A method and system for collecting working parameters of a fish-light complementary photovoltaic power station

Abstract

The application provides a fish-light complementary photovoltaic power station working parameter collection method and system, the method comprises the following steps: dividing an upstream conduction layer, a midstream conduction layer and a downstream conduction layer according to the physical influence path among all parameters to be collected, so as to calibrate the conduction time delay of corresponding parameters between adjacent conduction layers; taking the sampling time of the upstream conduction layer as a trigger starting point, triggering the collection action of corresponding associated parameters of the midstream conduction layer and the downstream conduction layer in sequence according to the conduction time delay, so as to form a sampling trigger sequence; performing granularity normalization processing on a reference time axis according to a unified time node, so as to generate a multi-layer parameter sequence; performing recursive verification of each layer of the multi-layer parameter sequence, and correcting the parameter mapping value of the upstream layer corresponding to the sampling point at the corresponding time of the sampling point which does not satisfy the mapping constraint relationship, so as to generate a target working parameter set. The application can greatly improve the collection efficiency of the working parameters of the fish-light complementary photovoltaic power station.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power station technology, and in particular to a method and system for acquiring operating parameters of a solar-fishery complementary photovoltaic power station. Background Technology

[0002] Photovoltaic power plants that integrate aquaculture and solar power achieve spatial reuse of photovoltaic power generation and aquaculture. Their efficient operation and maintenance and coordinated scheduling highly depend on the synchronous acquisition of multi-dimensional working parameters, such as photovoltaic electrical parameters, irradiance parameters, aquaculture water quality, and under-panel illumination. Existing technologies have built an integrated acquisition system of front-end sensing, gateway aggregation, and platform display, which can simultaneously connect to heterogeneous terminals such as photovoltaic inverters, combiner acquisition units, multi-parameter water quality sensors, and weather stations, basically meeting the needs of independent acquisition and parallel monitoring of the two types of parameters. However, as the requirements for the refinement of aquaculture-solar synergy optimization continue to increase, the shortcomings of existing acquisition methods in terms of data timing matching and coupling analysis support are gradually becoming apparent.

[0003] Current data acquisition systems generally employ independent sampling and scheduling mechanisms for the photovoltaic (PV) and aquaculture sides. PV electrical and irradiance parameters are typically sampled at high frequencies (seconds), while aquaculture parameters such as water quality and underfloor illumination are sampled at low frequencies (minutes). The two types of parameters are matched only by simply aligning timestamps afterward. In this model, the strongly coupled sampling times for parameters like irradiance and underfloor transmittance, as well as water temperature, are misaligned, resulting in mismatched data granularity and an inability to accurately characterize the dynamic relationship between PV operating conditions and the aquaculture environment. Furthermore, the frequent fluctuations in wireless transmission links due to water surface obstruction and signal attenuation easily lead to data disorder and packet loss, further amplifying the timing discrepancies and data gaps, directly resulting in insufficient accuracy in aquaculture-PV synergistic analysis and coordinated control.

[0004] Existing improvement schemes for collecting parameters of solar-fishery complementary power plants mostly focus on multi-protocol access and equipment compatibility optimization, or directly adopt time synchronization methods for general industrial scenarios. They do not combine the coupling characteristics of photovoltaic and fishery parameters to design targeted sampling scheduling and data fusion mechanisms. This makes it difficult to solve the core problems of misaligned timing and mismatched granularity of parameters from the source of collection, thus restricting the further release of the synergistic benefits of solar-fishery complementary power plants. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a method and system for acquiring operating parameters of a solar-fishery complementary photovoltaic power station, in order to solve the problem that the existing technology does not combine the coupling characteristics of photovoltaic and fishery parameters to design a targeted sampling scheduling and data fusion mechanism, which makes it difficult to solve the core problems of misalignment of timing and granularity of different frequency parameters from the source of acquisition.

[0006] The first aspect of the present invention proposes: A method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station, wherein the method includes: All parameters to be collected from photovoltaic, water environment, and fishery perspectives are collected, and the upstream, midstream, and downstream conduction layers are divided according to the physical influence paths between all the parameters to be collected, so as to calibrate the conduction delay of corresponding parameters between adjacent conduction layers. Taking the sampling time of the upstream conduction layer as the triggering starting point, the collection actions of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer are sequentially delayed according to the conduction delay to form a sampling trigger sequence; The raw data collected by the sampling trigger sequence is shifted and mapped along the time axis according to the propagation delay to map to the same reference time axis, and the reference time axis is normalized according to a unified time node to generate a multi-layer parameter sequence. The multi-level parameter sequence is subjected to a recursive verification at each level, and for sampling points that do not satisfy the mapping constraint relationship, the parameter mapping value at the corresponding time of the upstream level of the sampling point is used for correction to generate the target working parameter set.

[0007] The beneficial effects of this invention are as follows: This scheme divides the transmission layers into upstream, midstream, and downstream layers and calibrates the transmission delay of corresponding parameters in adjacent layers. Taking the sampling time of the upstream transmission layer as the triggering starting point, the collection of related parameters in the midstream and downstream layers is triggered sequentially according to the transmission delay. This matches the temporal transmission relationship of heterogeneous parameters from the photovoltaic side, the water environment side, and the fishery side from the source of collection, effectively solving the core problems of temporal misalignment of multi-source parameters and mismatch of sampling granularity. By aligning the original data to the same reference time axis through time axis translation mapping, and then completing granularity normalization and generating multi-layer parameter sequences according to a unified time node, the consistency of time reference and data granularity of multi-dimensional data are ensured. Subsequently, through step-by-step recursive verification, the parameter mapping values ​​of the corresponding time of the upstream layer are used to correct sampling points that do not meet the mapping constraints. Distorted data is eliminated while retaining the physical transmission logic of the parameters. The final target working parameter set has higher temporal matching degree and data credibility, providing reliable data support for the coupling analysis and precise control of fishery-solar complementary scenarios, and significantly improving the fusion quality of multi-source heterogeneous parameters and the accuracy of subsequent decision-making.

[0008] Furthermore, the step of taking the sampling time of the upstream conduction layer as the trigger starting point and sequentially delaying the acquisition actions of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer according to the conduction delay to form a sampling trigger sequence includes: Establish parameter-associated conduction paths from the upstream conduction layer to the midstream conduction layer and from the midstream conduction layer to the downstream conduction layer, and configure an initial conduction delay for each parameter-associated conduction path; Taking the sampling time of the upstream conduction layer as the reference starting point, the instantaneous change rate of each parameter in the current sampling period is detected, and the target correction coefficient is matched with the instantaneous change rate in the preset correction coefficient lookup table, and the initial conduction delay is corrected to obtain the actual trigger delay; The sampling time of the upstream conduction layer is superimposed with the actual trigger delay to generate the acquisition trigger time of each associated parameter of the midstream conduction layer. Based on the acquisition trigger time of each parameter of the midstream conduction layer, the acquisition trigger time of each parameter of the downstream conduction layer is calculated according to the same change rate extraction, coefficient matching and delay correction logic, so as to generate the sampling trigger sequence based on the acquisition trigger time of all parameters.

[0009] Furthermore, the step of generating the sampling trigger sequence based on the acquisition trigger times of all parameters includes: Based on the functional domain to which the sampling terminal belongs, the acquisition trigger times of all parameters are collected by domain to construct the initial trigger time slot queue for each functional domain. Based on the parameter association transmission path, the trigger time of each parameter is bound to the trigger time of its upstream and downstream corresponding parameters as transmission anchor points to form a domain-specific time slot queue. In order to resolve the trigger time conflict of the same acquisition hardware channel in each functional domain, the domain-specific time slot queue is processed by the anchor point linkage staggering method to generate a conflict-free time slot queue for each functional domain. The various conflict-free time slot queues are merged into the global time axis to generate the global sampling trigger sequence.

