Informatization interconnection method and system of solar water purification terminal

Abstract

The invention discloses an informatization interconnection method and system for solar water purification terminals. The method comprises the following steps: acquiring state data of a plurality of water purification terminals; evaluating the energy efficiency and water quality standard conditions of each water purification terminal according to the state data to obtain an energy efficiency grade and a water quality standard index; extracting characteristic values from the energy efficiency grade and the water quality standard index, and generating a load capacity grade and an output parameter threshold value of each water purification terminal; issuing the task allocation scheme and the resource allocation instruction to the water purification terminal, and receiving adjusted state data; updating the energy efficiency grade and the water quality standard index according to the adjusted state data, verifying the network stability and generating an optimization scheme; and the optimization scheme is iteratively adjusted until a preset termination condition is met, and a final task allocation scheme is generated and issued to each water purification terminal, so that the management problem of the distributed water purification terminals is effectively solved, and the overall performance of the system is remarkably improved.

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 a solar water purification terminal. Background Art

[0002] Existing solar water purification solutions are mostly based on single-terminal operation, lacking coordination mechanisms between devices. This independent operation mode often exposes problems of inefficiency and uneven resource utilization when facing large-scale demand or complex environments, especially in scenarios with energy fluctuations or large differences in water quality, making it difficult to ensure the overall stability and purification capacity of the system.

[0003] The current limitations of solar water purification systems are mainly reflected in the lack of interconnection and collaboration between devices, resulting in isolated operating data, unreasonable task allocation, and limited energy efficiency. Traditional systems usually only focus on single-point optimization, ignoring the potential of networked operations, and are therefore unable to dynamically adapt to changing needs and environmental conditions. This limitation makes it difficult for water purification systems to achieve the goals of scale and efficiency in actual deployment.

[0004] Looking further, the core challenges focus on three technical factors: how to achieve data sharing, load balancing, and resource allocation among multiple terminals. Due to the lack of a data sharing mechanism, each terminal cannot understand each other's operating status and water quality treatment requirements in real time, resulting in a lack of intelligent basis for task allocation; insufficient load balancing causes some terminals to operate at overload, while other terminal resources are idle; the difficulty of resource allocation when energy is insufficient directly affects the stability and reliability of the system under extreme conditions.

[0005] Therefore, how to achieve data sharing among multiple solar water purification terminals through an information interconnection 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 scheme of the present invention is as follows:

[0008] On the one hand, the present invention provides an information interconnection method for a solar water purification terminal, 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 according to the status data, and 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, and generate a load capacity level and an output parameter threshold of each water purification terminal; judge the energy status of each water purification terminal according to 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 according to 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 an energy conversion efficiency level for 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 classifying the energy efficiency characteristic values ​​and the water quality dynamic characteristic values ​​using a real-time clustering analysis algorithm based on K-means 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-insufficient terminal; a list of energy-insufficient terminals is generated according to the judgment result, and the geographical location and communication delay data of the energy-insufficient terminal 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, 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, the energy consumption optimization function aims to minimize total energy consumption; generating resource allocation instructions based on the adjusted task optimization plan, the resource allocation instructions including 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, updating the energy efficiency level and water quality compliance index according to the adjusted status data includes: receiving adjusted status data after each water purification terminal executes a resource allocation instruction; recalculating the energy conversion efficiency level and water quality compliance index according to the adjusted status data; and generating performance change data for each water purification terminal by comparing the energy efficiency level and water quality compliance index before and after the adjustment.

[0016] Furthermore, verifying network stability and generating an optimization plan includes: 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 plan is iteratively adjusted until a preset termination condition is 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 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 plan 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 a solar water purification terminal, comprising:

[0019] A data acquisition module, configured to obtain status data of a plurality of water purification terminals, wherein the status data at least includes 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 according to the status data, and 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 of each water purification terminal;

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

[0023] A communication control module, configured to send 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] The present invention addresses the problems of low efficiency, unstable water quality, and poor system reliability of traditional decentralized solar water purification terminals. The present invention integrates decentralized terminals into a collaboratively operated intelligent network through a technical chain of centralized data management, intelligent multi-objective optimization, and dynamic closed-loop verification. The present invention achieves a comprehensive improvement in energy utilization efficiency, water quality stability, and system reliability, and provides a feasible technical framework for the deployment of large-scale distributed water purification systems. Through intelligent upgrades, the present invention effectively solves the management problems of decentralized water purification terminals, significantly improves the overall performance of the system, provides a safe and stable supply of drinking water for remote areas, and promotes the realization of sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, but the implementation scope 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 at least includes energy parameters and water quality parameters; evaluating the energy efficiency and water quality compliance of each water purification terminal according to the status data, and obtaining an energy efficiency grade and a water quality compliance index.

