Algae efficient cultivation control method and system

Abstract

The application provides an algal efficient cultivation control method and system, relates to the technical field of data processing, and comprises the following steps: dividing a water body into multiple equal-interval depth levels to obtain level division data, calculating the oxygen concentration difference value between adjacent levels and the gas diffusion trend value per unit depth, determining the disturbance tension value of each level, calculating the disturbance ratio value of each level, combining the oxygen concentration recovery time of each level in a historical period, calculating the stirring frequency coefficient and the duration value, obtaining first control data, extracting the oxygen concentration recovery amplitude in different time periods, calculating the actual disturbance response value, and comparing the actual disturbance response value with the disturbance tension data by difference to obtain disturbance error data, analyzing the change direction of the disturbance error, calculating the error score value of the disturbance error of each level by error calculation and direction superposition, and iteratively updating the first control data. The application can dynamically adjust the stirring frequency to ensure balanced growth of algae.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an efficient algae cultivation control method and system. BACKGROUND

[0002] In the existing industrialized cultivation process of algae, in order to realize the standardization and efficient output of algae, a constant temperature, constant light source environment and a multi-parameter online monitoring control system of dissolved oxygen, water temperature and pH value are usually configured in the production workshop. These systems combine with automatic stirrers and bottom oxygenation devices to realize real-time regulation of the growth environment of algae, so that algae can rapidly reproduce under constant water quality and physical conditions.

[0003] Some specific algae species need to maintain the dissolved oxygen concentration between 6-8 mg / L, which often needs to be operated by combining bottom continuous air blowing and stirring. However, due to the gradient distribution of dissolved oxygen concentration at different depths in the water body, it may be difficult to eliminate the local over-oxygen or anoxic area by constant frequency stirring. For example, in a large algae tank, if the stirrer does not adjust the speed according to the dynamic changes of water level and water temperature, it may cause problems such as over-exposure in the upper layer and anoxic in the bottom, affecting the balanced distribution and growth of algae. SUMMARY

[0004] The purpose of the present application is to provide an efficient algae cultivation control method and system to solve the problems mentioned in the background.

[0005] To solve the above technical problems, the technical solution of the present application is as follows:

[0006] In a first aspect, an efficient algae cultivation control method is provided, which comprises:

[0007] Collecting water level data, oxygen concentration data and temperature data, and performing vertical direction segmentation processing according to the water level data to divide the water body into multiple equal-interval depth levels to obtain level division data;

[0008] According to the level division data and the oxygen concentration data, the oxygen concentration difference between adjacent levels and the gas diffusion trend value per unit depth are calculated, the disturbance tension value of each level is determined, and the disturbance tension data is obtained;

[0009] According to the disturbance tension data and the temperature data, the disturbance ratio of each level is calculated, and combined with the oxygen concentration recovery time of each level in the historical period, the stirring frequency coefficient and the duration value are calculated to obtain the first control data;

[0010] According to the first control data, the oxygen concentration recovery amplitude in different time periods is extracted, the actual disturbance response value is calculated, and it is compared with the disturbance tension data by difference to obtain the disturbance error data;

[0011] According to the disturbance error data, the change direction of the disturbance error is analyzed, error calculation and direction superposition are performed on the disturbance error, error score values of disturbance errors of each level are calculated, and disturbance evaluation data is obtained;

[0012] According to the disturbance evaluation data, the first control data is iteratively updated to generate second control data, and control instructions are generated according to the second control data and output to the stirring device to obtain a set of control data.

[0013] Further, according to the hierarchical division data and the oxygen concentration data, the oxygen concentration difference value between adjacent levels and the gas diffusion trend value per unit depth are calculated, the disturbance tension values of each level are determined, and disturbance tension data is obtained, including:

[0014] According to the hierarchical division data and the oxygen concentration data, the oxygen concentration values of adjacent levels are extracted, and the oxygen concentration difference value between adjacent levels is calculated to obtain an oxygen concentration difference value sequence;

[0015] According to the concentration difference value sequence and the equal-interval depth of each level, the gas diffusion offset value per unit depth of each level is calculated to obtain diffusion trend data;

[0016] According to the oxygen concentration data, the mean and standard deviation of the oxygen concentration difference value between each level are calculated; according to the mean and the oxygen concentration difference value between adjacent levels, the weighted density of the current level concentration difference value relative to the mean is calculated to obtain a disturbance density term; according to the gas diffusion offset value in the diffusion trend data, the abnormal degree of the current level disturbance in the fluctuation range is calculated to obtain a concentration deviation fluctuation term; according to the oxygen concentration between adjacent levels, the gas diffusion coupling strength between the two levels is calculated to obtain a disturbance energy accumulation term;

[0017] The disturbance density term, the concentration deviation fluctuation term and the disturbance energy accumulation term are weighted and fused to calculate the disturbance tension value of each level to obtain disturbance tension data.

[0018] Further, according to the disturbance tension data and the temperature data, the disturbance ratio of each level is calculated, and the oxygen concentration recovery time of each level in the historical period is combined to calculate the stirring frequency coefficient and the duration value to obtain the first control data, including:

[0019] According to the disturbance tension data, the maximum disturbance tension value and the average disturbance tension value are extracted, and the disturbance tension values of each level are subjected to ratio normalization processing to obtain a disturbance ratio sequence;

[0020] According to the disturbance ratio sequence and the temperature data, the product of the disturbance ratio and the temperature value is calculated to obtain thermal disturbance score data;

[0021] According to the thermal disturbance score data, the difference in thermal disturbance score between adjacent levels is calculated to obtain a water layer thermal disturbance difference sequence;

[0022] According to the water layer thermal disturbance difference sequence, the thermal disturbance difference of each level is normalized with the corresponding thermal disturbance score to obtain a disturbance adjustment sequence;

[0023] According to the disturbance adjustment sequence, the adjustment level of each level is determined to obtain disturbance adjustment factor data.

