A photovoltaic water pump inverter control method based on maximum power point tracking

Abstract

The present invention relates to the field of inverter control technology, specifically a photovoltaic water pump inverter control method based on maximum power point tracking, comprising the following steps: based on the output end of the photovoltaic module, analyzing the voltage, current, power data and their periodic changes, combining the ambient temperature and the terminal resistance, classifying the abnormalities and outputting the abnormal attribution sequence, optimizing the data sequence, adjusting the pump speed response, issuing the flow drive instruction, screening the supply and demand balance, forming the supply and demand matching signal, adjusting the switch parameters, and outputting the frequency adjustment parameter group. In the present invention, through the interactive analysis of multiple real-time data, the environmental interference and the equipment status can be carefully distinguished, and the data processing priority can be flexibly adjusted for different abnormal attributions to achieve a rapid response matching of the relationship between the pump speed and the flow rate. Combined with the continuous judgment of the balance of power supply and demand, the inverter is promoted to maintain coordinated operation under load fluctuations and external influences, and the output parameters are dynamically adjusted to promote a smooth and coherent water supply process.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter control, and in particular to a photovoltaic water pump inverter control method based on maximum power point tracking. Background Art

[0002] Inverter control technology primarily involves devices and control methods for converting DC power into AC power. This technical field encompasses power conversion principles, power electronic device control, inverter topology, and inverter output waveform adjustment. It is widely used in systems such as photovoltaic power generation, wind power generation, motor drives, and uninterruptible power supplies. Traditional photovoltaic water pump inverter control methods monitor the output voltage and current of photovoltaic modules and utilize maximum power point tracking strategies such as the perturbation observation method or the conductance increment method to adjust the operating point in real time, ensuring that the photovoltaic modules always operate at their maximum power point. Furthermore, by adjusting the inverter's switching control logic, DC-to-AC energy conversion is achieved, meeting the pump's AC power needs.

[0003] Due to the single collection method and fixed data processing process, the existing technology is easily affected by sudden environmental changes or abnormal component status. It lacks the attribution and adaptive regulation of multi-dimensional data, resulting in untimely response to supply and demand fluctuations, misalignment between pump speed and actual flow demand, and when encountering load changes or connection abnormalities, the inverter output is delayed or fluctuates abnormally, affecting the continuity of water supply and reducing the system operation stability and energy utilization efficiency. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a photovoltaic water pump inverter control method based on maximum power point tracking.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a photovoltaic water pump inverter control method based on maximum power point tracking, comprising the following steps:

[0006] S1: Based on the output end of the photovoltaic module, analyze the periodic changes of voltage, current and power, determine the fluctuation of the main data group, screen key abnormal data, and combine the changes in ambient temperature and terminal resistance to attribute and classify the abnormalities to obtain the abnormal attribution sequence;

[0007] S2: Based on the abnormal attribution sequence, determine the abnormal attributes, screen component end or terminal problems, adjust the auxiliary parameter priority, optimize the control process, assign the data ownership and sequence of this cycle, and obtain the intervention path label;

[0008] S3: Based on the intervention path label, compare the corresponding relationship between the real-time flow rate and the water pump speed, analyze the pairing situation, determine the consistency between the two, filter the deviation data, adjust the pump speed response signal, and obtain the flow drive instruction;

[0009] S4: Based on the flow drive instruction, analyze the photovoltaic module power, compare the pump speed drive demand and the power supply relationship, judge the balance between power supply and load, screen the unbalanced state, and issue a load limit instruction to obtain a supply and demand matching signal.

[0010] The present invention has the following improvements: the abnormal attribution sequence includes a fluctuation identification mark, a status diagnosis label, and a classification attribution number; the intervention path label includes a control priority number, a response switching type, and an adjustment parameter index; the flow drive instruction includes a speed regulation mode number, a signal output category, and a target adjustment direction; and the supply and demand matching signal includes a balance status code, a demand allocation number, and a load control parameter.

[0011] The present invention is improved in that the steps of obtaining the abnormal attribution sequence are specifically as follows:

[0012] S111: Based on the output end of the photovoltaic module, analyze the voltage and current data, calculate the changes between the current cycle and the previous cycle, compare the voltage and current change amplitudes with the reference interval, filter the data with fluctuation amplitudes greater than the reference interval, and obtain key deviation data;

[0013] S112: Based on the key deviation data, determine the change trend of the ambient temperature within the cycle, analyze the corresponding relationship between the temperature change direction and the voltage fluctuation trend in combination with the conductivity performance of the terminal, select cases where the change trends of the two are consistent, and obtain the deviation trend consistency value;

[0014] S113: Based on the offset trend consistency, analyze the current change at the inverter input end, the feedback information of the control component and the response state within the control cycle, compare the combination relationship between the parameters, obtain the abnormal behavior feature offset, classify the abnormal type, and obtain the abnormal attribution sequence.

[0015] The present invention is improved in that the step of obtaining the intervention path label is specifically as follows:

[0016] S211: Based on the anomaly attribution sequence, call the time series data of voltage and current, analyze the fluctuation amplitude of voltage and current in adjacent cycles, compare the offset direction with the distribution of acquisition points, and calculate the relationship between the offset concentration position and the anomaly attribution to obtain the anomaly distribution structure characteristics;

[0017] S212: Based on the abnormal distribution structure characteristics, detect the terminal resistance change trend, collect the water temperature difference distribution trajectory, analyze the distribution of auxiliary parameters in the attribution sequence time slice, and arrange the parameter processing order according to the parameter participation frequency and the density of the attribution abnormality linkage to obtain the parameter sorting structure characteristics;

[0018] S213: Based on the parameter sorting structure characteristics, monitor the processing order of each response node in the inverter control trigger process, analyze the matching of parameter input sequence and data category, synchronously adjust the control priority, combine the response switching type and adjustment instruction classification index, adjust the data flow order and update the parameter mapping to obtain the intervention path label.

[0019] The present invention is improved in that the step of obtaining the flow driving instruction is specifically as follows:

[0020] S311: Based on the intervention path label, analyzing the correspondence between the current water pump speed and the real-time flow rate of the pipe network, comparing the numerical fluctuations of the two within the same period, determining the matching status between the two, screening the key difference items in the data, and obtaining the pairing deviation item;

[0021] S312: Analyze the relationship between the flow rate change trend and the speed change trend based on the paired deviation item, determine the consistency of the change direction, identify the representativeness of the deviation item in the trend, optimize the grouping result, and obtain the deviation adjustment interval index;

[0022] S313: According to the deviation adjustment interval index, the pump speed adjustment response strength is obtained, and the speed adjustment response direction and amplitude are determined to obtain a flow drive instruction.