[0032] When obtaining the status data of multiple water purification terminals, it is first necessary to clarify the specific contents of energy parameters and water quality parameters. Energy parameters usually include the output power of photovoltaic panels and the remaining capacity of energy storage batteries. These data reflect the energy supply capacity and reserve of solar water purification terminals. 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 when there is insufficient light. Water quality parameters include water purification flow 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 encrypted and transmitted in real time to ensure the integrity and security of the data. When evaluating the energy efficiency of each water purification terminal based on the status data, it is necessary to conduct a comprehensive analysis in combination with the output power of photovoltaic panels and water purification flow.

[0033] For example, the photovoltaic panel output power of a terminal is 500 watts and the clean water flow rate is 100 liters per hour. By comparing the energy consumption and clean water output of different terminals, the energy conversion efficiency of each terminal can be calculated. Assuming that the energy conversion efficiency of terminal A is 80%, while that of terminal B is only 60%, terminal A has a higher energy efficiency level. This evaluation process helps to identify terminals with low energy utilization efficiency, thereby providing a basis for further optimization. In the assessment of water quality compliance, water quality sensor detection parameters are key indicators.

[0034] For example, the water quality test results of a terminal show that the turbidity is 1NTU, the pH value is 7.5, and the dissolved oxygen is 8mg / L. These data all meet the drinking water standards, so the water quality compliance index of this terminal is high. If the turbidity of another terminal exceeds 5NTU or the pH value exceeds the range of 6.5-8.5, its water quality compliance index is low. Through this assessment, terminals with substandard water quality can be discovered in a timely manner, and corresponding adjustment measures can be taken to ensure that the output water quality of the water purification system meets the requirements. This evaluation method based on status data can fully reflect the operating status of the water purification terminal and provide a scientific basis for resource allocation and system optimization.

[0035] For example, when the energy efficiency level of a terminal is low, its operation mode can be adjusted or energy storage equipment can be added; when the water quality compliance index is low, the filter element can be checked or the water purification process can be adjusted. Through this dynamic evaluation and adjustment, the overall performance and reliability of the distributed water purification system can be significantly improved, 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 batteries, 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 of each water purification terminal.

[0037] Receiving the status data periodically transmitted by each water purification terminal through the information interconnection platform is the basis for remote monitoring and management. The information interconnection platform usually uses the Internet of Things technology to connect water purification terminals distributed in different geographical locations to achieve real-time data transmission.

[0038] For example, a solar water purification terminal in a certain area will send status data to the platform once an hour, including photovoltaic panel output power, remaining capacity of energy storage batteries, water purification flow rate, and water quality sensor detection parameters. The output power of photovoltaic panels reflects the efficiency of converting solar energy into electrical energy, the remaining capacity of energy storage batteries shows the current power reserve of the energy storage system, the water purification flow rate indicator is used to evaluate the water purification capacity of the equipment, and the water quality sensor detection parameters directly reflect the safety of water quality. The periodic transmission of these data ensures that managers can understand the operating status of the equipment in a timely manner and provide data support for subsequent analysis and decision-making. Encrypting and storing status data is an important part of ensuring data security. Since status data involves equipment operating parameters and water quality information, data security is crucial.

[0039] For example, the platform can use the AES encryption algorithm to encrypt the transmitted data to ensure that the data will not be maliciously intercepted or tampered with during the transmission process. The encrypted data will be stored in the cloud server or local database, and the data will be backed up during storage to prevent data loss. Through encryption and storage, not only can the privacy and integrity of the data be protected, but also a reliable data source can be provided for subsequent data analysis. Extracting status data from the information interconnection platform according to the preset time interval and generating 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 set to extract status data from the database every 24 hours, and classify and organize these data by terminal number to form a status data set for each terminal. These sets can be used to generate historical operation reports of the equipment, or as basic data for training machine learning models.

[0041] For example, by analyzing the output power data of photovoltaic panels over the past 30 days, it is possible to predict the solar power generation in the future, thereby optimizing the charging and discharging strategy of the energy storage battery. In this way, managers can monitor and manage water purification terminals more efficiently, ensuring their 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; determining the operating status of each water purification terminal based on the energy conversion efficiency level and the water quality compliance index.

[0043] The preset energy efficiency evaluation model is used to calculate the ratio of the photovoltaic panel output power to the clean water flow rate, and generate the energy conversion efficiency level of each water purification terminal. The core of the energy efficiency evaluation model is to evaluate the energy utilization efficiency by quantifying the relationship between the photovoltaic panel output power and the clean water flow rate. The output power of the photovoltaic panel is affected by factors such as light intensity and temperature, while the clean water flow rate depends on the operating status of the water pump and the efficiency of the filtration system.