[0010] Furthermore, the step of performing granular normalization processing on the reference time axis according to a unified time node to generate a multi-layer parameter sequence includes: On the reference time axis, candidate node sets for each layer are generated based on the coupling characteristics of the original sampling intervals and corresponding conduction delays of the parameters of each conduction layer in the upstream, midstream, and downstream. Using the candidate nodes of the midstream transmission layer as the time-series skeleton, nodes that simultaneously satisfy the upstream layer sampling data coverage constraint and the downstream layer parameter response delay constraint are selected as globally unified reference time nodes. For each reference time node, the original mapping data of each transmission layer is normalized to obtain the initial normalized data of each layer. The conduction coupling degree of the correlation parameters in the initial normalized data is calculated. For nodes with conduction coupling degree lower than the preset coupling threshold, verification nodes are added in the temporal neighborhood before and after them. All the reference time nodes and the added verification nodes are arranged in chronological order and bound to the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence.

[0011] Furthermore, the step of arranging all the reference time nodes and the supplementary verification nodes in chronological order and binding them one by one with the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence includes: All reference time nodes and supplementary verification nodes are arranged in ascending order of time sequence and deduplicated to obtain a time sequence node chain. For each node in the time sequence node chain, the upstream layer pre-time and downstream layer post-time are determined based on the corresponding time of the midstream conduction layer and the calibrated conduction delay, forming a three-layer conduction time sequence window for a single node. For each node's three-layer propagation time window, the sampling points within the corresponding window in the original mapping data of each propagation layer are extracted, and resampling is performed at equal intervals according to the window time span to obtain the binding parameter values ​​of each layer corresponding to that node. The binding parameter values ​​of each layer corresponding to each node are aligned sequentially according to the order of the time sequence node chain to form a hierarchical parameter sequence. The boundary time and resampling interval of the three-layer conduction time window of each node are associated and stored to obtain a multi-layer parameter sequence.

[0012] Furthermore, the step of performing a step-by-step recursive verification on the multi-layer parameter sequence, and correcting sampling points that do not satisfy the mapping constraints by using the parameter mapping value at the corresponding time of the upstream level of the sampling point, in order to generate the target working parameter set, includes: The multi-layer parameter sequence is divided into several verification node groups arranged sequentially according to a unified time node. The upstream transmission layer parameters of the first verification node group are used as the initial verification benchmark, and the constraint comparison and anomaly marking of the midstream and downstream transmission layer parameters of this group are completed in sequence. Substitute the midstream and downstream transmission layer parameters after this group of verifications into the next group of verification nodes as the dynamic calibration benchmark for the next group of interlayer mapping constraints, and complete the verification marking of all nodes in turn. For the abnormal sampling points marked in each layer, the upstream layer parameter values ​​of the reference time node corresponding to the abnormal sampling point are retrieved. The correction value is calculated by combining the inter-layer parameter mapping coefficients of the two sets of normal verification nodes before and after the reference time node. The original parameter values ​​of the corresponding abnormal sampling points are replaced. After all abnormal points are replaced, the parameter data of each layer are integrated to obtain the target working parameter set.

[0013] Furthermore, after all the anomaly points have been replaced, the step of integrating the parameter data from each layer to obtain the target working parameter set includes: Extract the dynamic calibration benchmark parameters generated during the sequential verification process, arrange them in time sequence to form a benchmark anchor point sequence, and use each benchmark anchor point as the segment boundary to divide the upstream, midstream, and downstream transmission layer parameter sequences that have completed the replacement of abnormal points into continuous parameter segments that correspond one-to-one with the verification node group. For each parameter segment, the relative changes of the upstream, midstream, and downstream layer parameters relative to the corresponding layer reference parameters at each reference time node within the segment are calculated, using the dynamic calibration reference parameter corresponding to that segment as a reference. The original parameter values ​​of each node are associated and bound with their corresponding relative changes to generate a set of segmented node parameters. All the segmented node parameter sets of parameters are then assembled according to the time sequence of the baseline anchor point to restore the target working parameter set of the complete time sequence dimension.

[0014] The second aspect of the present invention proposes: A system for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station, wherein the system comprises: The acquisition module is used to acquire all the parameters to be acquired from the photovoltaic measurement, water environment and fishery sides, and divides the upstream conduction layer, midstream conduction layer and downstream conduction layer according to the physical influence path between all the parameters to be acquired, so as to calibrate the conduction delay of the corresponding parameters between adjacent conduction layers; The processing module is used to take the sampling time of the upstream conduction layer as the trigger starting point, and sequentially delay the acquisition action of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer according to the conduction delay to form a sampling trigger sequence; The mapping module is used to shift and map the raw data collected by the sampling trigger sequence according to the propagation delay to the same reference time axis, and to perform granular normalization processing on the reference time axis according to a unified time node to generate a multi-layer parameter sequence. The verification module is used to perform a step-by-step recursive verification on the multi-layer parameter sequence, and to correct the sampling points that do not meet the mapping constraints by using the parameter mapping value at the corresponding time of the upstream level of the sampling point, so as to generate the target working parameter set.

[0015] Furthermore, the step of taking the sampling time of the upstream conduction layer as the trigger starting point and sequentially delaying the acquisition actions of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer according to the conduction delay to form a sampling trigger sequence includes: Establish parameter-associated conduction paths from the upstream conduction layer to the midstream conduction layer and from the midstream conduction layer to the downstream conduction layer, and configure an initial conduction delay for each parameter-associated conduction path; Taking the sampling time of the upstream conduction layer as the reference starting point, the instantaneous change rate of each parameter in the current sampling period is detected, and the target correction coefficient is matched with the instantaneous change rate in the preset correction coefficient lookup table, and the initial conduction delay is corrected to obtain the actual trigger delay; The sampling time of the upstream conduction layer is superimposed with the actual trigger delay to generate the acquisition trigger time of each associated parameter of the midstream conduction layer. Based on the acquisition trigger time of each parameter of the midstream conduction layer, the acquisition trigger time of each parameter of the downstream conduction layer is calculated according to the same change rate extraction, coefficient matching and delay correction logic, so as to generate the sampling trigger sequence based on the acquisition trigger time of all parameters.

[0016] Furthermore, the step of generating the sampling trigger sequence based on the acquisition trigger times of all parameters includes: Based on the functional domain to which the sampling terminal belongs, the acquisition trigger times of all parameters are collected by domain to construct the initial trigger time slot queue for each functional domain. Based on the parameter association transmission path, the trigger time of each parameter is bound to the trigger time of its upstream and downstream corresponding parameters as transmission anchor points to form a domain-specific time slot queue. In order to resolve the trigger time conflict of the same acquisition hardware channel in each functional domain, the domain-specific time slot queue is processed by the anchor point linkage staggering method to generate a conflict-free time slot queue for each functional domain. The various conflict-free time slot queues are merged into the global time axis to generate the global sampling trigger sequence.

[0017] Furthermore, the step of performing granular normalization processing on the reference time axis according to a unified time node to generate a multi-layer parameter sequence includes: On the reference time axis, candidate node sets for each layer are generated based on the coupling characteristics of the original sampling intervals and corresponding conduction delays of the parameters of each conduction layer in the upstream, midstream, and downstream. Using the candidate nodes of the midstream transmission layer as the time-series skeleton, nodes that simultaneously satisfy the upstream layer sampling data coverage constraint and the downstream layer parameter response delay constraint are selected as globally unified reference time nodes. For each reference time node, the original mapping data of each transmission layer is normalized to obtain the initial normalized data of each layer. The conduction coupling degree of the correlation parameters in the initial normalized data is calculated. For nodes with conduction coupling degree lower than the preset coupling threshold, verification nodes are added in the temporal neighborhood before and after them. All the reference time nodes and the added verification nodes are arranged in chronological order and bound to the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence.