[0024] Further, according to the disturbance tension data and the temperature data, the disturbance ratio of each level is calculated, and combined with the oxygen concentration recovery time of each level in the historical period, the stirring frequency coefficient and the duration value are calculated to obtain the first control data, which further includes:

[0025] Obtain the oxygen concentration recovery time of each level in the historical period, and combine the disturbance adjustment factor data to calculate the basic frequency value to obtain the basic frequency data;

[0026] According to the basic frequency data, the minimum and maximum basic frequency values are extracted to determine the numerical interval, and the numerical interval is equally divided into multiple frequency level intervals;

[0027] According to the basic frequency data, the frequency level interval where each basic frequency value is located is determined, and the corresponding frequency level label is assigned to obtain the stirring frequency coefficient sequence;

[0028] According to the stirring frequency coefficient sequence and the corresponding disturbance ratio, the inverse product is processed to obtain the stirring duration value;

[0029] According to the stirring duration value, linear mapping processing is performed to limit the maximum and minimum stirring duration values to obtain the duration value, and according to the duration value and the stirring frequency coefficient sequence, the first control data is obtained.

[0030] Further, according to the first control data, the oxygen concentration recovery amplitude in different time periods is extracted, the actual disturbance response value is calculated, and it is compared with the disturbance tension data by difference to obtain disturbance error data, which includes:

[0031] According to the stirring frequency coefficient and the duration value of each level in the first control data, the stirring device is controlled to perform stirring operation on the corresponding level, and the execution cycle number is recorded;

[0032] According to the execution cycle number, oxygen concentration data is collected in a fixed time window before and after the execution cycle, and the difference between the data before and after is calculated to obtain the oxygen concentration recovery amplitude sequence;

[0033] According to the oxygen concentration recovery amplitude sequence, the oxygen concentration recovery value per unit time of each level is standardized to obtain the actual disturbance response value data;

[0034] The actual disturbance response value data and the disturbance tension value of each level in the disturbance tension data are operated by difference to obtain the actual disturbance difference sequence;

[0035] According to the actual disturbance difference sequence, a disturbance error value between the control execution result and the expected disturbance result is determined, and disturbance error data is obtained.

[0036] Further, according to the disturbance error data, the change direction of the disturbance error is analyzed, and error calculation and direction superposition are performed, to calculate error score values of disturbance errors at each level, and obtain disturbance evaluation data, including:

[0037] According to the disturbance error values at each level in the disturbance error data, disturbance error values in a continuous execution period are extracted, and error change values between adjacent execution periods are calculated, to obtain an error change sequence;

[0038] According to the positive and negative signs of the error change values in the error change sequence, the change direction of the current disturbance error is determined, and a disturbance direction sequence is obtained;

[0039] According to the disturbance direction sequence, the number of continuous segments with consistent directions in adjacent time periods is counted, to obtain a direction consistent segment quantity;

[0040] According to the disturbance error value and its mean value, a standardized deviation score item of the disturbance error is calculated; according to the error increment and the error change direction of the disturbance error in the time sequence, a disturbance error growth item is calculated; a second-order change ratio of the disturbance error is calculated, to obtain a disturbance trend growth item; according to the standardized deviation score item, the disturbance error growth item and the disturbance trend growth item, a dynamic offset fluctuation score item is obtained;

[0041] According to the disturbance error change direction and the direction consistent segment quantity, a direction consistency item is calculated; according to the proportion of the error amplitude relative to the overall disturbance system, a disturbance amplitude normalization evaluation item is calculated; according to the direction consistency item and the disturbance amplitude normalization evaluation item, a trend amplitude adjustment score item is obtained;

[0042] The dynamic offset fluctuation score item and the trend amplitude adjustment score item are fused to calculate error score values at each level, to obtain the disturbance evaluation data.

[0043] Further, according to the disturbance evaluation data, the first control data is iteratively updated to generate second control data, and a control instruction is generated according to the second control data and output to the stirring device, to obtain a control data set, including:

[0044] According to the error score values at each level in the disturbance evaluation data, and the stirring frequency coefficients and the duration values at the corresponding levels in the first control data, a control update factor set is obtained;

[0045] According to each element in the control update factor set, the stirring frequency coefficients and the duration values in the first control data are respectively subjected to parameter iterative processing, to obtain an updated control parameter set;

[0046] According to the updated control parameter set, the target frequency value, the target duration value and the control cycle number are encapsulated respectively for each level to obtain second control data;

[0047] According to the second control data, the control parameter groups of each level are packaged into an instruction format to generate a control instruction, and the control instruction is output to the stirring device, and the execution time, the level number and the parameter value are recorded to obtain a control data set.

[0048] In a second aspect, an efficient algal cultivation control system is provided, and the system comprises:

[0049] The water body division module is used for collecting water level data, oxygen concentration data and temperature data, and performing vertical direction segmentation processing according to the water level data to divide the water body into multiple equal-interval depth levels to obtain level division data;

[0050] The disturbance tension module is used for calculating the oxygen concentration difference value between adjacent levels and the gas diffusion trend value per unit depth according to the level division data and the oxygen concentration data, determining the disturbance tension value of each level, and obtaining disturbance tension data;

[0051] The control parameter module is used for calculating the disturbance ratio of each level according to the disturbance tension data and the temperature data, and combining the oxygen concentration recovery time of each level in the historical period to calculate the stirring frequency coefficient and the duration value to obtain first control data;

[0052] The disturbance error module is used for extracting the oxygen concentration recovery amplitude in different time periods according to the first control data, calculating the actual disturbance response value, and comparing the actual disturbance response value with the disturbance tension data to obtain disturbance error data;

[0053] The error evaluation module is used for analyzing the change direction of the disturbance error according to the disturbance error data, and performing error calculation and direction superposition on the disturbance error to calculate the error score value of the disturbance error of each level to obtain disturbance evaluation data;

[0054] The parameter update module is used for iteratively updating the first control data according to the disturbance evaluation data to generate second control data, and outputting the control instruction generated according to the second control data to the stirring device to obtain a control data set.