[0044] For example, the photovoltaic panel of a terminal has an output power of 500 watts on a sunny day, and the water purification flow rate is 100 liters per hour, and its energy efficiency ratio is 5 watts / liter. According to the preset energy efficiency classification standard, this ratio falls into the "high efficiency" level. Through this calculation method, the energy utilization of the terminal can be intuitively reflected, providing a basis for optimizing energy configuration. The turbidity, pH value and heavy metal concentration in the water quality sensor detection parameters are classified by the support vector machine classification algorithm to generate the water quality compliance index of each water purification terminal. Support vector machine is a classification algorithm based on statistical learning theory, which divides data of different categories by constructing a hyperplane. The detection parameters of water quality sensors are important indicators for evaluating water quality. Turbidity reflects the content of suspended matter in the water body, pH value indicates the acidity and alkalinity of the water body, and heavy metal concentration is directly related to the safety of the water body.

[0045] For example, if the detection parameters of a terminal are turbidity 2NTU, pH value 7.5, and heavy metal concentration 0.01mg / L, the support vector machine will classify it as "high quality" based on the trained model. This classification method can quickly and accurately evaluate water quality and provide a scientific basis for water quality management. The operating status of each water purification terminal is determined according to 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 the operating status of the terminal. The energy conversion efficiency level reflects the energy utilization of the terminal, while the water quality compliance index reflects the water quality treatment capacity of the terminal.

[0046] For example, if the energy conversion efficiency level of a terminal is "high efficiency" and the water quality compliance index is "high quality", its operating status can be judged as "good". By comprehensively analyzing these two indicators, the operating status of the terminal can be comprehensively evaluated, potential problems can be discovered and solved in a timely manner, and stable operation and efficient management of the terminal can be ensured.

[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 eigenvalues ​​from energy efficiency levels, 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 levels are usually determined by historical data and real-time monitoring data. For example, if the energy conversion efficiency of a terminal is maintained between 85% and 90% for a long time with little fluctuation, its energy efficiency level is high. By inputting these eigenvalues ​​into the clustering algorithm, the load capacity level can be generated.

[0049] For example, if the energy efficiency level of a terminal is high and its peak energy consumption is low, its load capacity level may be classified as high and can undertake more tasks. In the extraction of characteristic values ​​of water quality compliance index, the focus is on the stability of water quality and the compliance rate of key indicators.

[0050] For example, if the water quality compliance index of a terminal remains above 95% for a long time, and the fluctuation range of key indicators such as turbidity and pH value is small, then its water quality output parameter threshold can be set to a higher level. By analyzing these characteristic values, the output parameter threshold of the terminal can be generated. For example, the water quality output parameter threshold of a terminal is set to turbidity ≤ 0.5 NTU and pH value 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 energy efficiency level and water quality compliance index.

[0051] For example, if a terminal has a high energy efficiency level and a high water quality compliance index, its load capacity level may be classified as high, and it can handle more water purification tasks at the same time. On the contrary, if a terminal has a low energy efficiency level and a medium water quality compliance index, its load capacity level may be classified as medium, and its processing capacity needs to be limited when assigning tasks. The generation of the output parameter threshold needs to be adjusted according to the characteristic value of the water quality compliance index.

[0052] For example, if the water quality compliance index of a terminal is high, its output parameter threshold can be set to a more stringent standard, such as turbidity ≤ 0.3NTU and pH value between 6.8 and 7.2. If the water quality compliance index of a terminal is low, its output parameter threshold may need to be relaxed, such as turbidity ≤ 1.0NTU and 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 energy efficiency level and water quality compliance index, and the load capacity level and output parameter threshold of each terminal are generated, which can provide 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 allocation of resources and efficient operation of the system.

[0054] Specifically, 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 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 classifying the energy efficiency characteristic values ​​and water quality dynamic characteristic values ​​using a real-time clustering analysis algorithm based on K-means to generate the current load capacity level and water quality output parameter threshold of each water purification terminal.

[0055] The energy consumption per unit time and the energy storage battery charging and discharging efficiency are extracted from the energy efficiency level as energy efficiency characteristic values ​​in order to quantify the efficiency of energy utilization and the performance of the energy storage system. The energy consumption per unit time reflects the intensity of the energy demand of the equipment during operation and is an important indicator for evaluating energy utilization efficiency.

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

[0057] For example, the charging and discharging efficiency of a certain energy storage battery is 90%, which means that there is a 10% energy loss during the charging and discharging process. By extracting these two characteristic values, the efficiency of energy utilization and the performance of the energy storage system can be comprehensively evaluated, providing basic 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 characteristic values ​​is to monitor the changing trend and stability of water quality. The pollutant concentration change rate reflects the rate of increase or decrease of pollutant concentration in water and is an important indicator for evaluating the dynamic changes of water quality.