[0023] The present invention is improved in that the step of obtaining the supply-demand matching signal is specifically as follows:

[0024] S411: Based on the flow drive instruction, analyzing the matching between the water pump operation data, calculating the degree of matching between the current water pump flow change and the pump speed setting, determining the difference between the two matching, screening and sorting various change characteristics, and obtaining the flow offset trend;

[0025] S412: Based on the flow rate deviation trend, compare the power supply adaptability between the photovoltaic module output power and the water pump drive power under the current working condition, analyze the power supply and load matching status, screen for insufficient or excessive power supply, optimize the characteristic content, and establish a power supply deviation correlation value;

[0026] S413: Analyze the relationship between the inverter control parameters, the maximum power point current of the photovoltaic module and the real-time current according to the power supply offset correlation, determine the current supply and demand adjustment state, obtain the power supply regulation and coordination amount, screen the power supply and load balance state, and obtain the supply and demand matching signal.

[0027] The present invention is improved in that the steps further include:

[0028] S5: Based on the supply-demand matching signal, determine the current switching frequency and conduction period of the inverter, analyze the corresponding relationship between the instantaneous current and the maximum power point, compare the change rate with the target difference, adjust the switching frequency and conduction time, and output the frequency adjustment parameter group;

[0029] The frequency adjustment parameter group includes a switching frequency setting, a conduction ratio setting, and a period adjustment index.

[0030] The present invention is improved in that the step of obtaining the frequency adjustment parameter group is specifically as follows:

[0031] S511: Based on the supply-demand matching signal, analyzing the relationship between the switching frequency and conduction period of the inverter control module and the instantaneous current and the maximum power point current of the photovoltaic module, determining the supply and demand fluctuation trend under different working conditions, screening abnormal power supply states during cycle switching, and obtaining the dynamic power supply fluctuation characteristics;

[0032] S512: Based on the dynamic power supply fluctuation characteristics, the power supply is compared with the maximum power point output, the coordination efficiency of the frequency and the conduction period under different combinations is screened, the control process is optimized, and a switch conduction combination sequence is established;

[0033] S513: Based on the switch conduction combination sequence, analyze the impact of frequency setting and conduction period adjustment on the supply and demand balance, determine the coordination mode of each parameter in different operation stages, select configuration content that meets the load regulation requirements, and output the frequency adjustment parameter group.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are:

[0035] In the present invention, through the interactive analysis of multiple real-time data, environmental interference and equipment status can be carefully distinguished. According to different abnormal attributions, the data processing priority can be flexibly adjusted to achieve rapid response matching of the relationship between pump speed and flow. Combined with the continuous judgment of the balance of power supply and demand, the inverter is promoted to maintain coordinated operation under load fluctuations and external influences, and the output parameters are dynamically adjusted to promote a smooth and continuous water supply process. The system can effectively resist external disturbances and equipment aging, and improve load adaptability and the stability of power output. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of the main steps of the present invention;

[0037] Figure 2 This is a flowchart for obtaining an abnormal attribution sequence in the present invention;

[0038] Figure 3 This is a flowchart for obtaining intervention path labels in the present invention;

[0039] Figure 4This is a flow chart for obtaining flow drive instructions in the present invention;

[0040] Figure 5 This is a flow chart for obtaining the supply and demand matching signal in the present invention;

[0041] Figure 6 This is a flow chart for obtaining the frequency adjustment parameter group in the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0043] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0044] Example

[0045] See also Figure 1 The present invention provides a technical solution: a photovoltaic water pump inverter control method based on maximum power point tracking, comprising the following steps:

[0046] S1: Based on the output end of the PV module, the collected voltage, current, and power data are analyzed. The change range of the master data group in the current cycle is compared with that in the previous cycle. The voltage and current fluctuations of the master data group are judged to be normal. The data with key deviations in the master data are screened. Then, based on the correlation between ambient temperature, terminal resistance, and the change trend of the master data, combined with the current operating conditions of the inverter input stage current control components, the abnormality type is classified to obtain the abnormality attribution sequence;

[0047] S2: Based on the anomaly attribution sequence, determine the anomaly attribute category, filter whether it is a component-side anomaly or a terminal-side anomaly, adjust the priority of auxiliary parameters in the data analysis sequence, optimize the inverter control trigger process, reallocate the data attribution type and control sequence for this cycle, and obtain the intervention path label;

[0048] S3: Based on the intervention path label, the real-time flow rate of the pipeline network is compared with the current speed output of the pump, the pairing between flow rate and speed is analyzed, the consistency between flow demand and speed output is determined, the deviation between flow rate and speed is screened, the generation mechanism of the pump speed response signal is adjusted, and the flow drive instruction is obtained;

[0049] S4: Based on the flow drive instruction, analyze the output power of the photovoltaic module under the corresponding working conditions, compare the relationship between the pump speed drive demand and the module power supply, determine whether the module power supply capacity is balanced with the load demand, screen the supply and demand imbalance, and issue a load power supply restriction instruction to obtain a supply and demand matching signal;

[0050] S5: Based on the supply and demand matching signal, determine the current configuration of the switching frequency and conduction period of the inverter control module, analyze the corresponding relationship between the instantaneous current of the PV module and the maximum power point output, compare the difference between the current change rate and the target output, and adjust the switching frequency and conduction time parameters to output the frequency adjustment parameter group.

[0051] The abnormal attribution sequence includes the fluctuation identification mark, status diagnosis label, and classification attribution number; the intervention path label includes the control priority number, response switching type, and adjustment parameter index; the flow drive instruction includes the speed regulation mode number, signal output category, and target adjustment direction; the supply and demand matching signal includes the balance status code, demand allocation number, and load control parameters; the frequency adjustment parameter group includes the switching frequency setting, conduction ratio setting, and cycle adjustment index.

