Intelligent charging and discharging management and fault maintenance system of solar power supply meteorological equipment

Abstract

The invention relates to an intelligent charging and discharging management and fault maintenance system of solar power supply meteorological equipment. The system comprises a load charging priority module which is used for setting a load charging priority according to the operation requirement of meteorological equipment; the demand judgment module is used for performing comparison judgment according to the solar power generation power and the load demand total power to obtain a judgment result; the power distribution module is used for obtaining a power control strategy based on the solar power generation power, the load charging priority and a battery electric quantity input power distribution model when the judgment result is that the solar power generation power does not meet the load requirement; and the battery charging module is used for charging the battery according to the working temperature and the charging degree of the battery based on temperature compensation of fuzzy control when the judgment result is that the solar power generation power meets the load requirement. According to the system, efficient charging management and fault prevention are realized through load priority and demand judgment, stable operation of equipment is effectively ensured, and the utilization rate of electric power resources is improved.

Description

Technical Field

[0001] The present invention belongs to the fields of computer and communication technologies, and particularly relates to an intelligent charge and discharge management and fault maintenance system for solar-powered meteorological equipment. Background Art

[0002] In the field of meteorological monitoring, with the continuous promotion of the concepts of sustainable development and energy conservation and emission reduction, the management and application of solar-powered meteorological equipment have gradually developed towards the direction of intelligence and high efficiency. As an important tool for monitoring the natural environment, such equipment plays a crucial role in environmental monitoring and data collection.

[0003] However, the traditional management mode relies on simple charge and discharge control means and lacks intelligent decision-making and real-time monitoring capabilities. It often fails to meet the requirements of modern meteorological monitoring for high reliability and high efficiency of power supply, and may even cause the equipment to be unable to obtain the required power support at critical moments, thus affecting the accuracy and timeliness of meteorological data. Summary of the Invention

[0004] Based on this, in view of the above technical problems, the object of the present invention is to provide an efficient and intelligent charge and discharge management and fault maintenance system for solar-powered meteorological equipment, aiming to monitor and schedule the power usage of meteorological equipment in real time, optimize the allocation and utilization efficiency of power resources, thereby ensuring the stability of the solar power supply system, extending the service life of the equipment, and improving the energy utilization efficiency.

[0005] In a first aspect, the present application provides an intelligent charge and discharge management and fault maintenance system for solar-powered meteorological equipment, the system comprising:

[0006] A load charging priority module, configured to set the load charging priority according to the operation requirements of the meteorological equipment;

[0007] A demand judgment module, configured to compare and judge the solar power generation power and the total power demand of the load to obtain a judgment result;

[0008] A power distribution module, configured to, when the judgment result is that the solar power generation power does not meet the load demand, input the solar power generation power, the load charging priority, and the battery power into a power distribution model to obtain a power control strategy, and the power control strategy is used to distribute the charging power of the load;

[0009] A battery charging module, configured to, when the judgment result is that the solar power generation power meets the load demand, charge the battery based on temperature compensation of fuzzy control according to the battery operating temperature and the charging degree.

[0010] In one embodiment, the battery charging module comprises:

[0011] A data processing sub-unit, configured to obtain the battery operating temperature, the battery charging current, and the battery voltage, and calculate the charging level according to the battery charging current and the battery voltage;

[0012] A data partitioning sub-unit, configured to determine and partition the battery operating temperature range to obtain a temperature fuzzy subset; and partition according to the charging level to obtain a charging level fuzzy subset;

[0013] A rule design sub-unit, configured to formulate fuzzy rules based on the physical characteristics and charging requirements of the battery according to the fuzzy control theory, in combination with the temperature fuzzy subset and the charging level fuzzy subset;

[0014] A fuzzy inference sub-unit, configured to perform inference according to the fuzzy rules to obtain a fuzzy result of the charging voltage adjustment;

[0015] It is configured to perform defuzzification on the fuzzy result of the charging voltage adjustment by the centroid method to obtain the charging voltage adjustment amount;

[0016] An adjustment charging sub-unit, configured to charge the battery according to the charging voltage adjustment amount.

[0017] In one embodiment, the system further includes a power prediction module, including:

[0018] A meteorological prediction sub-unit, configured to input meteorological real-time data into a meteorological prediction model to obtain predicted meteorological data;

[0019] A power generation prediction sub-unit, configured to input the predicted meteorological data into a solar power generation prediction model to obtain the solar power generation;

[0020] A load demand prediction sub-unit, configured to calculate based on the correlation between the historical operation data of the meteorological equipment load and the meteorological historical data, and obtain the predicted demand power of each load according to the predicted meteorological data;

[0021] It is configured to calculate based on the load priorities according to the predicted demand power of each load to obtain the total load demand power.