[0055] The above-mentioned scheme of the present application at least has the following beneficial effects:

[0056] The present application realizes the three-dimensional structure modeling of the water body by equally spacing the water body in the vertical direction and combining the oxygen concentration data of each layer, which is the core basis of dynamic control of algal growth environment. By monitoring the oxygen concentration layer by layer and further calculating the oxygen concentration difference between adjacent layers and the gas diffusion trend per unit depth, a disturbance tension model is formed, which quantifies the disturbance demand of each layer in the water body from the root cause, builds a disturbance control system with higher spatial resolution and stronger control accuracy, and the subsequent stirring control action can be differentiated according to the disturbance characteristics of different layers, so that the dissolved oxygen concentration of each layer can be stably controlled within the set range, thereby ensuring the balanced distribution of algal groups in the entire reactor volume.

[0057] The present application quantifies the abnormal density of oxygen concentration difference, the deviation fluctuation degree and the gas diffusion coupling strength between adjacent layers, respectively reflects the local gradient characteristics, disturbance trend deviation and diffusion coupling strength of the water disturbance, obtains the disturbance tension value of each layer, which not only makes the analysis of the disturbance source more scientific, but also enables the stirring control system to obtain more accurate disturbance magnitude evaluation before execution, so as to develop more fine and dynamic stirring strategy, which can effectively improve the disturbance response ability and regulation effect of the system.

[0058] The present application realizes the adaptive optimization of stirring frequency and time by introducing the historical period oxygen concentration recovery time, specifically by extracting the maximum and average disturbance tension value of each layer, and combining the temperature data to obtain the thermal disturbance score and adjustment level, and then determining the stirring frequency coefficient sequence under different frequency level intervals, and then calculating the stirring duration of each layer to ensure that the area with larger disturbance intensity can obtain higher frequency and shorter duration of local disturbance, while the area with smaller disturbance intensity adopts lower frequency and longer duration of regulation mode, which significantly improves the use efficiency of stirring energy consumption, while ensuring the control effectiveness, reduces the operation load of the stirring motor.

[0059] The present application establishes a dynamic disturbance prediction and adjustment score system through trend analysis of disturbance error, not only depicts the change rate of disturbance error, but also reflects the fluctuation trend of error in continuous period, which is helpful to identify the potential disturbance stability risk, the system can judge whether the current control strategy exists systematic deviation, so as to correct the control strategy in advance, avoid the problem of disturbance out of control caused by continuous error accumulation, and the system can quantify the disturbance fluctuation intensity and direction consistency to provide data basis for subsequent control iteration.

[0060] The application realizes a complete closed loop chain from modeling, execution, feedback to re-optimization of disturbance control by constructing a control update factor set to iteratively optimize the original control parameters to form the updated second control data, can gradually approach the optimal control strategy as the running time increases, reduces the frequency of manual adjustment, and significantly improves the adaptability of disturbance control under different environmental conditions. The control mechanism provides strong technical support for intelligent algae cultivation and has good universality and migration. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 is a flowchart of an algae efficient cultivation control method provided by an embodiment of the application. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0063] As shown in Figure 1 , an algae efficient cultivation control method is provided by an embodiment of the application, which comprises:

[0064] Collecting water level data, oxygen concentration data and temperature data, and performing vertical direction segmentation processing according to the water level data to divide the water body into multiple equal-interval depth levels to obtain level division data;

[0065] According to the level division data and the oxygen concentration data, the oxygen concentration difference between adjacent levels and the gas diffusion trend value per unit depth are calculated, the disturbance tension value of each level is determined, and disturbance tension data is obtained;

[0066] According to the disturbance tension data and the temperature data, the disturbance ratio of each level is calculated, and combined with the oxygen concentration recovery time of each level in the historical period, the stirring frequency coefficient and the duration value are calculated to obtain first control data;

[0067] According to the first control data, the oxygen concentration recovery amplitude in different time periods is extracted, the actual disturbance response value is calculated, and it is compared with the disturbance tension data by difference to obtain disturbance error data;

[0068] According to the disturbance error data, the change direction of the disturbance error is analyzed, and the error calculation and direction superposition are performed to calculate the error score value of the disturbance error of each level to obtain disturbance evaluation data;

[0069] According to the disturbance evaluation data, the first control data is iteratively updated to generate second control data, and control instructions are generated therefrom and output to the stirring device to obtain a regulation and control data set.

[0070] In the embodiment of the present application, water level data, oxygen concentration data and temperature data are collected, the water body is divided into multiple equally spaced depth levels according to the vertical direction segmentation processing of the water level data, level division data is obtained, and a basic model of the three-dimensional structure of the water body is constructed, so that subsequent operations can independently analyze and process different depth regions, effectively avoiding the problem of local control failure caused by the overall processing mode in traditional systems; according to the level division data and the oxygen concentration data, the oxygen concentration difference between adjacent levels and the gas diffusion trend value per unit depth are calculated, the disturbance tension value of each level is determined, and disturbance tension data is obtained, which fully depicts the non-uniformity and directionality of water body disturbance, and can accurately identify the disturbance requirements of each level, providing a scientific basis for subsequent adaptive stirring control; according to the disturbance tension data and the temperature data, the disturbance ratio of each level is calculated, and combined with the oxygen concentration recovery time of each level in the historical period, the stirring frequency coefficient and the duration value are calculated, and the first control data is obtained, which changes the stirring frequency and duration from static parameters to dynamic adjustment, improves the accuracy and energy saving of stirring operation, and adapts to the change requirements of algal metabolism speed under different temperature conditions.