[0052] In S1, the amplitude of the change in the main data group refers to the change in voltage and current at the output of the PV module between two adjacent acquisition cycles. This is calculated by subtracting the voltage and current data from the current cycle from the previous cycle, reflecting the stability and trend of the operating status. Whether the fluctuation is normal is determined by analyzing the periodic changes in the main data group (voltage and current) and comparing them with the pre-set normal operating reference range to determine whether the fluctuation is within the acceptable operating range or an abnormal change has occurred. Key deviation data refers to voltage or current data whose value changes exceed the set reference range, indicating that equipment abnormality or environmental impact has occurred and requires special attention and treatment. The inverter input-stage current control component refers to the current detection and regulation module in the inverter input circuit, typically a current sensor, current sampling circuit, and related electronic control unit, used to monitor and manage the dynamic characteristics of the input current. The current operating condition refers to the operating state of the inverter input current control component at a specific moment, including real-time physical parameters such as the current magnitude, circuit operating mode, and load response status. The abnormality type is based on the data analysis results, attributing fluctuations, deviations, and other phenomena to a specific category, such as module performance degradation, environmental interference, and wiring faults, to facilitate subsequent hierarchical response and decision-making.

[0053] In S2, the abnormal attribute category is further subdivided into categories such as electrical performance abnormalities, environment-related abnormalities, and line connection abnormalities based on the attribution in the previous step, so that different control strategies can be adopted for different problems; component-side abnormalities refer to abnormalities attributed to the photovoltaic component itself or its direct output part, such as component aging, damage, or poor contact at the output end; terminal abnormalities refer to abnormalities occurring at the terminal where the inverter connects to the component. Common problems include loose terminals, oxidation, and increased resistance; auxiliary parameters are additional parameters used for abnormal diagnosis and control judgment in addition to the main data group (voltage, current), such as ambient temperature, terminal resistance, water flow, etc.; the control trigger process refers to the step-by-step process of abnormality detection, data judgment, and decision-making before the inverter executes the control action, which determines the actual response order and priority; the attribution type means that after the judgment of this cycle, the data and abnormalities are classified into a specific category (such as component, terminal, environment) for subsequent processing; the control order refers to the response and processing priority ranking of various types of data and parameters in the internal decision logic of the inverter.

[0054] In S3, the real-time flow rate of the pipeline network refers to the real-time water flow rate data of the pipeline (or system) connected to the water pump detected by the sensor, which is used to evaluate the load demand; the matching situation refers to the correspondence between the real-time water flow rate and the current water pump speed (which can be converted to the theoretical output flow rate), to determine whether the actual water supply matches the water demand; consistency refers to analyzing whether there is a deviation between the actual flow rate and the theoretical output of the speed. If the two are consistent, it indicates that the current pump speed can meet the water demand, otherwise there is an imbalance between supply and demand; the deviation between the flow rate and the speed is the flow detection result minus the theoretical output of the speed. When the value is not zero, it is a deviation, indicating that the pump speed needs to be adjusted; the generation mechanism refers to the specific logic or process of generating a response signal to increase or decrease the pump speed after deviation analysis.

[0055] In S4, the corresponding working condition refers to the current system working state formed after step S3, including the actual operating state of the water pump, flow demand, photovoltaic power supply conditions and other combinations; component power supply refers to the current power output capacity of the photovoltaic component that can be provided to the water pump, which is reflected in the actual power output; the supply and demand imbalance state refers to the difference between the pump speed drive power demand and the actual power supply capacity of the component, such as insufficient power supply capacity or excessive demand, and the system cannot maintain the set operating requirements; the load power supply limit instruction is a control signal issued by the control system, the purpose of which is to take measures such as current limiting and power reduction on the water pump or related loads to prevent system overload or energy imbalance.

[0056] In S5, the inverter control module refers to the logic circuit and processing unit inside the inverter for receiving, processing data and sending control signals, generally including a microprocessor, a switch driver module, etc.; the conduction period refers to the duration of conduction (closure) of the power switch tube at the output end of the inverter in each control cycle, which is related to PWM modulation, switching frequency, etc.; the instantaneous current refers to the output current of the photovoltaic module collected at a certain moment, reflecting its instantaneous state of power generation and used for dynamic control; the maximum power point refers to the current and voltage point corresponding to the maximum electric power that the photovoltaic module can output under the current environment, which is the target of MPPT (maximum power point tracking) control.

[0057] See also Figure 2 , the specific steps for obtaining the abnormal attribution sequence are:

[0058] S111: Based on the output end of the photovoltaic module, analyze the voltage and current data, calculate the changes between the current cycle and the previous cycle, compare the voltage and current change amplitudes with the reference interval, filter the data with fluctuation amplitudes greater than the reference interval, and obtain key deviation data;

[0059] The voltage and current values ​​recorded in the current acquisition cycle are called and subtracted from the voltage and current values ​​corresponding to the previous cycle to obtain the change amplitude of the voltage and current in this cycle. The values ​​are then compared with the set normal voltage and current fluctuation ranges to determine whether the voltage and current exceed the set fluctuation ranges. If the voltage difference or current difference exceeds the upper or lower limit of the reference range, it is marked as critical deviation data. This data is identified as a fluctuation signal generated under an unstable state and added to the abnormal cache list for subsequent call. In actual applications, for example, if the voltage changes from 34.2 to 36.8, the upper and lower limits of the reference range are set to ±2.0, and the change amplitude is 2.6, which exceeds the set range and is determined to be a critical deviation. For example, if the current changes from 7.1 to 7.3, with a change amplitude of 0.2, if the set range is ±0.3, this change is considered normal and is not marked. The acquisition control logic outputs all the comparison calculation results as the key deviation data set for this cycle for subsequent abnormality determination.

[0060] S112: Based on the key deviation data, determine the change trend of the ambient temperature within the cycle, combine the conductivity performance of the terminal, analyze the corresponding relationship between the temperature change direction and the voltage fluctuation trend, select cases where the change trends of the two are consistent, and obtain the deviation trend consistency value;

[0061] The ambient temperature information of the cycle is extracted, and the temperature value of the previous acquisition cycle is read and subtracted to analyze whether the temperature change is increasing, decreasing, or remaining stable. The conductive performance parameters of the terminal block corresponding to the data point are then called to analyze the resistance value change trend and compare it with the voltage fluctuation trend. If the temperature increases and the voltage decreases, or the temperature decreases and the voltage increases, the two change trends are determined to be consistent. Such data points are further screened from the key deviation data and included in the deviation trend consistency set. In practical applications, if the sampled temperature changes from 28.0 to 31.1 and the voltage drops from 36.2 to 33.4, this is considered a typical case of consistent trend of voltage drop caused by temperature rise. If the direction of voltage change is inconsistent with the direction of temperature change, it is determined to be non-trend consistent and is eliminated from this screening process. Finally, a set of abnormal points containing consistent voltage fluctuations and temperature trends is generated to determine whether the abnormality is caused by conductive performance degradation under the influence of temperature.