[0022] In one embodiment, the calculation formula for the total load demand power is:

[0023]

[0024] Wherein, 0 ≤ w i ≤ 1, and P total represents the total load demand power, n represents the total number of loads in the meteorological equipment, P i represents the predicted demand power of load i, and w i represents the priority coefficient of the load.

[0025] In one embodiment, the system further includes a fault detection module for:

[0026] When the operating state of the meteorological device is abnormal, obtain the real-time operating data of the meteorological device; the abnormal operating state of the meteorological device includes abnormal power generation power of the solar panel and abnormal battery operating temperature;

[0027] Input the real-time operating data into the trained fault detection model to obtain fault information, and send the fault information to the maintenance personnel. The fault information includes the fault type and the fault location.

[0028] In one embodiment, the system further includes a battery discharge protection module for:

[0029] Set key discharge threshold parameters according to the type and characteristics of the battery, including the discharge cut-off voltage and the lower limit of the remaining battery capacity;

[0030] Obtain the working state of the battery, mainly including the battery discharge voltage, the remaining battery capacity, and the operating conditions of each load;

[0031] When the battery is in the discharge state, compare the battery discharge voltage with the discharge cut-off voltage, and compare the remaining battery capacity with the lower limit of the remaining battery capacity to obtain a comparison result;

[0032] When the comparison result meets the preset conditions, charge the load according to the load priority and the remaining battery capacity.

[0033] In one embodiment, when the comparison result meets certain conditions, charging the load according to the load priority and the remaining battery capacity includes:

[0034] Use the following formula to calculate the actual discharge power of the load:

[0035]

[0036] where Q remain represents the remaining battery capacity, n represents the total number of loads of the meteorological device, C represents the rated capacity of the battery, P actual,i represents the actual discharge power of the load, P rated,i represents the rated power of the load, w i represents the discharge priority of the load;

[0037] Charge the load with the actual discharge power.

[0038] In a second aspect, the present application also provides an intelligent charge and discharge management and fault maintenance method for a solar-powered meteorological device. The method includes:

[0039] Set the load charging priority according to the operating requirements of the meteorological device;

[0040] Based on the comparison and judgment of the solar power generation power and the total power demand of the load, a judgment result is obtained;

[0041] When the judgment result is that the solar power generation power does not meet the load demand, based on the solar power generation power, the load charging priority, and the battery power input power distribution model, a power control strategy is obtained, and the power control strategy is used to allocate the charging power of the load;

[0042] When the judgment result is that the solar power generation power meets the load demand, the battery is charged based on the temperature compensation of fuzzy control according to the battery operating temperature and the charging level.

[0043] In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the intelligent charge and discharge management and fault maintenance system according to any one of the first aspects are implemented.

[0044] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent charge and discharge management and fault maintenance system according to any one of the first aspects are implemented.

[0045] The above-mentioned intelligent charge and discharge management and fault maintenance system, method, computer device, and storage medium of a solar-powered meteorological device set the load charging priority through the load charging priority module according to the operation requirements of the meteorological device, which helps to ensure that critical loads can obtain charging support first when power is in short supply. Secondly, the demand judgment module can perform demand judgment based on the solar power generation power and the total power demand of the load to obtain a judgment result. When the judgment result indicates that the solar power generation power cannot meet the load demand, the system will calculate an appropriate power control strategy through the power distribution module based on the solar power generation power, the load charging priority, and the battery power input power distribution model, and accurately allocate the charging power of the load according to this strategy, effectively ensuring that the load and the battery can obtain reasonable power supply.

[0046] When the judgment result is that the solar power generation power can meet the load demand, the battery charging module optimizes the battery charging process based on the temperature compensation technology of fuzzy control according to the battery operating temperature and the charging level, ensuring the maximization of the battery charging efficiency and the battery life.

[0047] Compared with traditional power management systems, this system can dynamically adjust the power distribution strategy according to the real-time power demand of meteorological equipment and solar power generation capacity through intelligent charge and discharge management and load priority, achieving more efficient power utilization. At the same time, through the collaborative work of multiple modules, it ensures the reasonable scheduling of battery charging and load power supply, not only improving the operation efficiency of the system, but also reducing equipment failures caused by insufficient power or improper charging strategies, greatly enhancing the stability and reliability of meteorological equipment. Brief Description of the Drawings

[0048] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0049] Figure 1 Schematic structural diagram of the intelligent charge and discharge management and fault maintenance system for solar-powered meteorological equipment provided by an exemplary embodiment of the present invention;

[0050] Figure 2 Flowchart of the intelligent charge and discharge management and fault maintenance method for solar-powered meteorological equipment provided by an exemplary embodiment of the present invention. Detailed Embodiments

[0051] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] In one embodiment, as Figure 1 shown, an intelligent charge and discharge management and fault maintenance system for solar-powered meteorological equipment is provided. In this embodiment, taking the application of this system to a terminal as an example, it can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. In this embodiment, the method includes:

[0053] A load charging priority module 101, configured to set a load charging priority according to the operation requirements of meteorological equipment.