[0071] According to the first control data, the oxygen concentration recovery amplitude in different time periods is extracted, the actual disturbance response value is calculated, and it is compared with the disturbance tension data to obtain disturbance error data, a comparison mechanism between theoretical disturbance requirements and actual disturbance effects is established, and an objective standard for evaluating whether the stirring strategy is effective is provided; according to the disturbance error data, the change direction of the disturbance error is analyzed, and error calculation and direction superposition are performed to calculate the error score value of the disturbance error of each level, and obtain the disturbance evaluation data, which accurately identifies the disturbance imbalance level and the long-term control deviation trend, and provides a direction for subsequent parameter optimization; according to the disturbance evaluation data, the first control data is iteratively updated to generate second control data, and control instructions are generated therefrom and output to the stirring device to obtain a regulation and control data set, so that the control strategy is continuously self-optimized in continuous execution.

[0072] Among them, water level data, oxygen concentration data and temperature data are collected, and the water body is divided into multiple equally spaced depth levels according to the vertical direction segmentation processing of the water level data, to obtain level division data, which specifically includes:

[0073] The parameter data is collected in real time by a plurality of sensing modules arranged in the algae cultivation container. The water level data is continuously detected by an ultrasonic water level sensor arranged at the upper part of the container, the water level sensor is set to measure at a frequency of not less than 30 seconds, and the measurement accuracy is better than ±1mm, so as to ensure the accurate capture of the dynamic water level change, a wave protection structure is arranged at the installation position of the sensor to reduce the measurement error caused by the water surface fluctuation, and the electric signal collected by the sensor is digitized by an A / D conversion module to finally form the water level data of the actual liquid level in the container.

[0074] The oxygen concentration data is collected by relying on a dissolved oxygen optical probe, which is arranged in a multi-point vertical manner, i.e., a plurality of fixed probe supports are arranged on the inner wall of the container along the depth direction, a plurality of dissolved oxygen sensors are installed on the supports, the sensors are arranged at intervals of 20cm-30cm, and the measurement range covers the entire water depth. Each probe collects the instantaneous oxygen concentration value at the corresponding level, and transmits the data to the central processing unit in a wired / wireless manner. In order to ensure data synchronization, the sampling time window of all dissolved oxygen probes is consistent, and the data in each sampling period is marked with the depth position and the time stamp.

[0075] The temperature data is collected in a manner similar to the oxygen concentration, i.e., a plurality of PT100 temperature sensors are arranged in a multi-point manner and installed on independent supports beside the dissolved oxygen sensors. The temperature sensors are connected to the temperature measurement module through a three-wire system, and the temperature sampling frequency is synchronized with the oxygen concentration. The temperature data formed in each sampling period is corresponding to the depth, which is used to describe the temperature distribution of each water layer. Since algae are sensitive to temperature changes, the sensors have an automatic calibration function to ensure that the measurement error is controlled within ±0.2℃.

[0076] After the above data collection is completed, the system analyzes the water level data to obtain the total depth value from the liquid surface to the bottom, then equally divides the depth interval with a set interval (e.g., 20cm), and obtains the total level number according to the depth interval and the division interval. The system marks a number for each level to form a level index sequence, and matches the depth of each sensing point with the nearest level according to the oxygen concentration and temperature sensor data of the multi-point distribution, so as to realize the binding and integration of the oxygen concentration data and the temperature data in the level structure. Finally, the level division data is generated in the unit of "level number+corresponding depth+oxygen concentration and temperature data at the depth".

[0077] In a preferred embodiment of the present application, the oxygen concentration difference between adjacent levels and the gas diffusion trend value per unit depth are calculated according to the level division data and the oxygen concentration data, the disturbance tension value of each level is determined, and the disturbance tension data is obtained, including:

[0078] According to the hierarchical division data and the oxygen concentration data, oxygen concentration values of adjacent hierarchical levels are extracted, and oxygen concentration difference values between the adjacent hierarchical levels are calculated to obtain an oxygen concentration difference value sequence;

[0079] According to the concentration difference value sequence and the equal interval depths of the hierarchical levels, gas diffusion offset values of unit depths of the hierarchical levels are calculated to obtain diffusion trend data;

[0080] According to the oxygen concentration data, a mean value and a standard deviation of the oxygen concentration difference values between the hierarchical levels are calculated, a weighted density of the current hierarchical level concentration difference value relative to the mean value is calculated according to the mean value and the oxygen concentration difference values between the adjacent hierarchical levels to obtain a disturbance density term, and an abnormal degree of the current hierarchical level disturbance in a fluctuation range is calculated according to the gas diffusion offset values in the diffusion trend data to obtain a concentration deviation fluctuation term;

[0081] The disturbance density term, the concentration deviation fluctuation term and the disturbance energy aggregation term are weighted and fused to calculate disturbance tension values of the hierarchical levels to obtain disturbance tension data.