[0062] S113: Based on the offset trend consistency, analyze the inverter input current change, the control component feedback information and the response state within the control cycle, compare the combination relationship between the parameters, and use the formula:

[0063] ;

[0064] Get the offset of abnormal behavior characteristics , classify the abnormal types and obtain the abnormal attribution sequence, where, Indicates the change in the inverter input current between this cycle and the previous cycle. Indicates the change in ambient temperature in this cycle compared to the previous cycle. Indicates the resistance data of the terminal block in this cycle. Indicates the The temperature response impact factor corresponding to each temperature detection point reflects the contribution of each point temperature to the system fluctuation. Indicates the voltage change amplitude corresponding to the key deviation data, Indicates that the inverter control components are The conduction feedback coefficient under the response state reflects the response ability of the control component to voltage changes. Indicates the number of temperature detection points.

[0065] The abnormal behavior characteristic offset comprehensively reflects the overall characteristic change amplitude of the photovoltaic modules and inverter input stage caused by various disturbances within a specific period, and is used to quantitatively characterize the abnormalities or deviations in the system.

[0066] Analyze the current change of the inverter input terminal in the current control cycle, the feedback coefficient of the control component, and the combined correlation between various environmental factors and the control state, and call the current change amplitude of this cycle The collected data is taken as 2.6A, which is normalized to 0.72. The ambient temperature change range is Take the measured value of 5.2℃, normalize it to 0.65, and the terminal resistance The acquisition is 0.8Ω, normalized to 0.40, the temperature response impact factors corresponding to the four temperature detection points are 0.92, 0.87, 0.95, and 0.90, respectively, with a total of 3.64, which is 1 after normalization. The voltage change amplitude of the key deviation point is 3.1V, normalized to 0.68, and the control feedback parameter is 1.7, normalized to 0.59, and the above normalized value is substituted into the formula for calculation:

[0067] ;

[0068] ;

[0069] According to the interval judgment standard of abnormal behavior deviation in the preset control strategy, if If it is classified as a medium-intensity abnormal behavior interval, the current offset intensity conforms to this interval, and the abnormality level is recorded as "medium fluctuation". The behavior offset intensity value is then combined with the previously obtained offset trend consistency value for judgment, and the abnormality type is determined to be caused by temperature influence, resistance degradation, or controller feedback abnormality. The final label number "02-A" is determined through the classification index matching method, and the accompanying state identification mark "△-2" is combined into a complete structure sequence to obtain the abnormal attribution sequence.

[0070] See also Figure 3 ,The steps to obtain the intervention path label are as follows:

[0071] S211: Based on the anomaly attribution sequence, call the time series data of voltage and current, analyze the fluctuation amplitude of voltage and current in adjacent cycles, compare the offset direction with the distribution of acquisition points, and statistically analyze the relationship between the offset concentration location and the anomaly attribution to obtain the anomaly distribution structure characteristics;

[0072] Call the historical data sequence of voltage and current recorded in multiple consecutive cycles, calculate the voltage difference and current difference of two adjacent cycles respectively, and construct the fluctuation amplitude set corresponding to each acquisition cycle. Further perform absolute value calculation on each set of fluctuation amplitude to determine the fluctuation intensity, and then add a direction mark to each fluctuation data point, that is, positive indicates that the voltage or current increases, and negative indicates that it decreases. Combine the direction mark with the corresponding time axis coordinate to generate a fluctuation change sequence with directional distribution, and then call the index of the data point attributed to anomaly in the sequence, check its distribution interval in the time series, for the data point with the same direction in three or more consecutive cycles and the fluctuation amplitude greater than the preset value, The data segment with the baseline value is determined to be a concentrated area of ​​abnormal offsets. Then, with the concentrated area as the center, the directional change trends of other sampling points within the five cycles around it are counted. If more than half of the sampling points are consistent with the central offset direction, the abnormal attribution segment is determined to have a concentrated feature. The abnormal attribution labels appearing in each concentrated area are further classified and counted. For example, if the frequency of component voltage offset in the label is greater than that of other label types, the offset in this segment is determined to be mainly associated with component-side anomalies. Through the above comprehensive processing of offset direction, amplitude, time interval and label frequency, the distribution structure information reflecting the concentration of various types of abnormal attribution and actual fluctuation characteristics is obtained, forming the abnormal distribution structure characteristics.

[0073] S212: Based on the abnormal distribution structure characteristics, detect the terminal resistance change trend, collect the water temperature difference distribution trajectory, analyze the distribution of auxiliary parameters in the attribution sequence time slice, and arrange the parameter processing order according to the parameter participation frequency and the density of the attribution anomaly linkage to obtain the parameter sorting structure characteristics;

[0074] The resistance monitoring channel is enabled to collect the resistance value of the terminal in each fluctuation cycle. The direction of change over time is analyzed through the continuously sampled resistance change value, and the interval where the resistance continues to rise or fall is marked. Then, the water temperature difference sampling channel is synchronously started to obtain the temperature data of the inlet and outlet of the water pump. The temperature difference value is calculated in each sampling cycle, and the temperature difference value is combined into a distribution trajectory according to the time series. Then, the time slices that appear in the attribution sequence are compared with the time points of the above-mentioned water temperature difference and resistance changes. The time overlap between the attribution label and the auxiliary parameter change is counted. The number of times each auxiliary parameter appears in the attribution sequence is counted and summarized. Its changing trend is the frequency of synchronous occurrence of the attribution anomaly label. The frequency value of the parameter appearing in the key anomaly segment and the number of time points overlapping with the anomaly are recorded, and all auxiliary parameters are arranged from high to low according to the frequency of overlap. At the same time, it is detected whether the synchronous linkage characteristics of a certain auxiliary parameter appear repeatedly in multiple anomaly intervals. If a parameter appears in the same direction as the abnormal fluctuation in more than 50% of the attribution time period, it is marked as a high-density linkage parameter, and then prioritized according to the three dimensions of its number of occurrences, linkage strength, and the number of attribution label types covered. It is integrated into a parameter processing priority structure in a weight value mapping manner to form a parameter sorting structure feature.