[0054] Specifically, during the operation of meteorological equipment, according to different work tasks and power demands, it is necessary to set priorities for certain equipment or loads to ensure their normal charging operation. The setting of load priorities can be dynamically adjusted by evaluating the work importance, functions, and operation time of the equipment. For example, loads such as key meteorological data acquisition sensors and core data processing units are given high priorities because they have a profound impact on the coherence and accuracy of meteorological observations; while relatively less important loads such as equipment status indicators and non-emergency data backup modules are set as low priorities. This module initializes the priority configuration at system startup and dynamically adjusts it according to the equipment operation mode. For example, in the extreme weather emergency monitoring mode, the priority of the data real-time transmission module is increased to ensure the rapid backhaul of key meteorological data, providing a solid guarantee for meteorological forecasting and disaster warning. Setting reasonable load charging priorities effectively ensures that key equipment can still be preferentially powered when power is insufficient, avoiding downtime or data loss of important equipment.

[0055] The demand judgment module 102 is used to make a comparison and judgment based on the solar power generation and the total power demand of the loads to obtain a judgment result.

[0056] Illustratively, the demand judgment module 102 can integrate high-precision power sensors to monitor the output power of the solar panels in real time, that is, the solar power generation. At the same time, based on the rated power and operation status of each load, it can accurately calculate the total power demand of the loads. If the solar power generation is greater than or equal to the total power demand of the loads, it is determined that the load demand is met, and the system can enter the battery charging module 104; otherwise, it is not met, and the system can enter the power distribution module 103. Through this demand judgment process, it effectively ensures the reasonable allocation of power resources under limited solar power generation conditions.

[0057] The power distribution module 103 is used to obtain a power control strategy based on the solar power generation, load charging priority, and battery power input power distribution model when the judgment result is that the solar power generation does not meet the load demand. The power control strategy is used to allocate the charging power of the loads.

[0058] Through the judgment of the demand judgment module 102, when the solar power generation is insufficient, the power distribution module 103 can use battery energy to charge the loads together, and calculate a reasonable power control strategy according to the solar power generation, load charging priority, and battery power input power distribution model to reasonably allocate the charging power of the loads. For example, at a certain moment, the power generation can only support some loads. The high-priority meteorological data acquisition sensors obtain sufficient power to continue working, and the low-priority auxiliary equipment reduces power appropriately or pauses to maintain the core functions of the equipment, extend the power supply duration of key loads, and prevent over-discharge of the battery, so as to achieve the optimal utilization of power resources and the stable operation of the equipment.

[0059] The battery charging module 104 is used to charge the battery based on temperature compensation of fuzzy control according to the battery operating temperature and charging level when the judgment result is that the solar power generation meets the load demand.

[0060] When the demand judgment module 102 determines that the solar power generation can meet the load demand, the battery charging module 104 starts and performs the charging operation. During the charging process, the battery status, such as the operating temperature, voltage, and current, can be monitored in real time through a high-precision temperature sensor and an advanced battery charge monitoring chip, and fuzzy control adjustment can be performed according to the battery operating temperature and charging level. When the battery temperature is too high or too low, or the charging level is close to saturation or not full, the charging voltage and current can be adjusted according to preset rules through fuzzy control. For example, when the temperature is high, the charging voltage is moderately reduced and the charging rate is slowed down to avoid battery thermal damage and performance degradation; when the temperature is low, the charging parameters are finely adjusted to optimize the charging efficiency and extend the battery life, ensuring that the battery is always charged under suitable working conditions, improving the charging safety, efficiency, and overall battery service life, and providing guarantee for the continuous power supply of meteorological equipment.

[0061] The system is divided into four main modules. First, the load charging priority module sets the charging priority of the load according to the operation requirements of the meteorological equipment to ensure that important loads can obtain charging support first when the power is insufficient. Secondly, the demand judgment module can make a demand judgment by comparing the solar power generation with the total power demand of the load, and obtain a judgment result, which can effectively avoid the occurrence of power shortage. When the judgment result shows that the solar power generation cannot meet the load demand, the power distribution module will calculate a power control strategy based on the solar power generation, load charging priority, and battery power input to the power distribution model, and optimize the distribution of the charging power of the load, so as to ensure the stable operation of key equipment.