[0058] For example, during the treatment process of a water purification terminal, the pollutant concentration dropped from 10 mg per liter to 5 mg per liter, and the rate of change can be calculated as a decrease of 5 mg per unit time. The index fluctuation range describes the fluctuation range of the water quality compliance index and is a key parameter for evaluating water quality stability.

[0059] For example, the fluctuation range of a water quality compliance index in a day is 80 to 90, indicating that the water quality is generally stable but has slight fluctuations. By extracting these two characteristic values, the changes and stability of water quality can be monitored in real time, providing a basis for setting the threshold of water quality output parameters.

[0060] The K-means-based real-time clustering analysis algorithm is used to classify the energy efficiency characteristic values ​​and water quality dynamic characteristic values ​​in order to scientifically divide the operating status of the water purification terminal. The K-means algorithm divides the data into multiple clusters by calculating the similarity between the characteristic values, and each cluster represents an operating status.

[0061] For example, by taking the energy consumption per unit time and the charging and discharging efficiency of the energy storage battery as input, the K-means algorithm can classify the terminals into categories such as high energy consumption and low efficiency, low energy consumption and high efficiency, etc. Similarly, by taking the pollutant concentration change rate and the exponential fluctuation amplitude as input, the K-means algorithm can classify the water quality status into categories such as stable compliance and fluctuating compliance. Through real-time cluster analysis, the current load capacity level and water quality output parameter threshold of each terminal can be generated, providing a scientific basis for the optimized operation of the system.

[0062] For example, a terminal is classified as a low-energy-consumption, high-efficiency category, indicating that its energy utilization efficiency is high and its load capacity can be further optimized; the water quality status of a terminal is classified as a fluctuating compliance category, indicating that its water quality fluctuates and its output parameter threshold needs to be adjusted to ensure stable water quality. Through the above steps, key feature values ​​can be extracted from the energy efficiency level and water quality compliance index, and the K-means algorithm can be used to classify them, and finally the load capacity level and water quality output parameter threshold of each terminal are generated. This method can not only monitor the operating status of the system in real time, but also provide a scientific basis for the optimization of the system, thereby improving energy utilization efficiency and water quality stability.

[0063] For example, through real-time cluster analysis, a distributed water purification system found that the energy efficiency characteristic values ​​of some terminals belong to the low energy consumption and high efficiency category, but their water quality dynamic characteristic values ​​belong to the fluctuation compliance category, indicating that these terminals have good performance in energy efficiency, but the water quality stability needs to be further optimized. By adjusting the output parameter threshold, the water quality stability can be improved while ensuring energy efficiency, thereby achieving overall optimization of the system.

[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 judging the energy status of each water purification terminal based on the load capacity level and output parameter threshold, it is first necessary to clarify the definition of the load capacity level. The load capacity level is usually divided based on the hardware configuration, processing capacity and current operating status of the terminal, such as low load, medium load and high load. The output parameter threshold refers to the key indicators of water quality monitoring, such as the allowable range of parameters such as turbidity, pH value, dissolved oxygen, etc. By monitoring these parameters in real time, it can be determined whether the terminal is operating stably.

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

[0067] For example, suppose there are three water purification terminals A, B, and C in a certain area. The energy reserve of terminal A is 80%, and the energy consumption rate is 2% per hour; the energy reserve of terminal B is 60%, and the energy consumption rate is 3% per hour; the energy reserve of terminal C is 40%, and the energy consumption rate is 4% per hour. Based on these data, the ratio α of the energy consumption rate of each terminal to the remaining energy reserve can be calculated. The α of terminal A is 0.025, the α of terminal B is 0.05, and the α of terminal C is 0.1. Obviously, the energy status of terminal A is the best and the energy status of terminal C is the worst. Therefore, when assigning tasks, high-priority tasks are assigned to terminal A, medium-priority tasks are assigned to terminal B, and low-priority tasks are assigned to terminal C to optimize the overall energy utilization efficiency. The generation of resource allocation instructions is based on the task allocation plan and the real-time status of the terminal.

[0068] For example, when terminal A completes the current task, its load capacity level may drop from high load to medium load. At this time, resources can be adjusted dynamically 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 operation 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 a distributed water purification system, the judgment of energy status is the key to ensuring the stable operation of the system. First of all, it is necessary to clarify the meaning of the load capacity level and the output parameter threshold. The load capacity level refers to the processing capacity of the water purification terminal under the current working state, and the output parameter threshold is the minimum water quality output standard required for the normal operation of the system. These two parameters can be used to evaluate the energy consumption of the terminal. Calculating the ratio α of the current energy consumption rate to the remaining energy reserve is the core step in judging the energy status. The energy consumption rate refers to the amount of energy consumed by the terminal per unit time, and the remaining energy reserve is the total amount of energy currently remaining in the terminal.

