteristic values ​​from the energy efficiency level and the water quality compliance index includes: Extracting the energy consumption per unit time and the energy storage battery charging and discharging efficiency from the energy efficiency level as energy efficiency characteristic values; Extracting the pollutant concentration change rate and index fluctuation amplitude from the water quality compliance index as water quality dynamic characteristic values; A real-time clustering analysis algorithm based on K-means is used to classify the energy efficiency characteristic values ​​and the water quality dynamic characteristic values ​​to generate the current load capacity level and water quality output parameter threshold of each water purification terminal; a decision control module configured to determine the energy status of each water purification terminal according to the load capacity level and the water quality output parameter threshold, and generate a task allocation plan and resource allocation instructions; The step of determining the energy status of each water purification terminal according to the load capacity level and the water quality output parameter threshold includes: Calculate the ratio α of the current energy consumption rate and the remaining energy reserve of each water purification terminal; If the ratio α is greater than or equal to a preset threshold β, the corresponding water purification terminal is determined to be an energy-deficient terminal; Generate a list of energy-deficient terminals according to the determination result, and extract geographic location and communication delay data of the energy-deficient terminals; Generating a task allocation plan and resource allocation instructions includes: A preliminary optimization plan for the water purification task is generated using a task allocation algorithm based on a load balancing model, where the load balancing model aims to minimize the load difference between terminals. Adjusting the preliminary water purification task optimization plan through an energy consumption optimization function, wherein the energy consumption optimization function aims to minimize total energy consumption; generating a resource allocation instruction according to the adjusted task optimization plan, wherein the resource allocation instruction includes an energy allocation ratio and a task migration path; a communication control module configured to issue the task allocation plan and resource allocation instructions to the water purification terminal and receive adjusted status data; an optimization verification module configured to update the energy efficiency level and water quality compliance index according to the adjusted status data, verify network stability and generate an optimization plan; The iterative execution module is configured to iteratively adjust the optimization plan until a preset termination condition is met, generate a final task allocation plan and send it to each water purification terminal.

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