[0062] Finally, when the judgment result shows that the solar power generation meets the load demand, the battery charging module can, based on the temperature compensation technology of fuzzy control, combine the battery operating temperature and charging level to intelligently adjust the battery charging strategy, optimize the charging process, extend the battery life, and improve the charging efficiency.

[0063] Through the collaborative work of these four modules, the system can achieve intelligent charge and discharge management and fault maintenance of solar-powered meteorological equipment, ensure the stable operation of the equipment under different power conditions, further improve the intelligent level of battery management, and effectively guarantee the continuous and reliable operation of meteorological equipment.

[0064] In one embodiment, the battery charging module 104 includes:

[0065] A data processing subunit, configured to obtain the battery operating temperature, the battery charging current, and the battery voltage, and calculate the charging level according to the battery charging current and the battery voltage;

[0066] A data partitioning subunit, configured to determine and partition the battery operating temperature range to obtain a temperature fuzzy subset; and partition according to the charging level to obtain a charging level fuzzy subset;

[0067] A rule design subunit, configured to formulate fuzzy rules based on the physical characteristics and charging requirements of the battery according to the fuzzy control theory, in combination with the temperature fuzzy subset and the charging level fuzzy subset;

[0068] A fuzzy inference subunit, configured to perform inference according to the fuzzy rules to obtain a fuzzy result of the charging voltage adjustment;

[0069] is configured to perform defuzzification on the fuzzy result of the charging voltage adjustment by the centroid method to obtain a charging voltage adjustment amount;

[0070] An adjustment charging subunit, configured to charge the battery according to the charging voltage adjustment amount.

[0071] Specifically, in the battery charging module 104, the battery operating temperature, charging current, and voltage data can be collected in real time through high-precision sensors, and the charging level can be calculated by combining the coulomb counting method and the open-circuit voltage method according to the charging current and voltage data. Secondly, the data partitioning subunit is responsible for constructing a fuzzy description framework of the battery state. According to the battery chemical characteristics and engineering experience, the operating temperature range can be divided and defined. For example, for a lithium-ion battery, the fuzzy subsets of "low temperature is less than -10°C)", "room temperature is -10°C to 45°C", and "high temperature is greater than >45°C" can be divided, and each subset is set with a dedicated membership function to quantify the temperature membership degree. Similarly, according to the charging level, fuzzy subsets of undercharging, moderate charging, and overcharging can be set to refine the battery charging process. The membership function can be a Gaussian function or a triangular function, etc. This process anchors the scale for the subsequent fuzzy rule formulation, helping the system to keenly perceive the subtle differences in the battery state.

[0072] In the rule design subunit, rules can be set for the temperature and charging level fuzzy subsets through the battery electrochemistry principle, thermal management requirements, and long-term practical experience, and then the charging voltage adjustment strategy can be accurately matched. For example, rules such as "greatly increase the voltage when it is low temperature and undercharged, and sharply decrease the voltage when it is high temperature and overcharged" can be designed. This process can transform the physical characteristics and charging requirements of the battery into intelligent control instructions, enabling the system to flexibly adjust the charging parameters according to fuzzy logic, achieve precise and adaptive charging, and thus improve the charging safety and efficiency, and avoid damage to the battery due to improper charging.

[0073] Subsequently, the fuzzy inference sub-unit can obtain the fuzzy result of charging voltage adjustment after activating the rules in the rule base based on the membership degrees of the data collected by the data processing sub-unit in the corresponding fuzzy subsets. Since the fuzzy result of charging voltage adjustment is not a definite value but a fuzzy set, the center of gravity of this result can be calculated by the center-of-gravity method to determine the final output value, that is, the charging voltage adjustment amount. Finally, the charging sub-unit can dynamically and precisely regulate the charging voltage and current according to the charging voltage adjustment amount through an intelligent charging controller such as a DC-DC converter to ensure that the charging process conforms to the battery state and the fuzzy control strategy throughout the charging process.

[0074] In one embodiment, the system further includes a power prediction module 105, including:

[0075] A meteorological prediction sub-unit, configured to input real-time meteorological data into a meteorological prediction model to obtain predicted meteorological data;

[0076] A power generation prediction sub-unit, configured to input the predicted meteorological data into a solar power generation prediction model to obtain the solar power generation;

[0077] A load demand prediction sub-unit, configured to calculate the predicted demand power of each load based on the correlation between the historical operation data of the meteorological equipment load and the historical meteorological data according to the predicted meteorological data;

[0078] It is used to calculate the total load demand power based on the load priorities according to the predicted demand power of each load.