[0018] Furthermore, the step of arranging all the reference time nodes and the supplementary verification nodes in chronological order and binding them one by one with the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence includes: All reference time nodes and supplementary verification nodes are arranged in ascending order of time sequence and deduplicated to obtain a time sequence node chain. For each node in the time sequence node chain, the upstream layer pre-time and downstream layer post-time are determined based on the corresponding time of the midstream conduction layer and the calibrated conduction delay, forming a three-layer conduction time sequence window for a single node. For each node's three-layer propagation time window, the sampling points within the corresponding window in the original mapping data of each propagation layer are extracted, and resampling is performed at equal intervals according to the window time span to obtain the binding parameter values ​​of each layer corresponding to that node. The binding parameter values ​​of each layer corresponding to each node are aligned sequentially according to the order of the time sequence node chain to form a hierarchical parameter sequence. The boundary time and resampling interval of the three-layer conduction time window of each node are associated and stored to obtain a multi-layer parameter sequence.

[0019] Furthermore, the step of performing a step-by-step recursive verification on the multi-layer parameter sequence, and correcting sampling points that do not satisfy the mapping constraints by using the parameter mapping value at the corresponding time of the upstream level of the sampling point, in order to generate the target working parameter set, includes: The multi-layer parameter sequence is divided into several verification node groups arranged sequentially according to a unified time node. The upstream transmission layer parameters of the first verification node group are used as the initial verification benchmark, and the constraint comparison and anomaly marking of the midstream and downstream transmission layer parameters of this group are completed in sequence. Substitute the midstream and downstream transmission layer parameters after this group of verifications into the next group of verification nodes as the dynamic calibration benchmark for the next group of interlayer mapping constraints, and complete the verification marking of all nodes in turn. For the abnormal sampling points marked in each layer, the upstream layer parameter values ​​of the reference time node corresponding to the abnormal sampling point are retrieved. The correction value is calculated by combining the inter-layer parameter mapping coefficients of the two sets of normal verification nodes before and after the reference time node. The original parameter values ​​of the corresponding abnormal sampling points are replaced. After all abnormal points are replaced, the parameter data of each layer are integrated to obtain the target working parameter set.

[0020] Furthermore, after all the anomaly points have been replaced, the step of integrating the parameter data from each layer to obtain the target working parameter set includes: Extract the dynamic calibration benchmark parameters generated during the sequential verification process, arrange them in time sequence to form a benchmark anchor point sequence, and use each benchmark anchor point as the segment boundary to divide the upstream, midstream, and downstream transmission layer parameter sequences that have completed the replacement of abnormal points into continuous parameter segments that correspond one-to-one with the verification node group. For each parameter segment, the relative changes of the upstream, midstream, and downstream layer parameters relative to the corresponding layer reference parameters at each reference time node within the segment are calculated, using the dynamic calibration reference parameter corresponding to that segment as a reference. The original parameter values ​​of each node are associated and bound with their corresponding relative changes to generate a set of segmented node parameters. All the segmented node parameter sets of parameters are then assembled according to the time sequence of the baseline anchor point to restore the target working parameter set of the complete time sequence dimension.

[0021] The third aspect of the present invention proposes: A computer includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for acquiring operating parameters of a solar-fishery hybrid power station.

[0022] The fourth aspect of the present invention proposes: A readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-described method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station.

[0023] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method for collecting operating parameters of a solar-fishery hybrid photovoltaic power station provided in the first embodiment of the present invention; Figure 2 The structural block diagram of the solar-fishery hybrid photovoltaic power station operating parameter acquisition system provided in the third embodiment of the present invention is shown.

[0025] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0026] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0027] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] Please see Figure 1 The figure shows the method for collecting working parameters of a solar-fishery complementary photovoltaic power station provided in the first embodiment of the present invention. The method for collecting working parameters of a solar-fishery complementary photovoltaic power station provided in this embodiment can eliminate distorted data while retaining the physical transmission logic of the parameters. The final target working parameter set has higher time series matching degree and data credibility, providing reliable data support for the coupling analysis and precise control of solar-fishery complementary scenarios, and significantly improving the fusion quality of multi-source heterogeneous parameters and the accuracy of subsequent decision-making.

[0030] Specifically, this embodiment provides: A method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station, wherein the method includes: Step S10: Collect all parameters to be collected from the photovoltaic measurement, water environment and fishery sides, and divide the upstream conduction layer, midstream conduction layer and downstream conduction layer according to the physical influence path between all parameters to be collected, so as to calibrate the conduction delay of corresponding parameters between adjacent conduction layers; It should be noted that a comprehensive analysis of all parameters to be collected from the photovoltaic (PV) side, the aquatic environment side, and the fisheries side is required: The PV side corresponds to the upstream conduction layer, including six source input parameters: total horizontal irradiance, PV module backsheet temperature, inverter output power, ambient air temperature, relative humidity, and wind speed. These parameters are collected by the power station's edge meteorological station and the PV monitoring system, with an initial sampling interval of 1 minute. The aquatic environment side corresponds to the midstream conduction layer, including six intermediate conduction parameters: surface water temperature, bottom water temperature, dissolved oxygen, pH value, water transparency, and ammonia nitrogen concentration. These parameters are collected by a buoy-type multi-parameter water quality meter, with an initial sampling interval of 5 minutes. The fisheries side corresponds to the downstream conduction layer, including four terminal response parameters: fish activity intensity, feeding activity, plankton density, and chlorophyll a concentration. These parameters are collected by an acoustic fish finder and plankton monitoring equipment, with an initial sampling interval of 15 minutes. Based on the physical influence paths between parameters, a three-tiered conduction layer is defined: the upstream layer is the energy source of the entire coupled system, where photovoltaic panel shading and solar irradiance jointly determine the light and heat input received by the water body; the midstream layer receives the light and heat input, and changes in aquatic environmental parameters occur through heat exchange and mass diffusion; the downstream layer is the terminal of the conduction link, where changes in the aquatic environment directly affect fish behavior and plankton reproduction. Simultaneously, the conduction delays of adjacent layers are calibrated. For example, under clear conditions, the nominal delay for changes in irradiance to reach surface water temperature is 12 minutes, for changes in surface water temperature to reach dissolved oxygen is 8 minutes, and for changes in dissolved oxygen to reach fish activity intensity is 15 minutes. This stratification and delay calibration reconstructs the sampling sequence based on physical laws, fundamentally solving the problem of timing misalignment in synchronous sampling.

[0031] Step S20: Taking the sampling time of the upstream conduction layer as the trigger starting point, the collection actions of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer are sequentially delayed according to the conduction delay to form a sampling trigger sequence; It should be noted that, using the sampling time of the upstream transmission layer as the triggering starting point, and based on the calibrated transmission delay, the collection actions of corresponding parameters in the midstream and downstream transmission layers are triggered sequentially with delays, forming an ordered sampling trigger sequence. This mechanism breaks the traditional mode of synchronous sampling of all parameters, and triggers the collection of each layer in staggered peaks according to the time law of causal transmission, ensuring that the collection time of each parameter corresponds exactly to the time when the upstream influence arrives. This allows the collected data to accurately reflect the causal correspondence between parameters and avoids the time sequence misalignment caused by synchronous sampling, where "the upstream has changed, but the downstream has not yet responded."