[0082] In the embodiment of the present application, according to the hierarchical division data and the oxygen concentration data, oxygen concentration values of adjacent hierarchical levels are extracted, and oxygen concentration difference values between the adjacent hierarchical levels are calculated to obtain an oxygen concentration difference value sequence, which quantifies the oxygen concentration gradient between the hierarchical levels and provides an important basis for subsequent disturbance intensity judgment; according to the concentration difference value sequence and the equal interval depths of the hierarchical levels, gas diffusion offset values of unit depths of the hierarchical levels are calculated to obtain diffusion trend data, which reflects the concentration gradient change intensity in the unit depth and makes up for the limitations of using only the original difference value for judgment; according to the oxygen concentration data, a mean value and a standard deviation of the oxygen concentration difference values between the hierarchical levels are calculated to determine the central tendency and the fluctuation range of the concentration change in the current water body vertical distribution; according to the mean value and the oxygen concentration difference values between the adjacent hierarchical levels, a weighted density of the current hierarchical level concentration difference value relative to the mean value is calculated to obtain a disturbance density term, which reflects whether there is an oxygen concentration mutation point in a certain hierarchical level and is an important means for judging a local high gradient area; according to the gas diffusion offset values in the diffusion trend data, an abnormal degree of the current hierarchical level disturbance in a fluctuation range is calculated to obtain a concentration deviation fluctuation term, which accurately identifies abnormal diffusion phenomena and discriminates whether the water body is affected by external sudden disturbance; according to the oxygen concentrations between the adjacent hierarchical levels, a gas diffusion coupling strength between two layers is calculated to obtain a disturbance energy aggregation term, which judges whether there is a strong diffusion trend in certain hierarchical levels; the disturbance density term, the concentration deviation fluctuation term and the disturbance energy aggregation term are weighted and fused to calculate disturbance tension values of the hierarchical levels to obtain disturbance tension data, which comprehensively reflects the disturbance driving force strength of the hierarchical levels and serves as a direct basis for subsequent stirring frequency and duration setting.

[0083] The calculation formula of the disturbance tension value is:

[0084] ,

[0085] wherein, is the disturbance tension value of the i-th level, is the index of the level, is the disturbance tension value of the i-th level, is the oxygen concentration difference value of the i-th level, , is the oxygen concentration value of the i-th level, is the oxygen concentration value of the i-th level, is the oxygen concentration value of the i-th level, is the oxygen concentration value of the i-th level, is the equal interval depth between each level, is the mean value of the oxygen concentration difference value of each level, is the standard deviation of the oxygen concentration difference value of each level, is the gas diffusion offset value of the i-th level unit depth, , is the weight coefficient.

[0086] wherein, is the disturbance density term, is the concentration deviation fluctuation term, is the disturbance energy aggregation term. is the weight coefficient, and the sum is 1.

[0087] wherein, in the initial inoculation period (initial proliferation stage of algae), the overall oxygen concentration is relatively low, and the focus is on the rapid identification of weak disturbance. At this stage, algae have not yet formed a stable growth structure, and the oxygen concentration fluctuation is small but sensitive, and a slight concentration anomaly may affect the uniformity of distribution. Therefore, the response capability to abnormal local concentration gradient, i.e., the disturbance density term , needs to be enhanced to quickly identify potential interference points and achieve early intervention. The diffusion fluctuation and coupling strength are not obvious at this stage, and their weights can be appropriately reduced. Therefore take the values of 0.6, 0.3 and 0.1.

[0088] In the rapid growth period (active metabolism stage of algae), the growth rate is high, the algal density rises rapidly, and the dissolved oxygen fluctuates dramatically. At this stage, the photosynthesis and respiration of algae are significantly enhanced, leading to dramatic fluctuations in water oxygen concentration between day and night or between upper and bottom layers, and frequent disturbance areas are prone to occur. At this time, the response to fluctuation characteristics should be focused on, the identification of is enhanced to judge in advance whether the system may appear over-disturbance or disturbance deficiency, the density deviation is secondary, and the energy coupling value also begins to show importance. The weights are moderately increased. Therefore take the values of 0.3, 0.5 and 0.2.

[0089] ​​In the high-density stable period (algal distribution tends to be balanced), the water body system is stable, but there is a risk of long-term accumulation of local concentration. This stage has entered the stable operation period, and single-layer disturbance no longer occurs frequently, but oxygen may be long-term detained or poorly migrated in a specific area, forming an implicit dead angle. At this stage, the modeling of diffusion path and migration trend should be strengthened, that is, the calculation of determines whether there is a potential energy accumulation point or migration imbalance, and timely directional disturbance adjustment is made to improve the overall diffusion efficiency of the system. Therefore the values are 0.2, 0.2 and 0.6.

[0090] In the abnormal state of the system (such as device fluctuation, external disturbance), the data changes abnormally, and the disturbance source needs to be quickly located. In this scenario, the sensitivity to local density and fluctuation term should be enhanced at the same time to quickly locate the problem area at the initial stage of disturbance. The importance of the coupling term is relatively reduced because the system is not on the normal operation path, and the error isolation and real-time response capability should be prioritized. Therefore the values are 0.4, 0.4 and 0.2.

[0091] In a preferred embodiment of the present application, according to the disturbance tension data and temperature data, the disturbance ratio of each level is calculated, and the oxygen concentration recovery time of each level in the historical period is combined to calculate the stirring frequency coefficient and the duration value, to obtain the first control data, including:

[0092] According to the disturbance tension data, the maximum disturbance tension value and the average disturbance tension value are extracted, and the disturbance tension values of each level are subjected to ratio normalization processing to obtain a disturbance ratio sequence;

[0093] According to the disturbance ratio sequence and the temperature data, the product of the disturbance ratio and the temperature value is calculated to obtain thermal disturbance score data;

[0094] According to the thermal disturbance score data, the difference between the thermal disturbance scores of adjacent levels is calculated to obtain a water layer thermal disturbance difference sequence;

[0095] According to the water layer thermal disturbance difference sequence, the thermal disturbance difference of each level and its corresponding thermal disturbance score are subjected to normalization processing to obtain a disturbance adjustment sequence;

[0096] According to the disturbance adjustment sequence, the adjustment level of each level is determined to obtain disturbance adjustment factor data.