[0079] Illustratively, the real-time meteorological data may include data such as temperature, humidity, air pressure, and wind speed. After preprocessing such as data cleaning and normalization, it is input into a meteorological prediction model that integrates physical principles and statistical learning, which can accurately simulate the spatio-temporal evolution of meteorological elements and output high-resolution predicted meteorological data for future periods. The solar power generation prediction model in the power generation prediction sub-unit can be constructed based on the analysis of a large amount of historical meteorological data and the measured data of the power generation of the corresponding solar power station by using a long short-term memory network. This model can obtain the solar power generation by comprehensively considering the interaction of factors such as the duration of sunlight, the change of radiation intensity, and the influence of temperature through the predicted meteorological data, ensuring the stable and efficient utilization of solar energy resources in the system and improving the energy self-sufficiency and power supply reliability.

[0080] Similarly, in the load demand prediction sub-unit, parameters such as load power consumption, working hours, start-stop frequency, etc. and corresponding meteorological condition records can be extracted from the replicated long-term operation database. After data preprocessing, principal component analysis is used to reduce the dimension and mine the core influencing factors, and a deep learning network is used to mine complex non-linear mappings to construct a demand prediction model. This model can simulate the change of the load operation state under future meteorological scenarios based on the predicted meteorological data, and then obtain the predicted demand power of each load. Finally, the load priority weights can be set according to factors such as the functional importance of each load, task urgency, and impact on the continuity of meteorological observations. By summing the products of the predicted demand power of each load and the corresponding priority weights, the total load demand power can be obtained. This process can reflect the total amount and structure of the load power demand under different working conditions, ensure the stable and continuous operation of the core functions of meteorological equipment in complex energy scenarios, and improve the overall reliability and operation efficiency of the system.

[0081] In one embodiment, the calculation formula for the total load demand power is:

[0082]

[0083] Where 0 ≤ w i ≤ 1, and P total represents the total load demand power, n represents the total number of loads in the meteorological equipment, P i represents the predicted demand power of load i, and w i represents the priority coefficient of the load.

[0084] In the above formula, for each load i, its predicted demand power P i is multiplied by its priority coefficient w i , and the power contribution of the load adjusted according to the priority can be obtained. Then, the sum of the loads from 1 to n is calculated, and further, the total load demand power considering the priorities of each load can be obtained. Through this process, the total load demand power can be accurately predicted in a dynamic environment, and then the power resource allocation can be optimized to make the best use of solar energy.

[0085] In one embodiment, the system further includes a fault detection module 106, which is used for:

[0086] When the operation state of the meteorological equipment is abnormal, obtain the real-time operation data of the meteorological equipment; the abnormal operation state of the meteorological equipment includes abnormal power generation power of the solar panel and abnormal battery working temperature;

[0087] Input the real-time operation data into the trained fault detection model to obtain fault information, and send the fault information to the maintenance personnel. The fault information includes the fault type and the fault location.

[0088] Specifically, the fault detection module 106 is used to monitor the operating status of meteorological equipment in real time to ensure that the equipment operates under normal working conditions. If abnormalities are found in the equipment, this module can detect and report faults in a timely manner. Whether the operating status of meteorological equipment is abnormal can be judged by monitoring whether the operating parameters deviate from the normal values. For example, if the power generation power of the solar panel is abnormal, such as too low or too high, it may mean that there are faults in the panel or problems such as dirt occlusion and damage. If the operating temperature of the battery is abnormal, that is, outside the normal range, it may affect the performance of the battery or even cause battery damage. To identify the cause of the abnormality, the real-time operating data obtained can be input into a trained fault detection model for analysis to obtain fault information. This information can include the type of fault, such as abnormal battery temperature, and the location of the fault, such as the location of the abnormal panel. After the fault information is generated, the system can automatically send it to the maintenance personnel through notifications, emails, text messages or other means for timely maintenance. Through event-driven automatic fault detection, the system can quickly analyze the type and location of faults and notify the maintenance personnel for timely repair or maintenance, which helps to improve the reliability of the equipment and reduce maintenance costs.

[0089] In one embodiment, the system further includes a battery discharge protection module 107, which is used for:

[0090] Set key discharge threshold parameters according to the type and characteristics of the battery, including the discharge cut-off voltage and the lower limit of the remaining battery power;

[0091] Obtain the working status of the battery, mainly including the battery discharge voltage, the remaining battery power, and the operating conditions of each load;

[0092] When the battery is in the discharge state, compare the battery discharge voltage with the discharge cut-off voltage, and compare the remaining battery power with the lower limit of the remaining battery power to obtain a comparison result;

[0093] When the comparison result meets the preset conditions, charge the load according to the load priority and the remaining battery power.