[0032] Step S30: The raw data collected by the sampling trigger sequence is shifted and mapped to the same reference time axis according to the conduction delay, and the reference time axis is normalized according to the unified time node to generate a multi-layer parameter sequence. It should be noted that the raw data collected by the sampling trigger sequence is time-shifted and mapped according to the corresponding propagation delay, unifying the upstream and downstream parameters collected at different times onto the same reference time axis, eliminating the time offset caused by differences in trigger times. Then, the reference time axis is normalized to a uniform time node, unifying the data from each layer at different sampling intervals to the same time granularity, generating a structured multi-layer parameter sequence. The shift mapping achieves time alignment of causal parameters, while granularity normalization solves the problem of inconsistent sampling frequencies from different acquisition terminals, ensuring a one-to-one correspondence of parameters in each layer across the time dimension, providing a unified time benchmark for subsequent inter-layer verification and analysis.

[0033] Step S40: Perform a step-by-step recursive verification on the multi-layer parameter sequence, and for sampling points that do not satisfy the mapping constraint relationship, use the parameter mapping value at the corresponding time of the upstream level of the sampling point to correct them, so as to generate the target working parameter set.

[0034] It should be noted that a recursive verification is performed on the multi-layer parameter sequence, checking whether the parameters of each layer at each time point conform to the transmission law based on the physical mapping constraints between parameters. For abnormal sampling points that do not meet the mapping constraints, the parameter mapping value of the corresponding time in the upstream layer is used for correction, ultimately generating a high-quality target working parameter set. The recursive verification mechanism uses the inherent physical correlation between layers to identify abnormal data and corrects it based on the upstream source data, which ensures the rationality and consistency of the data while avoiding the time series break caused by directly removing abnormal points.

[0035] Second Embodiment Furthermore, the step of taking the sampling time of the upstream conduction layer as the trigger starting point and sequentially delaying the acquisition actions of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer according to the conduction delay to form a sampling trigger sequence includes: Establish parameter-associated conduction paths from the upstream conduction layer to the midstream conduction layer and from the midstream conduction layer to the downstream conduction layer, and configure an initial conduction delay for each parameter-associated conduction path; Taking the sampling time of the upstream conduction layer as the reference starting point, the instantaneous change rate of each parameter in the current sampling period is detected, and the target correction coefficient is matched with the instantaneous change rate in the preset correction coefficient lookup table, and the initial conduction delay is corrected to obtain the actual trigger delay; The sampling time of the upstream conduction layer is superimposed with the actual trigger delay to generate the acquisition trigger time of each associated parameter of the midstream conduction layer. Based on the acquisition trigger time of each parameter of the midstream conduction layer, the acquisition trigger time of each parameter of the downstream conduction layer is calculated according to the same change rate extraction, coefficient matching and delay correction logic, so as to generate the sampling trigger sequence based on the acquisition trigger time of all parameters.

[0036] It should be noted that 12 parameter-related conduction paths were identified and established from the upstream to the midstream and from the midstream to the downstream conduction layers, covering core causal links such as "irradiance → surface water temperature," "component temperature → near-surface air temperature → surface water temperature," "surface water temperature → dissolved oxygen," "dissolved oxygen → fish activity intensity," and "plankton density → feeding activity." An initial conduction delay was assigned to each parameter-related conduction path. The initial values ​​were based on the heat transfer equation, the dissolved oxygen kinetic model, and statistical calibration using data from the power station's operation over the past two years, representing the nominal delay under standard operating conditions. For example, under sunny, light wind, and an air temperature of 25°C, the initial conduction delay for "total horizontal irradiance → surface water temperature" was calibrated to 12 minutes, and the initial conduction delay for "surface water temperature → dissolved oxygen" was calibrated to 8 minutes. These initial delays provide a physically consistent benchmark for dynamic correction, preventing the delays from deviating from a reasonable range.

[0037] Using the sampling time of the upstream conduction layer as the baseline, the instantaneous rate of change of each upstream parameter within the current sampling period is calculated, which is the ratio of the difference between two consecutive sampling points to the time interval. Based on the magnitude of the rate of change, the corresponding target correction coefficient is matched in a preset correction coefficient lookup table. The initial conduction delay is scaled using the correction coefficient to obtain the actual trigger delay. The parameter change rate directly determines the energy input intensity; the more drastic the change, the faster the conduction speed and the shorter the delay. For example, when the instantaneous rate of change of irradiance reaches 200 W / m²... For the condition of rapid cloud dispersal and a sudden increase in irradiance, the correction factor is 0.8, and the initial delay of 12 minutes is corrected to 9.6 minutes; when the rate of change in irradiance is less than 20W / ㎡ For the target time, corresponding to a stable operating condition under cloudy conditions, the correction factor is 1.2, and the delay is corrected to 14.4 minutes. The dynamic correction mechanism allows the trigger delay to adapt to the transmission speed of the current operating condition in real time, improving the matching accuracy by more than 30% compared to a fixed delay.

[0038] The actual trigger delay calculated by superimposing the sampling times of the upstream conduction layer generates the acquisition trigger times for each associated parameter in the midstream conduction layer. Then, using the acquisition trigger times of each parameter in the midstream conduction layer as a new benchmark, the acquisition trigger times for each parameter in the downstream conduction layer are calculated according to the same logic of rate of change extraction, coefficient matching, and delay correction. For example, if the upstream irradiance is sampled at 0:00 AM, and the actual delay for water temperature is 9.6 minutes, then the trigger time for midstream water temperature is 9.6 minutes; if the actual delay for dissolved oxygen corresponding to the rate of change in water temperature is 7.2 minutes, then the trigger time for downstream dissolved oxygen is 16.8 minutes; further calculation yields the trigger time for fish activity intensity at 31.8 minutes. Summarizing the trigger times of all 16 parameters in chronological order forms a complete sampling trigger sequence. This step-by-step calculation ensures that the trigger time of each layer is dynamically adjusted based on the actual response speed of the previous layer, and the sampling sequence of the entire conduction link perfectly matches the actual conduction rhythm under the current operating conditions.

[0039] Furthermore, the step of generating the sampling trigger sequence based on the acquisition trigger times of all parameters includes: Based on the functional domain to which the sampling terminal belongs, the acquisition trigger times of all parameters are collected by domain to construct the initial trigger time slot queue for each functional domain. Based on the parameter association transmission path, the trigger time of each parameter is bound to the trigger time of its upstream and downstream corresponding parameters as transmission anchor points to form a domain-specific time slot queue. In order to resolve the trigger time conflict of the same acquisition hardware channel in each functional domain, the domain-specific time slot queue is processed by the anchor point linkage staggering method to generate a conflict-free time slot queue for each functional domain. The various conflict-free time slot queues are merged into the global time axis to generate the global sampling trigger sequence.

[0040] It should be noted that, based on the functional domain to which the sampling terminals belong—namely, the photovoltaic domain, the aquatic environment domain, and the fisheries domain—the trigger times for all parameter acquisition are categorized and grouped, and initial trigger time slot queues are constructed for each functional domain. The photovoltaic domain queue corresponds to meteorological stations and inverter acquisition terminals, the aquatic environment domain queue corresponds to buoy water quality meters, and the fisheries domain queue corresponds to acoustic monitoring and plankton equipment. This domain-based grouping aligns with the distributed deployment architecture of the acquisition terminals, facilitates hardware resource scheduling and conflict detection within a single domain, and also conforms to the management model of power plant subsystem operation and maintenance.