[0071] For example, if the energy consumption rate of a water purification terminal is 2 kilowatts per hour and the remaining energy reserve is 10 kilowatts, then its ratio α is 0.2. This ratio reflects the intensity of terminal energy use. 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 the α value of a terminal reaches or exceeds 0.3, the terminal is judged to be an energy-deficient terminal. This judgment process needs to be carried out in real time to ensure that the system can detect potential energy problems in a timely manner. Generating a list of energy-deficient terminals is the basis for subsequent resource allocation. The system will summarize the information of all terminals judged to be energy-deficient into a list for centralized management and processing. This list contains not only the identification information of the terminal, but also its geographical location and communication delay data.

[0073] For example, a terminal with insufficient energy 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 the efficiency of resource allocation. Geographic location information can help the system determine the physical location of the terminal, and communication delay data is used to evaluate the communication efficiency between the terminal and neighboring terminals.

[0074] For example, the system can use geographic location information to find that there are multiple available resource terminals near a terminal with insufficient energy, and use communication delay data to select the terminal with the fastest response for resource allocation. The implementation of this series of steps can effectively avoid the operation interruption of the distributed water purification system due to insufficient energy. By real-time monitoring and judging the energy status, the system can discover and solve problems in advance to ensure the continuity and stability of the water treatment process. At the same time, the generation of the list of insufficient energy terminals and the extraction of geographic location 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, 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 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.

[0076] A preliminary optimization scheme for water purification tasks is generated by 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 volume of each water purification terminal.

[0078] In practical 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, if there are three water purification terminals, and their current loads are 60%, 70%, and 50%, respectively, the load balancing model will migrate some tasks from the terminal with higher load to the terminal with lower load, so that the loads of the three terminals are close to 60%, thereby avoiding overloading of a terminal and affecting the overall system performance. In this way, the system can efficiently utilize the processing power of all terminals, while avoiding failure or performance degradation of a single terminal due to excessive load.

[0080] The preliminary water purification task optimization scheme is adjusted by an energy consumption optimization function, wherein the energy consumption optimization function aims at minimizing the 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 a 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 the task to the terminal with lower energy consumption. At the same time, the optimization function will also consider the communication energy consumption generated during task migration to ensure that the communication delay of the migration path does not exceed 50ms. Through this adjustment, the system can minimize overall energy consumption while ensuring task execution efficiency, thereby achieving the goal of green energy saving.

[0084] Generate 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; 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 Dijkstra algorithm or 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 allocating 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, the task is migrated from terminal A with insufficient energy to terminal B with sufficient energy, and the energy allocation ratio of terminal B is specified to be 70%, while ensuring that the communication delay of the migration path does not exceed 50ms. In this way, the system can optimize energy utilization and resource allocation while ensuring the efficiency of task execution, thereby maximizing overall performance.

[0088] Step S104, sending the task allocation plan and resource allocation instructions to the water purification terminal, receiving the adjusted status data; updating the energy efficiency level and water quality compliance index according to the adjusted status data, verifying the network stability and generating 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, when the output power of a terminal's photovoltaic panel is 500 watts and the remaining capacity of the energy storage battery is 80%, high-energy consumption tasks such as large-flow water purification can be assigned first. For terminals with a photovoltaic panel output of only 200 watts and a remaining capacity of the energy storage battery of 30%, low-energy consumption tasks such as small-flow water purification are assigned. This dynamic task allocation method can effectively utilize solar energy resources and avoid energy waste. When receiving the adjusted status data, the system will monitor the water purification flow and water quality sensor detection parameters of each terminal in real time.

[0091] For example, the adjusted water flow rate of a terminal 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 level 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 a basis for subsequent optimization can be provided. When updating the energy efficiency level, the system will recalculate the energy utilization efficiency of each terminal based on the changes in the output power of the photovoltaic panel and the remaining capacity of the energy storage battery.

[0092] For example, after adjustment, the output power of the photovoltaic panel of a terminal 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 can reflect the energy utilization of the terminal under different conditions and provide a reference for subsequent resource allocation. When updating the water quality compliance index, the system will re-evaluate the water quality treatment effect of each terminal according to the changes in the detection parameters of the water quality sensor.

[0093] For example, after adjustment, the turbidity of a terminal is 0.3 NTU, the pH value is 7.0, and the water quality compliance index is increased from 80 points to 90 points. This real-time updated water quality compliance index can reflect the water quality treatment capacity of the terminal under different conditions and provide a basis for subsequent optimization.