[0094] Specifically, the threshold can be determined based on the battery's chemical composition, structural design, and performance characteristics, and the lower limit of the remaining battery charge can be determined based on the battery capacity, charge-discharge rate, and application scenario. For example, the lower limit of the remaining charge of a lithium battery is 10%-20%. This process sets a limit for the safe and efficient discharge of the battery by setting a threshold to prevent irreversible damage, capacity attenuation, and lifespan shortening caused by over-discharging of the battery, ensuring stable battery performance and extending the battery lifespan. During the discharge process, the battery status can be obtained in real time through sensors, and then the battery discharge voltage can be compared with the discharge cut-off voltage, and the remaining battery charge can be compared with the lower limit of the remaining charge. If the comparison result meets the preset conditions, for example, the real-time battery status does not reach the preset threshold, it indicates that the battery status is good, and the normal power supply to the load can be maintained. Furthermore, according to the load priority and demand allocation, key loads can be guaranteed first, and the remaining power can be supplied to secondary loads to ensure the complete functionality and efficient operation of the device, and to achieve the optimal utilization of power resources.

[0095] In one embodiment, when the comparison result meets certain conditions, the load is charged according to the load priority and the remaining battery charge, including:

[0096] Use the following formula to calculate the actual discharge power of the load:

[0097]

[0098] Where Q remain represents the remaining battery charge, n represents the total number of meteorological equipment loads, C represents the rated capacity of the battery, P actual,i represents the actual discharge power of the load, P rated,i represents the rated power of the load, w i represents the discharge priority of the load;

[0099] Charge the load with the actual discharge power.

[0100] In the above formula, the discharge priority of the load is usually between 0 and 1, which is used to determine the priority degree of each load during power allocation. The higher the priority, the more power the load will obtain relatively. By multiplying the rated power P rated,i of the load by the power that the load may obtain can be adjusted according to the proportion of the remaining battery charge to the rated capacity, and then the power allocation can be adjusted by multiplying the discharge priority of the load. By summing up the products of the rated power of all loads and their respective priorities, it is ensured that the total power obtained by all loads does not exceed the power that the remaining battery charge can provide. Finally, charge the load with the calculated actual discharge power P actual,i When the battery power is insufficient, the power supply to high-priority loads can be guaranteed first, and the power of low-priority loads can be gradually reduced to extend the power supply duration of key loads, which helps to improve the reliability and adaptability of the system.

[0101] Based on the same inventive concept, as Figure 2 shown, an embodiment of the present application further provides an intelligent charge and discharge management and fault maintenance method for a solar-powered meteorological device, and the method includes:

[0102] S101: Set the load charging priority according to the operation requirements of the meteorological device;

[0103] S102: Compare and judge according to the solar power generation power and the total power demand of the load to obtain a judgment result;

[0104] S103: When the judgment result is that the solar power generation power does not meet the load demand, based on the solar power generation power, the load charging priority and the battery power input power distribution model, obtain a power control strategy, and the power control strategy is used to allocate the charging power of the load;

[0105] S104: When the judgment result is that the solar power generation power meets the load demand, charge the battery based on the temperature compensation of fuzzy control according to the battery working temperature and the charging degree.

[0106] In the above intelligent charge and discharge management and fault maintenance method for a solar-powered meteorological device, first of all, by setting the load charging priority according to the operation requirements of the meteorological device, it provides a strong guarantee for the stable operation of the device, especially when the power supply is insufficient, it can ensure the continuous operation of important meteorological monitoring devices.

[0107] When comparing and judging the solar power generation power and the total power demand of the load, determine whether the power demand of the device is met according to the judgment result. If the solar power generation power is not enough to meet the load demand, then based on the solar power generation power, the load charging priority and the battery power, input the power distribution model to obtain a power control strategy. This strategy can dynamically allocate the charging power according to the priorities of different loads, ensuring that the system can reasonably allocate resources under limited power, avoid excessive consumption of battery power, and improve the utilization efficiency of solar energy.

[0108] When the judgment result is that the solar power generation power meets the load demand, the system will optimize the battery charging process based on fuzzy control technology. Specifically, the system performs temperature compensation on the battery working temperature and the charging degree to ensure that the battery charging process is more stable and maximally extends the battery life. In addition, fuzzy control can flexibly adjust the charging strategy according to environmental changes, improving the stability and efficiency of battery charging.

[0109] Compared with traditional power management methods, this method can not only cope with complex power supply and demand changes through demand judgment and dynamic power allocation, but also ensure the reliability and stability of meteorological equipment in various working environments. By combining advanced fuzzy control and priority charging strategies, this method optimizes the use of solar energy resources, improves the charging efficiency, and provides effective protection for equipment fault prevention, ensuring that meteorological equipment can operate stably in harsh environments for a long time.