[0075] S213: Based on the parameter sorting structure characteristics, monitor the processing order of each response node in the inverter control trigger process, analyze the matching of parameter input sequence and data category, synchronously adjust the control priority, combine the response switching type and the adjustment instruction classification index, adjust the data flow order and update the parameter mapping, and obtain the intervention path label;

[0076] The response flow table of the current inverter control module is read. Starting from the first response node, the order of the parameter inputs received is compared item by item with the priority sequence in the parameter sorting structure. This determines whether the current control flow has been triggered prematurely by a low-priority parameter. Based on the classification label of the adjustment module connected to each response node in the process record, the position of each category of adjustment instructions in the triggering flow is counted. This determines whether there is a mismatch between the parameter category and the adjustment index in the current flow. If a mismatch occurs, the flow node is recorded as requiring adjustment. The parameters in the sorting structure are then remapped to the flow nodes according to the new priority order, the data call order in the triggering flow is rearranged, and the data forwarding paths and response sequence numbers between the nodes are adjusted. Finally, based on the switching type associated with each response node, a comparison is made to determine whether there is a conflict in which multiple parameters of the same response category are triggered simultaneously. If there is a conflict, the current path is retained based on the parameter with the higher priority, and the signal response channel with the lower priority is blocked. After the above parameter and process matching, conflict adjustment, and response path mapping update operations, the control response path and the corresponding parameter action index combination in the current cycle are output to form an intervention path label.

[0077] See also Figure 4 ,The specific steps for obtaining traffic driving instructions are:

[0078] S311: Based on the intervention path label, analyze the correspondence between the current water pump speed and the real-time flow rate of the pipe network, compare the numerical fluctuations of the two within the same period, determine the matching status between the two, screen the key difference items in the data, and obtain the pairing deviation item;

[0079] The speed sampling value of the water pump in the current control cycle and the network flow sampling value of the corresponding cycle are called, and the water pump speed is converted into a unit output capacity indicator in a standardized manner according to the percentage system. At the same time, the real-time water flow obtained by the flow sensor is converted into units and a matching relationship table is constructed corresponding to the speed. The speed and flow in the same cycle are further calculated to obtain the absolute difference and difference direction between the two. Then a set of thresholds are set to identify the upper and lower boundaries of the mismatch. For example, the corresponding tolerance range of the speed and flow is set to ±8%. In a certain cycle, the water pump speed is 70%, the theoretical corresponding flow is 2.1 cubic meters / hour, and the actual detection is 1.7 cubic meters / hour. The actual deviation is -19.0%, which exceeds the lower limit of the tolerance range and is recorded as mismatch. Matching items, add them to the deviation data queue, and then use the time axis as a reference to analyze whether there is a trend change in the speed and flow fluctuation amplitude within 3 cycles before and after each mismatching cycle. If there is still an offset in the same direction for two consecutive cycles, for example, the speed continues to decrease but the flow does not increase significantly, it is considered a stable deviation trend. The time index of the offset group is recorded, and the control node associated with the time period is extracted from the intervention path label. The data segment is marked as the key difference source, and then all difference items are sorted according to the absolute value of the error, and the maximum deviation value and its corresponding cycle number and control number are recorded together. The difference points with significant numerical deviations, duration of more than two cycles, and overlap with the intervention control nodes are identified as matching deviation items.

[0080] S312: Analyze the relationship between the flow rate change trend and the speed change trend based on the paired deviation items, determine the consistency of the change direction, identify the representativeness of the deviation item in the trend, optimize the grouping results, and obtain the deviation adjustment interval index;

[0081] First, call the flow change sequence and speed change sequence in the time period corresponding to the difference item, represent the change direction of each cycle with a symbol mark, and record the difference in the value of the increase or decrease between adjacent cycles, then divide the change trends of the flow and speed into groups in chronological order, and perform direction consistency comparison within each group, that is, if the flow increases and the speed also increases in a certain period, it is marked as a consistent trend segment, if the direction is opposite, it is marked as a conflict segment, and perform intersection operation on all trend segments and deviation item indexes to filter out whether the deviation items appear in the trend consistent segment. If so, mark the segment as a trend linkage segment, and record its start and end periods. The ratio of the number of deviation items in the trend segment to the total number of cycles in the segment is counted. If the ratio of deviation points exceeds 50%, the trend segment is marked as a highly representative area. All linkage segments are summarized, and the trend segments with strong representativeness are coded according to their start and end cycles. Combined with the deviation value sorting, the cycle with the largest deviation in the highly representative area is used as the adjustment reference cycle. The two cycles before and after this cycle are further used as the intervention adjustment interval. By recording its index position in the full cycle sequence, the adjustment interval is represented in a triple format, which includes the start cycle, end cycle and representative weight value. The interval is sorted in descending order by weight value to construct the deviation adjustment interval index.

[0082] S313: Adjust the interval index according to the deviation, using the formula:

[0083] ;

[0084] Get the pump speed adjustment response strength , and determine the speed regulation response direction and amplitude to obtain the flow drive instruction, where, Represents the pairing deviation term, that is, the main difference between the current speed of the pump and the real-time flow of the pipe network. Indicates the dynamic response coefficient of the water pump, reflecting the dynamic adjustment ability of the water pump in response to external control signals. It represents the speed-current correlation factor, which describes the coupling relationship between the pump speed and its driving current. It represents the flow inertia correlation factor, reflecting the impact of network flow changes on the overall system inertia. Indicates the power change trend of photovoltaic modules, which refers to the dynamic fluctuation of the current photovoltaic power supply capacity. It represents the deviation direction correction factor, which is used to correct the interaction between the deviation term and the average flow velocity. Indicates the average flow velocity of the pipe network, which represents the average water flow rate of the pipe system during the collection period. It is a very small positive number used to ensure that the denominator is always greater than zero, to prevent the denominator from being zero and resulting in undefined.

[0085] The pump speed regulation response strength is a comprehensive indicator used to quantify the adjustment force or sensitivity required when the water pump adjusts the deviation between flow and speed matching. When a mismatch between water flow and pump speed is detected, and taking into account the current equipment and power supply status, the control system determines to what extent (large / small, fast / slow) the pump speed needs to be adjusted to bring the flow and demand into line. The greater the regulation response strength, the more significant the deviation or the more urgent the adjustment need, and the control system will issue a stronger pump speed adjustment command; otherwise, it will make a subtle or slow adjustment.