[0094] When verifying network stability, the system monitors the data transmission delay and packet loss rate of each terminal. For example, the data transmission delay of a terminal is 50 milliseconds and the packet loss rate is 0.1%, which indicates that the network connection is stable. By verifying the network stability, it can ensure that the status data of each terminal can be transmitted to the central control system in a timely and accurate manner, providing reliable data support for subsequent optimization. When generating an optimization plan, the system will comprehensively analyze the energy efficiency level, water quality compliance index and network stability of each terminal, and propose targeted improvement measures.

[0095] For example, for terminals with low energy efficiency levels, we can recommend increasing the area of ​​photovoltaic panels or replacing high-efficiency energy storage batteries; for terminals with low water quality compliance indexes, 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 the overall system operating efficiency and water quality treatment effects.

[0096] Specifically, the sending of the task allocation plan and resource allocation instruction to the water purification terminal includes: performing supply and demand matching analysis on 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 available resources of the neighboring terminals, 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 of energy allocation ratio in 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 through 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 supply-demand matching analysis on the remaining capacity of energy-deficient terminals and the available resources of neighboring terminals, it is first necessary to clarify the specific values ​​of the remaining capacity of energy-deficient terminals and the available resources of neighboring terminals.

[0103] For example, if the remaining capacity of an energy-deficient terminal is 500Wh, and the available resources of its neighboring terminals are 2000Wh, a matching analysis is needed to determine whether the energy demand can be met. The key to the matching analysis is to determine whether the geographical distance and communication delay of the neighboring terminals meet the preset conditions. The preset conditions are usually set as geographical distance ≤ 5km or communication delay ≤ 30ms.

[0104] For example, if the geographical distance between adjacent terminals is 4km and the communication delay is 25ms, the preset conditions are met and supply and demand matching analysis can be performed. The purpose of matching analysis is to ensure the feasibility and efficiency of resource allocation and avoid allocation failure or resource waste due to long distance or high communication delay. The improved Hungarian algorithm is used to generate resource allocation instructions containing energy allocation ratios and task migration paths. It is necessary to understand the basic principles and application scenarios of the improved Hungarian algorithm. The improved Hungarian algorithm is an optimization matching algorithm that can find the optimal solution in many-to-many resource matching problems.

[0105] For example, in the energy allocation scenario, assuming there are 3 energy-deficient terminals and 5 neighboring terminals, the improved Hungarian algorithm can calculate which neighboring terminals each energy-deficient terminal should obtain energy from, as well as the specific allocation ratio and task migration path. The allocation ratio refers to the proportion of energy obtained by the energy-deficient terminal from the neighboring terminals to the total demand. For example, if a terminal needs 1000Wh of energy, the allocation ratio may be 40% (400Wh) from neighboring terminal A and 60% (600Wh) from neighboring terminal B. The task migration path refers to the specific path of energy allocation, 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 can comprehensively consider energy adequacy and water quality treatment capacity, ensuring that the allocated energy not only meets the demand, but also matches the water quality treatment capacity of the terminal, thereby improving resource utilization efficiency. The function and role of the information interconnection platform need to be clarified when sending resource allocation instructions to the corresponding water purification terminal through the information interconnection platform. The information interconnection platform is a resource management platform based on network communication technology, which can monitor and dispatch the operating status of the water purification terminal in real time.

[0106] For example, after the supply and demand matching analysis of energy-deficient terminals and neighboring terminals is completed, the information interconnection platform will send the generated resource allocation instructions to the relevant terminals. The instructions sent usually include energy allocation ratio, task migration path, and specific execution time.

[0107] For example, the instruction sent to the neighboring terminal A may include "provide 400Wh of energy to terminal B, the migration path is A→C→D, and the execution time is 14:00". The real-time and high efficiency of the information interconnection platform can ensure the accurate execution of resource allocation instructions. At the same time, by receiving status data fed back by the terminal, the platform can dynamically adjust the allocation strategy to achieve the optimal allocation of resources. The above steps and technical topics together constitute the complete process of generating and issuing resource allocation instructions. Through the synergy of supply and demand matching analysis, improved Hungarian algorithm and information interconnection platform, it can effectively solve the resource allocation problem of energy-deficient terminals and improve the operating efficiency and stability of distributed water purification systems.

[0108] Specifically, updating the energy efficiency level and water quality compliance index according to 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 according to 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 the adjustment.

[0109] Receiving the adjustment status data of each water purification terminal after executing the resource allocation instruction is the first step to update the energy efficiency level and water quality compliance index. The adjustment status data includes key parameters such as the water treatment volume, energy consumption value, and water quality test results of the water purification terminal.