[0110] Further, when the judgment result is that the solar power generation power meets the load demand, the temperature compensation based on fuzzy control charges the battery according to the battery working temperature and the charging degree, including:

[0111] Obtain the battery working temperature, battery charging current, and battery voltage, and calculate the charging degree according to the battery charging current and battery voltage;

[0112] Determine and divide the battery working temperature range to obtain a temperature fuzzy subset; and divide according to the charging degree to obtain a charging degree fuzzy subset;

[0113] Based on fuzzy control theory, according to the physical characteristics and charging requirements of the battery, combine the temperature fuzzy subset and the charging degree fuzzy subset to formulate fuzzy rules;

[0114] Infer according to the fuzzy rules to obtain the fuzzy result of the charging voltage adjustment;

[0115] Defuzzify the fuzzy result of the charging voltage adjustment through the centroid method to obtain the charging voltage adjustment amount;

[0116] Charge the battery according to the charging voltage adjustment amount.

[0117] Further, this method also includes:

[0118] Input the meteorological real-time data into the meteorological prediction model to obtain the predicted meteorological data;

[0119] Input the predicted meteorological data into the solar power generation power prediction model to obtain the solar power generation power;

[0120] Based on the correlation between the historical operation data of the meteorological equipment load and the historical meteorological data, calculate according to the predicted meteorological data to obtain the predicted demand power of each load;

[0121] Based on the load priority, calculate according to the predicted demand power of each load to obtain the total load demand power.

[0122] Further, the calculation formula for the total load demand power is:

[0123]

[0124] Among them, 0 ≤ w i ≤ 1, and P total represents the total power demand of the load, n represents the total number of loads in the meteorological equipment, P i represents the predicted demand power of load i, and w i represents the priority coefficient of the load.

[0125] Furthermore, the method further includes:

[0126] When the operating state of the meteorological equipment is abnormal, obtain the real-time operating data of the meteorological equipment; the abnormal operating state of the meteorological equipment includes abnormal power generation power of the solar panel and abnormal battery operating temperature;

[0127] Input the real-time operating data into the trained fault detection model to obtain fault information, and send the fault information to the maintenance personnel. The fault information includes the fault type and the fault location.

[0128] Furthermore, the method further includes:

[0129] Set key discharge threshold parameters according to the type and characteristics of the battery, including the discharge cut-off voltage and the lower limit of the remaining battery power;

[0130] Obtain the working state of the battery, mainly including the battery discharge voltage, the remaining battery power, and the operating conditions of each load;

[0131] When the battery is in the discharge state, compare the battery discharge voltage with the discharge cut-off voltage, and compare the remaining battery power with the lower limit of the remaining battery power to obtain a comparison result;

[0132] When the comparison result meets the preset conditions, charge the load according to the load priority and the remaining battery power.

[0133] Furthermore, when the comparison result meets certain conditions, charging the load according to the load priority and the remaining battery power includes:

[0134] Use the following formula to calculate the actual discharge power of the load:

[0135]

[0136] Among them, Q remain represents the remaining battery power, n represents the total number of loads in the meteorological equipment, C represents the rated capacity of the battery, P actual,i represents the actual discharge power of the load, P rated,i represents the rated power of the load, and w i represents the discharge priority of the load;

[0137] Charge the load with the actual discharge power.

[0138] In an exemplary embodiment, the present invention further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the intelligent charge and discharge management and fault maintenance system of the solar-powered meteorological device of the present application. A multi-core processor is preferred to improve the parallel processing ability of the system. Memory: Provide sufficient temporary storage space to support the operation of the program and the processing of data. The memory capacity should be large enough to accommodate a large amount of supply information and computing tasks.

[0139] In an exemplary embodiment, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the intelligent charge and discharge management and fault maintenance system of the solar-powered meteorological device of the present application. The computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), solid-state drive (SSD, Solid State Drives), or optical disc, etc. Among them, the random access memory may include resistive random access memory (ReRAM, Resistance Random Access Memory) and dynamic random access memory (DRAM, Dynamic Random Access Memory).

[0140] The above-described embodiments merely represent several implementation manners of the embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. Intelligent charging and discharging management and fault maintenance system for solar powered meteorological equipment, characterized in that: The system comprises: Load charging priority module, used to set load charging priority according to the operation requirements of meteorological equipment; The demand judgment module is used to compare and judge the solar power generation power with the total power demanded by the load to obtain a judgment result; A power allocation module, for obtaining a power control strategy based on the solar power generation power, the load charging priority and the battery power input power allocation model when the judgment result is that the solar power generation power does not meet the load demand, and the power control strategy is used to allocate the charging power of the load; The battery charging module is used to charge the battery according to the battery operating temperature and charging degree based on fuzzy control temperature compensation when the judgment result is that the solar power generation power meets the load demand.