[0086] According to the deviation adjustment interval index, the difference parameters in each group are normalized and substituted into the formula, where: is the normalized pairing bias term, is the dynamic response coefficient of the pump, is the normalized speed-current correlation factor, is the normalized flow inertia correlation factor, is the normalized photovoltaic power change trend, is the deviation direction correction factor, is the normalized average flow velocity of the pipe network, is a very small positive number used to prevent the denominator from being zero. The calculation is as follows:

[0087] Molecular part:

[0088] ;

[0089] ;

[0090] ;

[0091] Denominator:

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] Calculate the pump speed regulation response strength:

[0097] ;

[0098] The result shows that the calculated pump speed regulation response intensity under the current operating state is 0.2201, which means that within this data partition, the pump speed should be adjusted and responded at a medium-low rate. The subsequent control logic will adjust the pulse period and drive direction in the flow drive instruction based on this result, and link the response strategy of the flow-speed feedback relationship in the next cycle.

[0099] See also Figure 5 , the specific steps for obtaining the supply and demand matching signal are:

[0100] S411: Based on the flow drive instruction, the matching between the water pump operation data is analyzed, the matching degree between the current water pump flow change and the pump speed setting is calculated, the difference between the two matching is determined, and the various change characteristics are screened and sorted to obtain the flow deviation trend;

[0101] Call the pump speed setting value and water pump flow output value issued in the current cycle, match the water pump operation data, match the actual detected flow with the corresponding set speed one by one, calculate the difference between the theoretical flow output and the actual flow, and mark the positive and negative directions of the difference. If the absolute value of the difference exceeds the set offset reference threshold, it is considered that there is a pairing difference in the cycle. For example, in a certain cycle, the speed is set to 60%, the theoretical output should be 1.8 cubic meters / hour, and the detected flow is 2.2 cubic meters / hour. The difference is +0.4, which exceeds the set difference tolerance of ±0.2 and is marked as over-matching offset. Record the offset value and its direction, and then further call the pump speed setting and actual flow data for multiple consecutive cycles. A multi-period paired difference list is established, and a comparative operation is performed on the data changes of each period to determine the trend direction of the continuous difference. If the difference direction of three consecutive periods is consistent, it is marked as a trend-consistent segment. Subsequently, feature statistical operations are performed on each trend segment to extract three dimensional parameters: difference growth rate, number of consecutive directional periods, and offset level. They are classified into ascending offset, descending offset, and fluctuating offset respectively. The representative periods of each type of offset are numbered, and an offset trend index table is established. By combining and mapping the numerical interval, number of periods, and offset direction corresponding to each type of trend segment, the sorted results of various offset characteristics in this period and its surrounding periods are output as the output basis for the flow offset trend quantity.

[0102] S412: Based on the flow offset trend, compare the power supply adaptability between the PV panel output power and the water pump drive power under the current working conditions, analyze the power supply and load matching status, screen for insufficient or excessive power supply, optimize the feature content, and establish the power supply offset correlation value;

[0103] Retrieve the real-time output power of the photovoltaic components and the power consumption data of the water pump motor in the current cycle. First, collect the voltage and current values ​​of the output end of the photovoltaic components and convert them to obtain the current instantaneous power. At the same time, call the drive power value recorded in the water pump control module for matching processing. Subtract the two sets of power values. If the result is greater than the set power supply matching deviation reference value, it is considered as power supply offset, and then judged as oversupply or undersupply. Mark it according to the positive or negative offset value. For example, if the photovoltaic power supply power in a certain cycle is 650W and the water pump drive power is 720W, the power supply offset is -70W, which exceeds the set deviation limit of 50W and is judged as undersupply. Record the offset value, direction and matching failure, etc. The offset result is then compared with the flow offset trend to analyze whether the two types of offsets appear in the same time period. If there is a time overlap of more than two cycles, it is considered that there is a supply-demand pairing linkage feature between the two. Further, according to the correspondence between the power supply offset value and the flow offset value in the trend segment, a matching degree matrix is ​​constructed. For each trend segment, the function value between the mean power supply pairing error and the flow trend slope is counted. The trend segment with a pairing error higher than the average level is selected as the power supply offset associated segment. The corresponding flow offset interval index, power shortage direction mark and offset cumulative value are extracted, and combined to form the power supply offset associated value for subsequent control strategy input reference.

[0104] S413: Analyze the relationship between the inverter control parameters, the maximum power point current of the PV module, and the real-time current based on the power supply offset correlation, and determine the current supply and demand adjustment status using the formula:

[0105] ;

[0106] Obtaining power supply control coordination quantity , filter to meet the power supply and load balance state, and obtain the supply and demand matching signal, where, Indicates the output current corresponding to the maximum power point of the photovoltaic module under the current working conditions. Indicates the real-time output current of the photovoltaic module under the same working conditions. Indicates the adjustment ratio parameter corresponding to the switching frequency in the current conduction cycle of the inverter control module. Indicates the equivalent driving electric energy required by the water pump under the current working conditions, Indicates the dynamic power deviation reflected by the power supply offset correlation. Indicates the The influencing factors of the adjustment parameters reflect the degree of effect of each parameter on the supply and demand balance adjustment. Indicates the The response parameter of the adjustment parameter in the current control strategy is used to correct the overall adjustment effect. Indicates the number of adjustment parameters and reflects the scale of multi-parameter joint regulation.

[0107] The power supply regulation coordination quantity refers to the quantitative parameter of the dynamic balance between the current power supply capacity of the photovoltaic module and the actual load demand of the water pump, which is reflected by the joint analysis of the maximum power point current of the photovoltaic module, the real-time output current, the inverter switching frequency adjustment parameters, the driving power required by the water pump and multiple adjustment parameters during the operation of the photovoltaic water pump inverter system based on maximum power point tracking. It is a comprehensive indicator to measure the degree of coordinated matching between the power supply capacity and electricity demand in the photovoltaic water pump system after regulation, and reflects the dynamic balance state of the system.