[0097] In the embodiment of the present application, according to the disturbance tension data, the maximum disturbance tension value and the average disturbance tension value are extracted, and the disturbance tension values of each level are ratio normalized to obtain a disturbance ratio sequence, which maps the disturbance tension data to a dimensionless ratio, effectively improving the stability and universality of subsequent calculation; according to the disturbance ratio sequence and the temperature data, the product of the disturbance ratio and the temperature value is calculated to obtain the thermal disturbance score data, which introduces temperature into the adjustment logic of the disturbance control parameter, avoiding the problem of over-disturbance caused by misapplication of strong stirring under high temperature conditions; according to the thermal disturbance score data, the difference value of the thermal disturbance score between adjacent levels is calculated to obtain a water layer thermal disturbance difference sequence, which identifies the spatial variation gradient of the thermal disturbance score and finds the possible disturbance transition abnormal area, providing a basis for subsequent disturbance adjustment; according to the water layer thermal disturbance difference sequence, the thermal disturbance difference value of each level and its corresponding thermal disturbance score are normalized to obtain a disturbance adjustment sequence, which integrates the intensity of thermal disturbance and the disturbance intensity into a standardized index, eliminating the interference of different dimensions on the calculation results, thereby laying a unified standard for subsequent hierarchical control of adjustment levels; according to the disturbance adjustment sequence, the adjustment level of each level is determined to obtain disturbance adjustment factor data, realizing the refinement and differentiation of systematic disturbance response, and improving the accuracy of local disturbance adjustment and the coordination of overall control.

[0098] The product of the disturbance ratio and the temperature value is calculated according to the disturbance ratio sequence and the temperature data to obtain the thermal disturbance score data, which specifically includes:

[0099] The system reads the disturbance ratio and the temperature value of each level at the same time, and then performs direct multiplication operation on the two to obtain the thermal disturbance score value. This multiplication calculation process not only considers the disturbance intensity of the water layer, but also introduces temperature as a key factor, thereby comprehensively evaluating the sensitivity of each level to the disturbance operation in the current physical environment. Temperature, as an important influencing factor of oxygen diffusion rate, its high and low will directly affect the physical response of disturbance, so this kind of multiplication processing method has physical rationality and process calculability. Finally, the system integrates the thermal disturbance score values of all levels to form a thermal disturbance score data set, which is used for subsequent difference calculation and adjustment level division.

[0100] The product of the disturbance ratio and the temperature value is calculated according to the disturbance ratio sequence and the temperature data to obtain the thermal disturbance score data, which specifically includes:

[0101] The system calculates the score difference of each pair of adjacent levels to form a thermal disturbance difference sequence, which reflects the degree of thermal disturbance mutation of the water body in the vertical direction. In order to evaluate the thermal disturbance difference and the thermal disturbance score intensity uniformly, the system needs to further construct a disturbance adjustment sequence. First, the absolute value operation is performed on the difference sequence to reflect the change intensity, and then the thermal disturbance difference and its corresponding score value are jointly normalized. The system calculates the maximum value of all thermal disturbance differences and the maximum value of the score value , , , , and the obtained disturbance adjustment value can be used to comprehensively evaluate the adjustment priority or adjustment intensity of each level.

[0102] In a preferred embodiment of the present application, according to the disturbance tension data and the temperature data, the disturbance ratio of each level is calculated, and the stirring frequency coefficient and the duration value are calculated by combining the oxygen concentration recovery time of each level in the historical period to obtain the first control data, which further comprises:

[0103] Obtain the oxygen concentration recovery time of each level in the historical period, and combine the disturbance adjustment factor data to calculate the basic frequency value to obtain the basic frequency data;

[0104] According to the basic frequency data, the minimum and maximum basic frequency values are extracted to determine the numerical interval, and the numerical interval is equally divided into multiple frequency level intervals;

[0105] According to the basic frequency data, determine the frequency level interval where each basic frequency value is located, and assign it a corresponding frequency level label to obtain a stirring frequency coefficient sequence;

[0106] According to the stirring frequency coefficient sequence and its corresponding disturbance ratio, perform inverse proportional product processing to obtain the stirring duration value;

[0107] According to the stirring duration value, perform linear mapping processing to limit the maximum and minimum stirring duration values to obtain the duration value, and according to the duration value and the stirring frequency coefficient sequence, obtain the first control data.

[0108] In the embodiment of the present application, the oxygen concentration recovery time of each level in the historical period is obtained, and the disturbance adjustment factor data is combined to calculate the basic frequency value, obtain the basic frequency data, realize the dynamic assignment mechanism of the stirring frequency, and effectively avoid the inadaptation problem caused by the same frequency control for different levels; according to the basic frequency data, the minimum and maximum basic frequency values are extracted, the numerical interval is determined, and the numerical interval is equally divided into multiple frequency level intervals, the continuous frequency value is discretized by the equal interval interval, which provides a basis for subsequent parameter mapping and frequency grading; according to the basic frequency data, the frequency level interval where each basic frequency value is located is determined, and the corresponding frequency level label is assigned, to obtain the stirring frequency coefficient sequence, the continuous frequency value is converted into a discrete label and mapped into a frequency coefficient, and the stirring intensity that each level should execute is accurately identified, avoiding the execution error caused by too high frequency data accuracy; according to the stirring frequency coefficient sequence and the corresponding disturbance ratio, the inverse product processing is performed to obtain the stirring time value, realizing the dynamic adjustment of the stirring time, and avoiding the water quality fluctuation caused by disturbance redundancy; according to the stirring time value, linear mapping processing is performed to limit the maximum and minimum stirring time value, to obtain the continuous time value, and according to the continuous time value and the stirring frequency coefficient sequence, the first control data is obtained, to ensure the executability and stability of the stirring control strategy.