2. The intelligent charge-discharge management and fault maintenance system according to claim 1 is characterized in that: The battery charging module comprises: a data processing subunit, configured to obtain the battery operating temperature, the battery charging current and the battery voltage, and calculate the charging degree according to the battery charging current and the battery voltage; The data division subunit is used to determine and divide the battery operating temperature range to obtain a temperature fuzzy subset; and divide according to the charging degree to obtain a charging degree fuzzy subset; A rule design subunit, for formulating fuzzy rules based on fuzzy control theory according to the physical characteristics and charging requirements of the battery, in combination with the temperature fuzzy subset and the charging degree fuzzy subset; A fuzzy reasoning subunit, used for performing reasoning according to the fuzzy rules to obtain a fuzzy result of charging voltage adjustment; for performing a defuzzification operation on the fuzzy result of the charging voltage adjustment by a center of gravity method to obtain a charging voltage adjustment amount; The charging adjustment subunit is used to charge the battery according to the charging voltage adjustment amount.

3. The intelligent charge and discharge management and fault maintenance system according to claim 1 is characterized in that: The system also includes a power prediction module, including: A meteorological prediction subunit is used to input a meteorological prediction model according to real-time meteorological data to obtain predicted meteorological data; A power generation prediction subunit, used for inputting the predicted meteorological data into a solar power generation prediction model to obtain the solar power generation power; A load demand prediction subunit, for calculating the predicted power demand of each load based on the correlation between the historical operation data of the meteorological equipment load and the historical meteorological data and the predicted meteorological data; It is used to calculate the predicted required power of each load based on the load priority to obtain the total required power of the loads.

4. The intelligent charge-discharge management and fault maintenance system according to claim 3 is characterized in that: The calculation formula for the total power required by the load is: Where 0≤w i ≤1, and P total represents the total power demanded by the load, n represents the total number of loads in the meteorological equipment, P i represents the predicted required power of load i, w i Indicates the load priority factor.

5. The intelligent charge-discharge management and fault maintenance system according to claim 1, characterized in that: The system also includes a fault detection module, which is used to: When the meteorological equipment is in an abnormal state, real-time operation data of the meteorological equipment is obtained; the abnormal state of the meteorological equipment includes abnormal power generation of the solar panel and abnormal operating temperature of the battery; The real-time operation data is input into a trained fault detection model to obtain fault information, and the fault information is sent to maintenance personnel. The fault information includes the fault type and the fault location.

6. The intelligent charge-discharge management and fault maintenance system according to claim 1, characterized in that: The system also includes a battery discharge protection module, which is used to: According to the type and characteristics of the battery, set key discharge threshold parameters, including discharge cut-off voltage and remaining power lower limit; Obtain the working status of the battery, mainly including the battery discharge voltage, the remaining battery power and the operating status of each load; When the battery is in a discharging state, the battery discharging voltage is compared with the discharging cut-off voltage, and the battery remaining capacity is compared with the lower limit of the remaining capacity to obtain a comparison result; When the comparison result meets a preset condition, the load is charged according to the load priority and the remaining power of the battery.

7. The intelligent charge-discharge management and fault maintenance system according to claim 6, characterized in that: When the comparison result satisfies a certain condition, charging the load according to the load priority and the remaining power of the battery includes: Use the following formula to calculate the actual discharge power of the load: Among them, Q remain represents the remaining battery power, n represents the total number of meteorological equipment loads, C represents the rated capacity of the battery, P actual,i Indicates the actual discharge power of the load, P rated,i Indicates the rated power of the load, w i Indicates the discharge priority of the load; The load is charged with the actual discharge power.

8. An intelligent charging and discharging management and fault maintenance method for solar powered meteorological equipment, characterized in that: The method comprises: Set load charging priority according to meteorological equipment operation requirements; Compare and judge the solar power generation power with the total power required by the load to obtain the judgment result; When the judgment result is that the solar power generation power does not meet the load demand, a power control strategy is obtained based on the solar power generation power, the load charging priority and the battery power input power allocation model, and the power control strategy is used to allocate the charging power of the load; When the judgment result is that the solar power generation power meets the load demand, the battery is charged according to the battery operating temperature and charging degree based on the temperature compensation of the fuzzy control.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent charging and discharging management and fault maintenance system according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent charging and discharging management and fault maintenance system according to any one of claims 1 to 7 are implemented.

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