[0108] Call the switching frequency setting, PWM conduction ratio and cycle control index in the inverter control parameters to analyze the current maximum power point current of the photovoltaic module With real-time current The difference between the two is multiplied by the adjustment ratio in the current conduction cycle. , to evaluate the response accuracy of the current photovoltaic system to the maximum power generation point, and the product term is combined with the driving power required by the water pump Correlation with power supply offset After the sum is normalized, the nonlinear input is smoothly compressed by the square root function, and the multiple groups of adjustment parameters set in the control module are called to strengthen the feedback of the supply and demand offset response path. The specific parameters are the adjustment influence factors and response adjustment parameters , perform the mean operation on the product set and add it to the main expression to improve the dynamic trade-off characteristics of the adjustment. The following is the calculation process of the example:

[0109] set up , normalized to 0.82, , normalized to 0.71, , normalized to 0.92, , normalized to 0.68, , normalized to 0.56, the three groups of adjustment parameters are:

[0110] , , , , , ;

[0111] The normalized impact factors and response parameters are:

[0112] , , , , , , substitute the above values ​​into the formula:

[0113] The first part of the operation:

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] The second part is to sum the mean:

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] Adding the two parts together, we get:

[0124] ;

[0125] The results show that the current power supply regulation coordination value is 0.644, which falls within the set coordinated control range of 0.4 to 0.7. This indicates that the current photovoltaic system power supply response is good but there is still a slight offset. It is necessary to continuously monitor whether the conduction period or response sequence needs to be adjusted under the current control priority to generate a supply and demand matching signal. The formula combines the current offset part with the mean of the composite regulation factor after normalizing it proportionally. This not only considers the immediate responsiveness of power supply changes, but also integrates the joint regulation effect between control parameters, making the coordinated feedback more accurate and flexible.

[0126] See also Figure 6 , the specific steps for obtaining the frequency adjustment parameter group are:

[0127] S511: Based on the supply-demand matching signal, analyze the relationship between the switching frequency and conduction period of the inverter control module, the instantaneous current of the photovoltaic module, and the maximum power point current, determine the supply and demand fluctuation trend under different operating conditions, screen out abnormal power supply states during cycle switching, and obtain the dynamic power supply fluctuation characteristics;

[0128] The current switching frequency setting value, conduction period setting value and instantaneous current value collected by the photovoltaic component in the corresponding time period stored in the inverter control module are retrieved, and the switching frequency and conduction period are associated frame by frame according to the sampling period to construct a switch control parameter sequence, and correspond one to one with the instantaneous current value. The difference between the instantaneous current and the maximum power point current setting value in each sampling period is calculated to determine whether the difference is within the set supply and demand matching error tolerance range. For example, if the maximum power point current is 8.0A and the instantaneous current is 7.4A, and the tolerance is set to ±0.3A, then the cycle deviation exceeds the limit and is recorded as a power point deviation item. For the period segments with continuous deviation items, the corresponding switching frequency and conduction period combination is called for classification. The system then analyzes the power supply to determine whether the combination is in a low power supply, high power supply, or supply-demand mismatch state. If the current instantaneous current is continuously low, it is classified as an insufficient power supply condition. If it is continuously high, it is marked as a redundant power supply condition. The classified conditions are then organized into a change trend sequence in chronological order. The cycle switching points in the trend sequence are marked. That is, the period where the matching state suddenly changes to the deviation state, or the deviation state recovers to the matching state, is marked as the switching period. The periods where the switching frequency or conduction period suddenly changes are then screened from the periods to determine whether they cause a supply-demand imbalance. For example, if the frequency is adjusted from 10kHz to 20kHz and a sudden current drop occurs, it is recorded as an abnormal power supply state. Such records are aggregated into power supply dynamic fluctuation characteristics.

[0129] S512: Based on the dynamic fluctuation characteristics of the power supply, the system compares the power supply with the maximum power point output, selects the coordination efficiency of different combinations of frequency and conduction period, optimizes the control process, and establishes a switch conduction combination sequence;

[0130] Call the maximum power point historical output power sequence, compare the difference between the actual output power of the photovoltaic module and the theoretical maximum output power in each fluctuation cycle, and establish a pairing index with the corresponding switching frequency and conduction period combination, record the power supply efficiency deviation value under each combination, further sort the efficiency deviation of all different combinations, and screen out the combination with the largest deviation value. For example, under a combination of a frequency of 12kHz and a conduction period of 60%, the maximum power point is 480W and the actual output is 420W, then the deviation is -60W. If the screening threshold is set to ±40W, this combination is classified as an inefficient combination, and this combination is recorded in each combination. The number of occurrences and the accompanying deviation direction are analyzed, and high-frequency inefficient combinations are extracted and set as key analysis items. Then, the frequency and conduction period parameter combination characteristics of the top 10% combinations with the smallest deviation are reversely searched. For example, if the error is less than 20W multiple times at 15kHz and 70% conduction ratio, it is considered an efficient combination. The combinations are sorted from small to large according to the deviation, and the combination index table is reconstructed. A frequency conduction coordination efficiency level table is established, and different combinations are classified according to the output matching level, for example, divided into three categories: high adaptation, medium adaptation, and low adaptation. All combinations are sorted according to the supply and demand adaptation level to form an optimized switch conduction combination sequence.

[0131] S513: Based on the switch conduction combination sequence, analyze the impact of frequency setting and conduction period adjustment on the supply and demand balance, determine the coordination of various parameters in different operating stages, select configuration content that meets the load regulation requirements, and output the frequency adjustment parameter group;

[0132] The load current demand value and component output current value within the operating cycle corresponding to each combination are called group by group, and the impact of increasing or decreasing the frequency setting value on the load current stability is compared and analyzed. At the same time, it is determined whether there is synchronization between the conduction cycle change and the load response. If the load response current does not change or changes in the opposite direction after the conduction cycle is increased by 10%, it is determined to be a mismatched combination. Such parameter combinations are marked as not meeting the adjustment requirements. Then, the combinations with the same load response change and frequency conduction adjustment direction are extracted to further analyze whether there is a hysteresis cycle. If two cycles are required to achieve a matching state after adjustment, it is recorded as a buffer matching group. All combinations are then evaluated in two dimensions based on regulation response speed and matching accuracy. The response efficiency values ​​of different combinations under specific load changes are calculated. For example, when the load increases from 300W to 350W, the frequency is set to 18kHz, the conduction period is 75%, the stabilization time after the combination is 1 cycle, and the supply-demand deviation is ±15W. In this case, this combination is included in the high-priority configuration. The three operating conditions of light load, medium load, and high load are distinguished according to the operating stage. Combinations with an error of no more than 20W and a response of no more than 2 cycles in each stage are selected. The frequency and conduction period parameters that meet the load regulation requirements are integrated and output as a frequency regulation parameter group.