[0110] For example, after a terminal executes the resource allocation instruction, the water treatment volume increases from 1000 liters per hour to 1200 liters, the energy consumption decreases from 1.2 kWh per hour to 1.0 kWh, and the water quality test results show that the turbidity decreases from 1.0 NTU to 0.8 NTU. These data provide the basis for subsequent calculations. Recalculating the energy conversion efficiency level and water quality compliance index based on the adjustment status data is the core step. The energy conversion efficiency level is measured by the water treatment volume per unit energy consumption. For example, before the adjustment, a terminal can process 833 liters of water per kWh, which increases to 1200 liters after the adjustment, and the efficiency level increases from C to B. The water quality compliance index is calculated based on multiple indicators such as turbidity, pH value, dissolved oxygen, etc. For example, the water quality compliance index of a terminal was 85 before the adjustment, and it increased to 90 after the adjustment, indicating that the water quality has improved significantly. By comparing the energy efficiency level and water quality compliance index before and after the adjustment, generating performance change data for each water purification terminal is the key to evaluating the effect of resource allocation.

[0111] For example, the energy efficiency level of a terminal is improved from C to B, and the water quality compliance index is improved from 85 to 90, indicating that the resource allocation instruction has effectively improved the terminal performance. These performance change data can be used to optimize subsequent resource allocation strategies, such as allocating more resources to terminals with lower energy efficiency, or strengthening water quality monitoring and regulation at terminals with lower water quality compliance indexes. The purpose of updating the energy efficiency level and water quality compliance index is to achieve closed-loop control of the system. By continuously receiving adjustment status data and recalculating key indicators, the system can dynamically adjust the resource allocation strategy to ensure that each terminal is always in the optimal operating state.

[0112] For example, when the energy efficiency level or water quality compliance index of a terminal decreases, 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; determining 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.

[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 the percentage value obtained by comparing the difference between the initial average delay and the adjusted average delay and dividing it by the initial average delay.

[0115] For example, if the initial average delay is 100 milliseconds and the adjusted average delay is 80 milliseconds, the data transmission delay reduction rate is (100-80) / 100×100%=20%. The terminal response time optimization rate is obtained 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 a 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 by the linear regression model trained based on past historical operation data. The linear regression model is a mathematical model established by analyzing the relationship between variables such as task allocation, resource allocation, data transmission delay, and terminal response time in historical data.

[0117] For example, it is found through historical data that when the data transmission delay reduction rate reaches 15% and the terminal response time optimization rate reaches 20%, the network stability reaches the best state. Therefore, these two values ​​can be used as stability thresholds. It is determined whether the data transmission delay reduction rate and the terminal response time optimization rate reach the stability threshold.

[0118] For example, if the data transmission delay reduction rate is 10% and the terminal response time optimization rate is 18%, the stability thresholds of 15% and 20% are not reached. If the stability threshold is not reached, 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 the 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 according to these two parameters, such as increasing the task volume of some terminals or reducing the task volume of some terminals, to further optimize network stability. Generate 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 the overall network stability.

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

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

[0123] For example, if the water purification efficiency of a terminal is lower than the preset threshold, it may be due to dust accumulation on the solar panel, which reduces the energy conversion efficiency, or the filter element is clogged, which slows down the water purification speed. At this time, the system will adjust the task allocation plan of the terminal in a targeted manner, such as reducing its water purification task volume or increasing maintenance reminders. Through multiple iterations, the system gradually optimizes the task allocation of each terminal to ensure that the energy utilization efficiency and water quality stability of the overall network meet the preset goals. In the process of meeting the preset termination conditions, the system dynamically evaluates the effect of each iteration.

[0124] For example, by comparing the water quality parameters and energy consumption data before and after the adjustment, we can determine whether the optimization plan is effective. If after a certain adjustment, the water quality compliance rate increases from 95% to 97%, but energy consumption increases by 10%, the system will further weigh whether further adjustments are needed. Termination conditions may include the water quality compliance rate being stable above 98%, energy consumption fluctuations being less than 5%, etc. Only when all conditions are met will the system generate the final task allocation plan and send it to each terminal. When generating the final task allocation plan, the system will comprehensively consider the load capacity, geographical location and water quality requirements of each terminal.

[0125] For example, if a terminal in a certain area is located in a remote area and has limited solar energy resources, the system will assign it fewer water purification tasks and increase the tasks of neighboring terminals to ensure stable water supply in the entire area. Through this dynamic allocation, the system can maximize energy efficiency and reduce the risk of failure of a single terminal while ensuring that water quality meets standards. When issuing task allocation plans, the system will use an efficient data transmission protocol to ensure that each terminal can receive and execute the new plan in a timely manner.

[0126] For example, through the Internet of Things technology, the system sends the optimization plan to each terminal in the form of an encrypted data packet, and the terminal makes adjustments immediately after receiving the data. This real-time distribution mechanism can quickly respond to environmental changes, such as sudden weather changes or water quality fluctuations, to ensure that the water purification system is always in the best operating state.