[0041] Based on the parameter-related transmission path, the trigger time of each parameter is bound to the trigger times of its upstream and downstream corresponding parameters as transmission anchor points. A fixed actual transmission delay difference is maintained between these anchor points, forming a domain-specific time slot queue. After binding, adjusting the trigger time of any parameter will synchronously shift its associated upstream and downstream anchor points, ensuring that the transmission timing relationship is not disrupted. To address trigger time conflicts within the same acquisition hardware channel in each functional domain—for example, if a multi-parameter sensor on a water environment buoy needs to simultaneously acquire surface water temperature and pH values, and both parameters' trigger times fall at 9.6 minutes, making parallel acquisition impossible—an anchor point linkage staggered peak method is used to resolve the conflict: the entire transmission link consisting of "water temperature + its upstream and downstream anchor points" is shifted backward by 1 minute, adjusting the water temperature trigger time to 10.6 minutes, and synchronously delaying the corresponding upstream and downstream anchor points by 1 minute, while maintaining the pH value at 9.6 minutes. This method avoids hardware occupancy conflicts while fully preserving the transmission delay relationship between parameters, without disrupting causal correspondence. After resolution, a conflict-free time slot queue is generated for each functional domain.

[0042] The conflict-free time slot queues of each functional domain are uniformly mapped onto the global timeline, arranged and merged in ascending order of timestamps, forming a global sampling trigger sequence covering all parameters to be collected. This sequence can be directly sent to the edge controllers of each acquisition terminal to trigger the acquisition action of the corresponding parameter at a preset time. The sequence also includes the associated anchor point information of each parameter, supporting timing verification on the terminal side.

[0043] Furthermore, the step of performing granular normalization processing on the reference time axis according to a unified time node to generate a multi-layer parameter sequence includes: On the reference time axis, candidate node sets for each layer are generated based on the coupling characteristics of the original sampling intervals and corresponding conduction delays of the parameters of each conduction layer in the upstream, midstream, and downstream. Using the candidate nodes of the midstream transmission layer as the time-series skeleton, nodes that simultaneously satisfy the upstream layer sampling data coverage constraint and the downstream layer parameter response delay constraint are selected as globally unified reference time nodes. For each reference time node, the original mapping data of each transmission layer is normalized to obtain the initial normalized data of each layer. The conduction coupling degree of the correlation parameters in the initial normalized data is calculated. For nodes with conduction coupling degree lower than the preset coupling threshold, verification nodes are added in the temporal neighborhood before and after them. All the reference time nodes and the added verification nodes are arranged in chronological order and bound to the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence.

[0044] It should be noted that, on a unified reference timeline, candidate node sets for each of the upstream, midstream, and downstream transmission layers are generated based on the original sampling intervals of the parameters and the coupling characteristics of the corresponding transmission delays. The upstream layer has a sampling interval of 1 minute and a candidate node density of 1 per minute; the midstream layer has a sampling interval of 5 minutes and a candidate node density of 1 per 5 minutes; and the downstream layer has a sampling interval of 15 minutes and a candidate node density of 1 per 15 minutes. The candidate node sets reflect the original temporal resolution of the data at each layer and are the basic material for normalization processing. Incorporating the transmission delay coupling characteristics ensures that the time scale of the candidate nodes matches the transmission process, avoiding nodes with excessively large spans that cannot correspond to the complete transmission process.

[0045] Using candidate nodes in the midstream transmission layer as the temporal skeleton, as the midstream layer connects upstream and downstream, serving as the central hub of the transmission link, its role as a benchmark can simultaneously ensure coverage of both upstream high-frequency data and downstream low-frequency data. Nodes satisfying two constraints are selected as globally unified benchmark time nodes: first, upstream layer sampling data must cover this moment, meaning original sampling points exist within one minute before and after, allowing for reliable values ​​to be obtained through interpolation; second, the response delay constraint of downstream layer parameters must be met, meaning the upstream influence at this moment has been fully transmitted downstream. After screening, benchmark nodes are obtained at 0, 5, 10, and so on, every 5 minutes. For each benchmark time node, cubic spline interpolation is used to normalize the original mapping data of each transmission layer, obtaining the corresponding parameter values ​​for each layer at that moment, forming the initial normalized data for each layer.

[0046] The conduction coupling degree of each correlation parameter pair in the initial normalized data is calculated, i.e., the time-series Pearson correlation coefficient, reflecting the observability of the conduction relationship at the current time granularity. For example, the coupling degree of "irradiance-surface water temperature" is 0.92, which is higher than the preset threshold of 0.8, indicating that the 5-minute granularity can accurately capture the conduction relationship between the two; while the coupling degree of "surface water temperature-fish activity intensity" is only 0.67, which is lower than the threshold, indicating that the 5-minute granularity is too coarse and cannot accurately capture the dynamic process of fish behavioral response. Therefore, one verification node is added to the temporal neighborhood before and after this type of node, and the local time granularity is encrypted to 2.5 minutes. All the reference time nodes and the added verification nodes are arranged in time sequence and bound to the corresponding parameter values ​​of each layer, finally generating a multi-layer parameter sequence with adaptive time granularity.

[0047] Furthermore, the step of arranging all the reference time nodes and the supplementary verification nodes in chronological order and binding them one by one with the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence includes: All reference time nodes and supplementary verification nodes are arranged in ascending order of time sequence and deduplicated to obtain a time sequence node chain. For each node in the time sequence node chain, the upstream layer pre-time and downstream layer post-time are determined based on the corresponding time of the midstream conduction layer and the calibrated conduction delay, forming a three-layer conduction time sequence window for a single node. For each node's three-layer propagation time window, the sampling points within the corresponding window in the original mapping data of each propagation layer are extracted, and resampling is performed at equal intervals according to the window time span to obtain the binding parameter values ​​of each layer corresponding to that node. The binding parameter values ​​of each layer corresponding to each node are aligned sequentially according to the order of the time sequence node chain to form a hierarchical parameter sequence. The boundary time and resampling interval of the three-layer conduction time window of each node are associated and stored to obtain a multi-layer parameter sequence.

[0048] It should be noted that all reference time nodes and supplementary verification nodes are arranged in ascending chronological order and duplicate nodes are removed to obtain a continuous chronological node chain. For each node in the chronological node chain, taking the corresponding time of the midstream conduction layer as the central reference, the upstream layer's pre-time and the downstream layer's post-time are calculated based on the calibrated conduction delays, together forming a three-layer conduction time window for a single node. For example, at the reference node at 30 minutes, the upstream irradiance pre-time delay is 12 minutes, and the window's front boundary is 18 minutes; the downstream fish activity post-time delay is 20 minutes, and the window's back boundary is 50 minutes; the total span of the single-node conduction window is 32 minutes, completely covering the entire process of "irradiance change → water temperature response → fish activity response".

[0049] For each node's three-layer conduction time-series window, all sampling points falling within the window from the original mapping data of each conduction layer are extracted. Equal-interval resampling is performed according to the window's time span, and a moving average filter is used to aggregate the parameters for each layer corresponding to that node. For example, within the aforementioned 32-minute window, upstream irradiance has 32 1-minute sampling points, and the average is taken as the upstream parameter value for that node; midstream water temperature has 6-7 5-minute sampling points, which are interpolated and then averaged within the window; downstream fish activity has 2-3 15-minute sampling points, which are weighted by time distance to obtain the corresponding value. Window averaging reduces random noise interference from single-point sampling, and the values ​​of each layer strictly correspond to the same conduction process, ensuring accurate and traceable time-series binding relationships.

[0050] Following the sequential order of the time-series node chain, the binding parameter values ​​corresponding to each layer of each node are aligned and arranged in order to form a hierarchical structured parameter sequence. Simultaneously, metadata such as the boundary times, resampling intervals, and number of original sampling points of each node's three-layer conduction time-series window are stored to maintain the traceability of data processing. The resulting multi-layer parameter sequence contains corresponding parameters for each of the upstream, midstream, and downstream layers at each time point, and the values ​​correspond to the same physical conduction process, making it directly usable for inter-layer coupling characteristic analysis.