[0109] The oxygen concentration recovery time of each level in the historical period is obtained, and the disturbance adjustment factor data is combined to calculate the basic frequency value, obtain the basic frequency data, and specifically includes:

[0110] Firstly, the oxygen concentration change of each water layer level in multiple historical control periods needs to be recorded. In each control period, after the stirring device executes the operation, the system records the time that the oxygen concentration in the level rises from the initial state to the target oxygen concentration (such as 6 mg / L) in real time through the oxygen concentration sensor. The time is defined as the oxygen concentration recovery time of the level, and the data needs to be stored together with the execution cycle number and the level number to represent the oxygen response performance of the level in the cycle. Then, the recovery time is combined with the disturbance adjustment factor data obtained in the previous stage. The disturbance adjustment factor is pre-classified according to parameters such as temperature gradient, disturbance intensity, historical change trend, etc. For example, three disturbance levels (low, medium and high) are set, and each level is assigned a corresponding adjustment weight. The system takes the disturbance adjustment factor as a weight parameter to adjust the oxygen concentration recovery time, thereby quantifying the basic frequency demand of the current level. The adjusted result is converted into a numerical basic frequency value, and is summarized as a complete basic frequency data sequence.

[0111] According to the stirring frequency coefficient sequence and the corresponding disturbance ratio, the inverse product processing is performed to obtain the stirring time value, and specifically includes:

[0112] The disturbance ratio represents the relative proportion of the current disturbance intensity of each level in the whole level, and the frequency coefficient represents the stirring frequency that the level should adopt in a certain frequency level interval. In order to realize the high-frequency stirring of the region with strong disturbance in a short time and the low-frequency disturbance of the region with weak disturbance, the system performs inverse product operation on the frequency coefficient and the disturbance ratio, that is, the formula The disturbance ratio performs inverse product operation, that is, the formula is calculated, where is the original stirring time length of the level, is a set proportional constant used to control the magnitude of the whole time length. The system performs the calculation for each level in turn to obtain the preliminary estimated value of the stirring time length.

[0113] The stirring time length value is linearly mapped to limit the maximum and minimum stirring time length values, and the continuous time length value is obtained. According to the continuous time length value and the stirring frequency coefficient sequence, the first control data is obtained, which specifically includes:

[0114] After the stirring time length value is processed by inverse product, abnormal values of maximum or minimum may occur. If these values are directly used, it may interfere with the execution of the control system, and even affect the mechanical life of the equipment. Therefore, the system needs to perform linear mapping processing on the stirring time length value to control all time length values within the preset execution interval, for example, setting the minimum value to 5 seconds and the maximum value to 90 seconds. Specifically, the system inputs each original stirring time length into the linear interval. If it exceeds the upper limit of the interval, it is forcibly assigned to the maximum value. If it is lower than the lower limit, it is assigned to the minimum value. The original value remains unchanged in the remaining range, so that the new stirring continuous time length value is obtained. Then the system combines the continuous time length value with the stirring frequency coefficient obtained, and combines the level number, control cycle number and other identification information to form the first control data.

[0115] In a preferred embodiment of the present application, according to the first control data, the oxygen concentration recovery amplitude in different time periods is extracted, the actual disturbance response value is calculated, and the disturbance error data is obtained by comparing the difference between the actual disturbance response value and the disturbance tension data, which includes:

[0116] According to the stirring frequency coefficient and the continuous time length value of each level in the first control data, the stirring device is controlled to perform stirring operation on the corresponding level, and the execution cycle number is recorded;

[0117] According to the execution cycle number, oxygen concentration data is collected in a fixed time window before and after the execution cycle, and the difference between the data before and after is calculated to obtain the oxygen concentration recovery amplitude sequence;

[0118] According to the oxygen concentration recovery amplitude sequence, the oxygen concentration recovery value per unit time of each level is standardized to obtain the actual disturbance response value data;

[0119] The actual disturbance difference sequence is obtained by difference operation of the disturbance tension value of each level in the actual disturbance response value data and the disturbance tension data.

[0120] The disturbance error data is obtained by determining the disturbance error value between the control execution result and the expected disturbance result according to the actual disturbance difference sequence.

[0121] In the embodiment of the application, according to the stirring frequency coefficient and the duration value of each level in the first control data, the stirring device is controlled to perform stirring operation at the corresponding level, and the execution cycle number is recorded, the accurate disturbance process is started, the stirring device no longer operates at a fixed rhythm, but responds to the disturbance tension characteristics to make differentiated disturbance, thereby providing a prerequisite for subsequent recovery monitoring and effect evaluation; according to the execution cycle number, oxygen concentration data is collected within a fixed time window before and after the execution cycle, and the difference between the data before and after is calculated to obtain an oxygen concentration recovery amplitude sequence, which quantifies the direct effect of the disturbance operation on the change of dissolved oxygen in the water body, and provides a reliable data basis for subsequent disturbance response analysis; according to the oxygen concentration recovery amplitude sequence, the oxygen concentration recovery value per unit time of each level is standardized to obtain the actual disturbance response value data, which eliminates the deviation caused by different stirring durations of different levels, and realizes horizontal comparison of disturbance response levels across levels; the actual disturbance response value data and the disturbance tension value of each level in the disturbance tension data are subjected to difference operation to obtain the actual disturbance difference sequence, which quantifies the difference between the actual disturbance effect and the predicted disturbance demand, and reflects whether the stirring measures meet the expected target; according to the actual disturbance difference sequence, the disturbance error value between the control execution result and the expected disturbance result is determined to obtain the disturbance error data, which reflects the effect of the disturbance operation in a single cycle.