[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 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 plan based on the final adjustment result and sending it to each water purification terminal.

[0128] In the process of iteratively adjusting the optimization plan, we first need to dynamically adjust the task allocation plan and resource allocation instructions according to 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. The change in its value reflects the effect of task allocation and resource allocation.

[0129] For example, in one iteration, the network stability improvement value increased from 5% to 8%, which indicates 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, such as by adjusting the data transmission priority of each terminal or reallocating computing resources to further improve network stability. When the change in the network stability improvement value is less than the preset threshold (such as 2%) and iterates for multiple consecutive times (such as 3 times), the system will terminate the adjustment. This is because the change in the improvement value is too small, indicating that the optimization scheme is close to the optimal state, and continued adjustments are unlikely to bring significant improvements in results.

[0130] For example, during a certain iteration, the network stability improvement value changes by 1.5%, 1.8%, and 1.7%, respectively, and is less than 2% for three consecutive times. At this time, the system determines that the optimization has reached a stable state and stops iterative adjustment. This mechanism can avoid ineffective resource consumption and improve system efficiency. If the total number of iterations reaches the preset upper limit, the system will also terminate the adjustment. This is to prevent the optimization process from falling into an infinite loop or failing to converge for a long time.

[0131] For example, the preset iteration limit is 10 times. If the termination condition of the stability improvement value is still not met after the 10th iteration, the system will forcibly terminate the adjustment and generate the final task allocation plan based on the current result. This mechanism ensures the timeliness of the optimization process and avoids the normal operation of the system being affected by over-optimization. Finally, the system will generate the final task allocation plan based on the adjustment results and send it to each water purification terminal.

[0132] For example, after multiple iterations, the system determined an optimal task allocation scheme, which prioritizes high-priority tasks to terminals with better network performance, and dynamically adjusts the task volume according to the resource load of each terminal. This scheme can effectively improve the stability of the overall network and the efficiency of task execution, ensuring the efficient operation of the water purification system. Through the above iterative adjustment process, the system can gradually optimize the task allocation and resource allocation scheme, and ultimately achieve a dual improvement in network stability and task execution efficiency.

[0133] The embodiment of the present invention also provides an information interconnection system of a solar water purification terminal, which specifically includes:

[0134] A data acquisition module, configured to obtain status data of a plurality of water purification terminals, wherein the status data at least includes 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 according to the status data, and 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 of each water purification terminal;

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

[0138] A communication control module, configured to send 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. An information interconnection method for solar water purification terminals, characterized in that: The method comprises the following steps: obtaining status data of a plurality of water purification terminals, wherein the status data at least comprises energy parameters and water quality parameters; According to the status data, the energy efficiency and water quality compliance of each water purification terminal are evaluated to obtain the energy efficiency level and water quality compliance index; characteristic values ​​are extracted from the energy efficiency level and water quality compliance index to generate the load capacity level and output parameter threshold of each water purification terminal; according to the load capacity level and output parameter threshold, the energy status of each water purification terminal is judged, and a task allocation plan and resource allocation instructions are generated; the task allocation plan and resource allocation instructions are issued to the water purification terminal, and the adjusted status data is received; according to the adjusted status data, the energy efficiency level and water quality compliance index are updated, the network stability is verified, and an optimization plan is generated; The optimization scheme is iteratively adjusted until the preset termination condition is met, and a final task allocation scheme is generated and sent to each water purification terminal.

2. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: The method of evaluating the energy efficiency and water quality compliance of each water purification terminal based on the status data includes: 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 an energy conversion efficiency level for 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.

3. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: 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 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 classifying the energy efficiency characteristic values ​​and the water quality dynamic characteristic values ​​using a real-time clustering analysis algorithm based on K-means to generate the current load capacity level and water quality output parameter threshold of each water purification terminal.

4. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: 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.

5. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: 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 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.

6. 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 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 via an information interconnection platform.

7. 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: receiving the adjusted status data after each water purification terminal executes the resource allocation instruction; recalculating the energy conversion efficiency level and the water quality compliance index according to the adjusted status data; and generating performance change data of each water purification terminal by comparing the energy efficiency level and the water quality compliance index before and after the adjustment.

8. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: 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; 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 the water quality fluctuation coefficient.

9. The information interconnection method of solar water purification terminal according to claim 1, characterized in that: 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.

10. 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 at least includes energy parameters and water quality parameters; An evaluation module, configured to evaluate the energy efficiency and water quality compliance of each water purification terminal according to the status data, and 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 an output parameter threshold of each water purification terminal; A decision control module is configured to determine the energy status of each water purification terminal according to the load capacity level and the output parameter threshold, and generate a task allocation plan and resource allocation instructions; A communication control module, configured to send 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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