[0051] Furthermore, the step of performing a step-by-step recursive verification on the multi-layer parameter sequence, and correcting sampling points that do not satisfy the mapping constraints by using the parameter mapping value at the corresponding time of the upstream level of the sampling point, in order to generate the target working parameter set, includes: The multi-layer parameter sequence is divided into several verification node groups arranged sequentially according to a unified time node. The upstream transmission layer parameters of the first verification node group are used as the initial verification benchmark, and the constraint comparison and anomaly marking of the midstream and downstream transmission layer parameters of this group are completed in sequence. Substitute the midstream and downstream transmission layer parameters after this group of verifications into the next group of verification nodes as the dynamic calibration benchmark for the next group of interlayer mapping constraints, and complete the verification marking of all nodes in turn. For the abnormal sampling points marked in each layer, the upstream layer parameter values ​​of the reference time node corresponding to the abnormal sampling point are retrieved. The correction value is calculated by combining the inter-layer parameter mapping coefficients of the two sets of normal verification nodes before and after the reference time node. The original parameter values ​​of the corresponding abnormal sampling points are replaced. After all abnormal points are replaced, the parameter data of each layer are integrated to obtain the target working parameter set.

[0052] It should be noted that the multi-layer parameter sequence is divided into several verification node groups arranged sequentially according to a unified time node. Each verification node group contains parameters from the upstream, midstream, and downstream layers at the same time. Using the upstream transmission layer parameters of the first verification node group as the initial verification benchmark, and based on a pre-established inter-layer physical mapping constraint model, the constraint comparison of the midstream and downstream transmission layer parameters in this group is completed sequentially. Parameter points that do not conform to the mapping constraint range are marked as abnormal sampling points. For example, at a certain time, the dissolved oxygen sensor's sampling value jumps to 12 mg / L due to interference from water surface bubbles. However, based on the current water temperature of 28℃, the theoretical value of saturated dissolved oxygen is approximately 7.8 mg / L, and the mapping constraint range is 7.0~8.5 mg / L. This sampling point clearly exceeds the reasonable range and is marked as abnormal. The inter-layer mapping constraint is determined jointly based on the mechanistic model and historical statistical data, covering various transmission relationships such as positive and negative correlations, and is the core basis for anomaly identification.

[0053] The parameters of the midstream and downstream conduction layers verified in this group are substituted into the next group of verification nodes as the dynamic calibration benchmark for the next group of interlayer mapping constraints. This logic is followed sequentially to complete the verification marking of all nodes. The dynamic benchmark continuation mechanism takes into account the continuity of the conduction process. For example, if the water temperature of the previous node is 27℃ and the dissolved oxygen is 7.9mg / L, and the water temperature of the next node rises to 28℃, then the dissolved oxygen should be in the range of 7.0~7.8mg / L, rather than using a fixed global threshold. This mechanism can adapt to operating condition drift scenarios such as slow rises and falls in water temperature, reducing misjudgments caused by stable changes in operating conditions.

[0054] For each layer of marked anomalous sampling points, the upstream layer parameter values ​​corresponding to the reference time node are retrieved. Combined with the interlayer parameter mapping coefficients of the two sets of normal verification nodes before and after that reference time node, the theoretical correction value for that point is calculated. For example, for the dissolved oxygen anomaly point mentioned above, the upstream water temperature is 28℃, and the water temperature-dissolved oxygen mapping coefficient of the normal nodes before and after it is approximately -0.12 mg / L / ℃. The calculated correction value is 7.7 mg / L, replacing the original anomalous value. After all anomalous points are replaced, the parameter data from each layer are integrated to obtain a preliminary target working parameter set. The correction method based on upstream parameters and mapping coefficients conforms to the physical transmission logic, improving the repair accuracy by more than 40% compared to simple linear interpolation.

[0055] Furthermore, after all the anomaly points have been replaced, the step of integrating the parameter data from each layer to obtain the target working parameter set includes: Extract the dynamic calibration benchmark parameters generated during the sequential verification process, arrange them in time sequence to form a benchmark anchor point sequence, and use each benchmark anchor point as the segment boundary to divide the upstream, midstream, and downstream transmission layer parameter sequences that have completed the replacement of abnormal points into continuous parameter segments that correspond one-to-one with the verification node group. For each parameter segment, the relative changes of the upstream, midstream, and downstream layer parameters relative to the corresponding layer reference parameters at each reference time node within the segment are calculated, using the dynamic calibration reference parameter corresponding to that segment as a reference. The original parameter values ​​of each node are associated and bound with their corresponding relative changes to generate a set of segmented node parameters. All the segmented node parameter sets of parameters are then assembled according to the time sequence of the baseline anchor point to restore the target working parameter set of the complete time sequence dimension.

[0056] It should be noted that the dynamic calibration benchmark parameters generated during the sequential verification process are extracted and arranged chronologically to form a benchmark anchor point sequence. Anchor points typically correspond to nodes where operating conditions change significantly, such as sunrise, midday peak irradiance, and sunset. Using each benchmark anchor point as a segment boundary, the upstream, midstream, and downstream conduction layer parameter sequences after anomaly point replacement are divided into continuous parameter segments corresponding one-to-one with the verification node groups. For example, a typical summer day at this power station is divided into three operating condition segments: the morning segment (6:00-11:00) is the irradiance rise period, the midday segment (11:00-15:00) is the high-temperature, high-irradiance period, and the afternoon segment (15:00-19:00) is the irradiance decline period. Each segment corresponds to a relatively stable conduction condition, and the inter-layer mapping relationship within the segment remains consistent.

[0057] For each parameter segment, using the corresponding dynamic calibration reference parameter as the benchmark, the relative changes of the upstream, midstream, and downstream layer parameters relative to the corresponding layer benchmark parameter at each reference time point within the segment are calculated and expressed as percentages. For example, in the noon segment, 11:00 is used as the reference anchor point, and the irradiance benchmark value is 800 W / m². 2 At 12:00, the irradiance was 1100 W / m², with a relative change of +37.5%; the surface water temperature baseline was 26℃, and at 13:00 it was 29℃, with a relative change of +11.5%; the dissolved oxygen baseline was 8.2 mg / L, and at 14:00 it was 7.1 mg / L, with a relative change of -13.4%. The relative changes directly reflect the fluctuation range and trend of each parameter within the operating range, eliminating the interference of absolute baseline differences on the analysis of conduction patterns.

[0058] The original parameter values ​​of each node are associated and bound with their corresponding relative changes to generate a segmented node parameter set. Then, according to the temporal order of the baseline anchor points, the segmented node parameter sets of all parameter segments are concatenated to reconstruct the complete time-series target working parameter set. The final target working parameter set contains four types of information: absolute values ​​of the three layers of parameters, relative changes, segmented anchor point identifiers, and transmission delay metadata. The data structure is complete and the temporal correspondence is accurate, which can be directly used for various subsequent applications such as power generation efficiency analysis of fishery-solar hybrid power plants, water environment evolution simulation, aquaculture feeding scheduling, and multi-system collaborative optimization.

[0059] Please see Figure 2 The third embodiment of the present invention provides: A system for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station, wherein the system comprises: The acquisition module is used to acquire all the parameters to be acquired from the photovoltaic measurement, water environment and fishery sides, and divides the upstream conduction layer, midstream conduction layer and downstream conduction layer according to the physical influence path between all the parameters to be acquired, so as to calibrate the conduction delay of the corresponding parameters between adjacent conduction layers; The processing module is used to take the sampling time of the upstream conduction layer as the trigger starting point, and sequentially delay the acquisition action of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer according to the conduction delay to form a sampling trigger sequence; The mapping module is used to shift and map the raw data collected by the sampling trigger sequence according to the propagation delay to the same reference time axis, and to perform granular normalization processing on the reference time axis according to a unified time node to generate a multi-layer parameter sequence. The verification module is used to perform a step-by-step recursive verification on the multi-layer parameter sequence, and to correct the sampling points that do not meet the mapping constraints by using the parameter mapping value at the corresponding time of the upstream level of the sampling point, so as to generate the target working parameter set.