[0133] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A photovoltaic water pump inverter control method based on maximum power point tracking, characterized in that: The following steps are involved: S1: Based on the output end of the photovoltaic module, analyze the periodic changes of voltage, current and power, determine the fluctuation of the main data group, screen key abnormal data, and combine the changes in ambient temperature and terminal resistance to attribute and classify the abnormalities to obtain the abnormal attribution sequence; S2: Based on the abnormal attribution sequence, determine the abnormal attributes, screen component end or terminal problems, adjust the auxiliary parameter priority, optimize the control process, assign the data ownership and sequence of this cycle, and obtain the intervention path label; S3: Based on the intervention path label, compare the corresponding relationship between the real-time flow rate and the water pump speed, analyze the pairing situation, determine the consistency between the two, filter the deviation data, adjust the pump speed response signal, and obtain the flow drive instruction; The steps for obtaining the traffic driving instruction are specifically as follows: S311: Based on the intervention path label, analyzing the correspondence between the current water pump speed and the real-time flow rate of the pipe network, comparing the numerical fluctuations of the two within the same period, determining the matching status between the two, screening the key difference items in the data, and obtaining the pairing deviation item; S312: Analyze the relationship between the flow rate change trend and the speed change trend based on the paired deviation item, determine the consistency of the change direction, identify the representativeness of the deviation item in the trend, optimize the grouping result, and obtain the deviation adjustment interval index; S313: Obtaining the pump speed adjustment response strength according to the deviation adjustment interval index, and determining the speed adjustment response direction and amplitude to obtain a flow drive instruction; S4: Based on the flow drive instruction, analyze the photovoltaic module power, compare the pump speed drive demand and the power supply relationship, determine the balance between power supply and load, screen the imbalance state, and issue a load limit instruction to obtain a supply and demand matching signal; The steps for obtaining the supply-demand matching signal are specifically as follows: S411: Based on the flow drive instruction, analyzing the matching between the water pump operation data, calculating the degree of matching between the current water pump flow change and the pump speed setting, determining the difference between the two matching, screening and sorting various change characteristics, and obtaining the flow offset trend; S412: Based on the flow rate deviation trend, compare the power supply adaptability between the photovoltaic module output power and the water pump drive power under the current working condition, analyze the power supply and load matching status, screen for insufficient or excessive power supply, optimize the characteristic content, and establish a power supply deviation correlation value; S413: Analyze the relationship between the inverter control parameters, the maximum power point current of the photovoltaic module and the real-time current according to the power supply offset correlation, determine the current supply and demand adjustment state, obtain the power supply regulation and coordination amount, screen the power supply and load balance state, and obtain the supply and demand matching signal.

2. The photovoltaic water pump inverter control method based on maximum power point tracking according to claim 1, characterized in that: The abnormal attribution sequence includes a fluctuation identification mark, a status diagnosis label, and a classification attribution number; the intervention path label includes a control priority number, a response switching type, and an adjustment parameter index; the flow drive instruction includes a speed regulation mode number, a signal output category, and a target adjustment direction; the supply and demand matching signal includes a balance status code, a demand allocation number, and a load control parameter.

3. The photovoltaic water pump inverter control method based on maximum power point tracking according to claim 1, characterized in that: The steps for obtaining the abnormal attribution sequence are specifically as follows: S111: Based on the output end of the photovoltaic module, analyze the voltage and current data, calculate the changes between the current cycle and the previous cycle, compare the voltage and current change amplitudes with the reference interval, filter the data with fluctuation amplitudes greater than the reference interval, and obtain key deviation data; S112: Based on the key deviation data, determine the change trend of the ambient temperature within the cycle, analyze the corresponding relationship between the temperature change direction and the voltage fluctuation trend in combination with the conductivity performance of the terminal, select cases where the change trends of the two are consistent, and obtain the deviation trend consistency value; S113: Based on the offset trend consistency, analyze the current change at the inverter input end, the feedback information of the control component and the response state within the control cycle, compare the combination relationship between the parameters, obtain the abnormal behavior feature offset, classify the abnormal type, and obtain the abnormal attribution sequence.

4. The photovoltaic water pump inverter control method based on maximum power point tracking according to claim 1, characterized in that: The steps for obtaining the intervention path label are specifically as follows: S211: Based on the anomaly attribution sequence, call the time series data of voltage and current, analyze the fluctuation amplitude of voltage and current in adjacent cycles, compare the offset direction with the distribution of acquisition points, and calculate the relationship between the offset concentration position and the anomaly attribution to obtain the anomaly distribution structure characteristics; S212: Based on the abnormal distribution structure characteristics, detect the terminal resistance change trend, collect the water temperature difference distribution trajectory, analyze the distribution of auxiliary parameters in the attribution sequence time slice, and arrange the parameter processing order according to the parameter participation frequency and the density of the attribution abnormality linkage to obtain the parameter sorting structure characteristics; S213: Based on the parameter sorting structure characteristics, monitor the processing order of each response node in the inverter control trigger process, analyze the matching of parameter input sequence and data category, synchronously adjust the control priority, combine the response switching type and adjustment instruction classification index, adjust the data flow order and update the parameter mapping to obtain the intervention path label.

5. The photovoltaic water pump inverter control method based on maximum power point tracking according to claim 1, characterized in that: The steps also include: S5: Based on the supply-demand matching signal, determine the current switching frequency and conduction period of the inverter, analyze the corresponding relationship between the instantaneous current and the maximum power point, compare the change rate with the target difference, adjust the switching frequency and conduction time, and output the frequency adjustment parameter group; The frequency adjustment parameter group includes a switching frequency setting, a conduction ratio setting, and a period adjustment index.

6. The photovoltaic water pump inverter control method based on maximum power point tracking according to claim 5, characterized in that: The steps for obtaining the frequency adjustment parameter group are specifically as follows: S511: Based on the supply-demand matching signal, analyzing the relationship between the switching frequency and conduction period of the inverter control module and the instantaneous current and the maximum power point current of the photovoltaic module, determining the supply and demand fluctuation trend under different working conditions, screening abnormal power supply states during cycle switching, and obtaining the dynamic power supply fluctuation characteristics; S512: Based on the dynamic power supply fluctuation characteristics, the power supply is compared with the maximum power point output, the coordination efficiency of the frequency and the conduction period under different combinations is screened, the control process is optimized, and a switch conduction combination sequence is established; S513: Based on the switch conduction combination sequence, analyze the impact of frequency setting and conduction period adjustment on the supply and demand balance, determine the coordination mode of each parameter in different operation stages, select configuration content that meets the load regulation requirements, and output the frequency adjustment parameter group.

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