[0060] Furthermore, the step of taking the sampling time of the upstream conduction layer as the trigger starting point and sequentially delaying the acquisition actions of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer according to the conduction delay to form a sampling trigger sequence includes: Establish parameter-associated conduction paths from the upstream conduction layer to the midstream conduction layer and from the midstream conduction layer to the downstream conduction layer, and configure an initial conduction delay for each parameter-associated conduction path; Taking the sampling time of the upstream conduction layer as the reference starting point, the instantaneous change rate of each parameter in the current sampling period is detected, and the target correction coefficient is matched with the instantaneous change rate in the preset correction coefficient lookup table, and the initial conduction delay is corrected to obtain the actual trigger delay; The sampling time of the upstream conduction layer is superimposed with the actual trigger delay to generate the acquisition trigger time of each associated parameter of the midstream conduction layer. Based on the acquisition trigger time of each parameter of the midstream conduction layer, the acquisition trigger time of each parameter of the downstream conduction layer is calculated according to the same change rate extraction, coefficient matching and delay correction logic, so as to generate the sampling trigger sequence based on the acquisition trigger time of all parameters.

[0061] Furthermore, the step of generating the sampling trigger sequence based on the acquisition trigger times of all parameters includes: Based on the functional domain to which the sampling terminal belongs, the acquisition trigger times of all parameters are collected by domain to construct the initial trigger time slot queue for each functional domain. Based on the parameter association transmission path, the trigger time of each parameter is bound to the trigger time of its upstream and downstream corresponding parameters as transmission anchor points to form a domain-specific time slot queue. In order to resolve the trigger time conflict of the same acquisition hardware channel in each functional domain, the domain-specific time slot queue is processed by the anchor point linkage staggering method to generate a conflict-free time slot queue for each functional domain. The various conflict-free time slot queues are merged into the global time axis to generate the global sampling trigger sequence.

[0062] Furthermore, the step of performing granular normalization processing on the reference time axis according to a unified time node to generate a multi-layer parameter sequence includes: On the reference time axis, candidate node sets for each layer are generated based on the coupling characteristics of the original sampling intervals and corresponding conduction delays of the parameters of each conduction layer in the upstream, midstream, and downstream. Using the candidate nodes of the midstream transmission layer as the time-series skeleton, nodes that simultaneously satisfy the upstream layer sampling data coverage constraint and the downstream layer parameter response delay constraint are selected as globally unified reference time nodes. For each reference time node, the original mapping data of each transmission layer is normalized to obtain the initial normalized data of each layer. The conduction coupling degree of the correlation parameters in the initial normalized data is calculated. For nodes with conduction coupling degree lower than the preset coupling threshold, verification nodes are added in the temporal neighborhood before and after them. All the reference time nodes and the added verification nodes are arranged in chronological order and bound to the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence.

[0063] Furthermore, the step of arranging all the reference time nodes and the supplementary verification nodes in chronological order and binding them one by one with the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence includes: All reference time nodes and supplementary verification nodes are arranged in ascending order of time sequence and deduplicated to obtain a time sequence node chain. For each node in the time sequence node chain, the upstream layer pre-time and downstream layer post-time are determined based on the corresponding time of the midstream conduction layer and the calibrated conduction delay, forming a three-layer conduction time sequence window for a single node. For each node's three-layer propagation time window, the sampling points within the corresponding window in the original mapping data of each propagation layer are extracted, and resampling is performed at equal intervals according to the window time span to obtain the binding parameter values ​​of each layer corresponding to that node. The binding parameter values ​​of each layer corresponding to each node are aligned sequentially according to the order of the time sequence node chain to form a hierarchical parameter sequence. The boundary time and resampling interval of the three-layer conduction time window of each node are associated and stored to obtain a multi-layer parameter sequence.

[0064] Furthermore, the step of performing a step-by-step recursive verification on the multi-layer parameter sequence, and correcting sampling points that do not satisfy the mapping constraints by using the parameter mapping value at the corresponding time of the upstream level of the sampling point, in order to generate the target working parameter set, includes: The multi-layer parameter sequence is divided into several verification node groups arranged sequentially according to a unified time node. The upstream transmission layer parameters of the first verification node group are used as the initial verification benchmark, and the constraint comparison and anomaly marking of the midstream and downstream transmission layer parameters of this group are completed in sequence. Substitute the midstream and downstream transmission layer parameters after this group of verifications into the next group of verification nodes as the dynamic calibration benchmark for the next group of interlayer mapping constraints, and complete the verification marking of all nodes in turn. For the abnormal sampling points marked in each layer, the upstream layer parameter values ​​of the reference time node corresponding to the abnormal sampling point are retrieved. The correction value is calculated by combining the inter-layer parameter mapping coefficients of the two sets of normal verification nodes before and after the reference time node. The original parameter values ​​of the corresponding abnormal sampling points are replaced. After all abnormal points are replaced, the parameter data of each layer are integrated to obtain the target working parameter set.

[0065] Furthermore, after all the anomaly points have been replaced, the step of integrating the parameter data from each layer to obtain the target working parameter set includes: Extract the dynamic calibration benchmark parameters generated during the sequential verification process, arrange them in time sequence to form a benchmark anchor point sequence, and use each benchmark anchor point as the segment boundary to divide the upstream, midstream, and downstream transmission layer parameter sequences that have completed the replacement of abnormal points into continuous parameter segments that correspond one-to-one with the verification node group. For each parameter segment, the relative changes of the upstream, midstream, and downstream layer parameters relative to the corresponding layer reference parameters at each reference time node within the segment are calculated, using the dynamic calibration reference parameter corresponding to that segment as a reference. The original parameter values ​​of each node are associated and bound with their corresponding relative changes to generate a set of segmented node parameters. All the segmented node parameter sets of parameters are then assembled according to the time sequence of the baseline anchor point to restore the target working parameter set of the complete time sequence dimension.

[0066] The fourth embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station.

[0067] The fifth embodiment of the present invention provides a readable storage medium storing a computer program thereon, wherein when the program is executed by a processor, it implements the above-described method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station.

[0068] In summary, the method and system for acquiring operating parameters of a solar-fishery complementary photovoltaic power station provided in the above embodiments of the present invention can eliminate distorted data while retaining the physical transmission logic of the parameters. The final target operating parameter set has higher time-series matching degree and data reliability, providing reliable data support for the coupled analysis and precise control of solar-fishery complementary scenarios, and significantly improving the fusion quality of multi-source heterogeneous parameters and the accuracy of subsequent decision-making.

[0069] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0070] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0071] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0072] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0073] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0074] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station, characterized in that, The method includes: All parameters to be collected from photovoltaic, water environment, and fishery perspectives are collected, and the upstream, midstream, and downstream conduction layers are divided according to the physical influence paths between all the parameters to be collected, so as to calibrate the conduction delay of corresponding parameters between adjacent conduction layers. Taking the sampling time of the upstream conduction layer as the triggering starting point, the collection actions of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer are sequentially delayed according to the conduction delay to form a sampling trigger sequence; The raw data collected by the sampling trigger sequence is shifted and mapped along the time axis according to the propagation delay to map to the same reference time axis, and the reference time axis is normalized according to a unified time node to generate a multi-layer parameter sequence. The multi-level parameter sequence is subjected to a recursive verification at each level, and for sampling points that do not satisfy the mapping constraint relationship, the parameter mapping value at the corresponding time of the upstream level of the sampling point is used for correction to generate the target working parameter set.

2. The method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station according to claim 1, characterized in that, The step of taking the sampling time of the upstream conduction layer as the trigger starting point and sequentially delaying the acquisition actions of the corresponding associated parameters of the midstream and downstream conduction layers according to the conduction delay to form a sampling trigger sequence includes: Establish parameter-associated conduction paths from the upstream conduction layer to the midstream conduction layer and from the midstream conduction layer to the downstream conduction layer, and configure an initial conduction delay for each parameter-associated conduction path; Taking the sampling time of the upstream conduction layer as the reference starting point, the instantaneous change rate of each parameter in the current sampling period is detected, and the target correction coefficient is matched with the instantaneous change rate in the preset correction coefficient lookup table, and the initial conduction delay is corrected to obtain the actual trigger delay; The sampling time of the upstream conduction layer is superimposed with the actual trigger delay to generate the acquisition trigger time of each associated parameter of the midstream conduction layer. Based on the acquisition trigger time of each parameter of the midstream conduction layer, the acquisition trigger time of each parameter of the downstream conduction layer is calculated according to the same change rate extraction, coefficient matching and delay correction logic, so as to generate the sampling trigger sequence based on the acquisition trigger time of all parameters.

3. The method for acquiring operating parameters of a solar-fishery complementary photovoltaic power station according to claim 2, characterized in that, The step of generating the sampling trigger sequence based on the acquisition trigger times of all parameters includes: Based on the functional domain to which the sampling terminal belongs, the acquisition trigger times of all parameters are collected by domain to construct the initial trigger time slot queue for each functional domain. Based on the parameter association transmission path, the trigger time of each parameter is bound to the trigger time of its upstream and downstream corresponding parameters as transmission anchor points to form a domain-specific time slot queue. In order to resolve the trigger time conflict of the same acquisition hardware channel in each functional domain, the domain-specific time slot queue is processed by the anchor point linkage staggering method to generate a conflict-free time slot queue for each functional domain. The various conflict-free time slot queues are merged into the global time axis to generate the global sampling trigger sequence.

4. The method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station according to claim 1, characterized in that, The step of performing granular normalization processing on the reference time axis according to a unified time node to generate a multi-layer parameter sequence includes: On the reference time axis, candidate node sets for each layer are generated based on the coupling characteristics of the original sampling intervals and corresponding conduction delays of the parameters of each conduction layer in the upstream, midstream, and downstream. Using the candidate nodes of the midstream transmission layer as the time-series skeleton, nodes that simultaneously satisfy the upstream layer sampling data coverage constraint and the downstream layer parameter response delay constraint are selected as globally unified reference time nodes. For each reference time node, the original mapping data of each transmission layer is normalized to obtain the initial normalized data of each layer. The conduction coupling degree of the correlation parameters in the initial normalized data is calculated. For nodes with conduction coupling degree lower than the preset coupling threshold, verification nodes are added in the temporal neighborhood before and after them. All the reference time nodes and the added verification nodes are arranged in chronological order and bound to the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence.

5. The method for acquiring operating parameters of a solar-fishery complementary photovoltaic power station according to claim 4, characterized in that, The step of arranging all the reference time nodes and the supplementary verification nodes in chronological order and binding them one by one with the corresponding parameter values ​​of each layer to generate the multi-layer parameter sequence includes: All reference time nodes and supplementary verification nodes are arranged in ascending order of time sequence and deduplicated to obtain a time sequence node chain. For each node in the time sequence node chain, the upstream layer pre-time and downstream layer post-time are determined based on the corresponding time of the midstream conduction layer and the calibrated conduction delay, forming a three-layer conduction time sequence window for a single node. For each node's three-layer propagation time window, the sampling points within the corresponding window in the original mapping data of each propagation layer are extracted, and resampling is performed at equal intervals according to the window time span to obtain the binding parameter values ​​of each layer corresponding to that node. The binding parameter values ​​of each layer corresponding to each node are aligned sequentially according to the order of the time sequence node chain to form a hierarchical parameter sequence. The boundary time and resampling interval of the three-layer conduction time window of each node are associated and stored to obtain a multi-layer parameter sequence.

6. The method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station according to claim 1, characterized in that, The steps of performing a step-by-step recursive verification on the multi-layer parameter sequence and correcting sampling points that do not satisfy the mapping constraints by using the parameter mapping value at the corresponding time of the upstream level of the sampling point to generate the target working parameter set include: The multi-layer parameter sequence is divided into several verification node groups arranged sequentially according to a unified time node. The upstream transmission layer parameters of the first verification node group are used as the initial verification benchmark, and the constraint comparison and anomaly marking of the midstream and downstream transmission layer parameters of this group are completed in sequence. Substitute the midstream and downstream transmission layer parameters after this group of verifications into the next group of verification nodes as the dynamic calibration benchmark for the next group of interlayer mapping constraints, and complete the verification marking of all nodes in turn. For the abnormal sampling points marked in each layer, the upstream layer parameter values ​​of the reference time node corresponding to the abnormal sampling point are retrieved. The correction value is calculated by combining the inter-layer parameter mapping coefficients of the two sets of normal verification nodes before and after the reference time node. The original parameter values ​​of the corresponding abnormal sampling points are replaced. After all abnormal points are replaced, the parameter data of each layer are integrated to obtain the target working parameter set.

7. The method for acquiring operating parameters of a solar-fishery complementary photovoltaic power station according to claim 6, characterized in that, After all anomalies have been replaced, the steps for integrating parameter data from each layer to obtain the target working parameter set include: Extract the dynamic calibration benchmark parameters generated during the sequential verification process, arrange them in time sequence to form a benchmark anchor point sequence, and use each benchmark anchor point as the segment boundary to divide the upstream, midstream, and downstream transmission layer parameter sequences that have completed the replacement of abnormal points into continuous parameter segments that correspond one-to-one with the verification node group. For each parameter segment, the relative changes of the upstream, midstream, and downstream layer parameters relative to the corresponding layer reference parameters at each reference time node within the segment are calculated, using the dynamic calibration reference parameter corresponding to that segment as a reference. The original parameter values ​​of each node are associated and bound with their corresponding relative changes to generate a set of segmented node parameters. All the segmented node parameter sets of parameters are then assembled according to the time sequence of the baseline anchor point to restore the target working parameter set of the complete time sequence dimension.

8. A system for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station, characterized in that, The system includes: The acquisition module is used to acquire all the parameters to be acquired from the photovoltaic measurement, water environment and fishery sides, and divides the upstream conduction layer, midstream conduction layer and downstream conduction layer according to the physical influence path between all the parameters to be acquired, so as to calibrate the conduction delay of the corresponding parameters between adjacent conduction layers; The processing module is used to take the sampling time of the upstream conduction layer as the trigger starting point, and sequentially delay the acquisition action of the corresponding associated parameters of the midstream conduction layer and the downstream conduction layer according to the conduction delay to form a sampling trigger sequence; The mapping module is used to shift and map the raw data collected by the sampling trigger sequence according to the propagation delay to the same reference time axis, and to perform granular normalization processing on the reference time axis according to a unified time node to generate a multi-layer parameter sequence. The verification module is used to perform a step-by-step recursive verification on the multi-layer parameter sequence, and to correct the sampling points that do not meet the mapping constraints by using the parameter mapping value at the corresponding time of the upstream level of the sampling point, so as to generate the target working parameter set.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station as described in any one of claims 1 to 7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for acquiring operating parameters of a solar-fishery hybrid photovoltaic power station as described in any one of claims 1 to 7.

Images