[0122] The disturbance error data is obtained by determining the disturbance error value between the control execution result and the expected disturbance result according to the actual disturbance difference sequence, and specifically includes:

[0123] First, each difference item in the difference sequence is processed to identify its relative deviation degree under the current control background, in order to enhance the adaptability and comparability of the error evaluation, the system constructs an expected disturbance change reference interval based on the disturbance tension value distribution of all levels. For example, the mean and standard deviation of the disturbance tension of all levels can be used to set a reasonable fluctuation range, when a difference item exceeds the range of the mean plus the standard deviation, it can be considered as a high sensitivity disturbance difference, which needs to be paid attention to.

[0124] Subsequently, the difference items are aggregated according to the corresponding level, marked with the corresponding execution cycle number, and sorted according to the execution sequence to form a disturbance error time sequence. On this basis, a dynamic weight factor is further introduced to strengthen the error influence evaluation of the key level or high-frequency disturbance area. For example, for a level with a high disturbance tension value, a higher weight can be given to the error difference value because it has a more direct impact on the overall system stability.

[0125] During the error value calculation process, the system also combines the stirring frequency coefficient and the duration in the current control data to perform causal relationship inference. If the disturbance difference of a certain level is continuously high and the corresponding stirring frequency deviates significantly from the normal value range, it can be preliminarily determined that the disturbance intensity configuration is abnormal. Conversely, if the frequency is moderate but the error is still significant, it may be caused by external environmental changes or system response lag, prompting the need to strengthen the environmental perception module or model structure optimization. Finally, the calculation and logical reasoning results are integrated to generate disturbance error data.

[0126] In a preferred embodiment of the present application, according to the disturbance error data, the change direction of the disturbance error is analyzed, and error calculation and direction superposition are performed to calculate the error score value of each level disturbance error to obtain disturbance evaluation data, including:

[0127] According to the disturbance error values of each level in the disturbance error data, the disturbance error values in the continuous execution cycle are extracted, and the error change values between adjacent execution cycles are calculated to obtain an error change sequence.

[0128] According to the positive and negative signs of the error change values in the error change sequence, the change direction of the current disturbance error is determined to obtain a disturbance direction sequence.

[0129] According to the disturbance direction sequence, the number of continuous segments with consistent directions in adjacent time periods is counted to obtain a consistent direction segment quantity.

[0130] According to the disturbance error value and its mean value, a standardized deviation score item of the disturbance error is calculated. According to the error increment and error change direction of the disturbance error in the time sequence, a disturbance error growth item is calculated. A second-order change ratio of the disturbance error is calculated to obtain a disturbance trend growth item. According to the standardized deviation score item, the disturbance error growth item, and the disturbance trend growth item, a dynamic offset fluctuation score item is obtained.

[0131] According to the disturbance error change direction and the consistent direction segment quantity, a direction consistency item is calculated. According to the proportion of the error amplitude relative to the overall disturbance system, a disturbance amplitude normalization evaluation item is calculated. According to the direction consistency item and the disturbance amplitude normalization evaluation item, a trend amplitude adjustment score item is obtained.

[0132] The dynamic deviation fluctuation score item and the trend amplitude adjustment score item are fused to calculate error score values of each level to obtain disturbance evaluation data.

[0133] In the embodiment of the present application, according to the disturbance error values of each level in the disturbance error data, the disturbance error values in the continuous execution period are extracted, and the error change values between adjacent execution periods are calculated to obtain an error change sequence, thereby realizing time sequence analysis of the disturbance error and providing a quantitative basis for subsequent judgment of whether the disturbance tends to be stable, deteriorates or repeatedly fluctuates; according to the positive and negative signs of the error change values in the error change sequence, the change direction of the current disturbance error is judged to obtain a disturbance direction sequence, and the disturbance error is identified as being continuously increased, decreased or kept stable, thereby distinguishing the type of disturbance change; according to the disturbance direction sequence, the direction consistent segments of adjacent time periods are counted to obtain a direction consistent segment quantity; according to the disturbance error values and the mean value thereof, a standardized deviation score item of the disturbance error is calculated to reflect the relative position of the current disturbance error in the overall historical distribution and provide a basis for subsequent control parameter adjustment; according to the error increment and the error change direction of the disturbance error in the time sequence, a disturbance error growth item is calculated to identify whether the disturbance is intensifying; a second-order change ratio of the disturbance error is calculated to obtain a disturbance trend growth item, thereby reflecting the trend of error intensification; according to the standardized deviation score item, the disturbance error growth item and the disturbance trend growth item, a dynamic deviation fluctuation score item is obtained to depict the deviation amplitude, growth rate and change trend of the current disturbance error, which is a key indicator for dynamically measuring the stability of the disturbance; according to the disturbance error change direction and the direction consistent segment quantity, a direction consistency item is calculated to reveal whether the disturbance trend is continuous and clear and to judge whether the error fluctuation presents a stable growth or stable decay mode; according to the proportion of the error amplitude relative to the overall disturbance system, a disturbance amplitude normalization evaluation item is calculated to reflect the relative strength of the current error relative to the maximum error of the system; according to the direction consistency item and the disturbance amplitude normalization evaluation item, a trend amplitude adjustment score item is obtained to reflect the overall trend of the disturbance control strategy and to provide a more comprehensive adjustment score for the system; the dynamic deviation fluctuation score item and the trend amplitude adjustment score item are fused to calculate error score values of each level to obtain disturbance evaluation data, which comprehensively reflects the actual execution effect, deviation degree and trend of the current level disturbance control and is the core input data for subsequent control parameter adjustment, stirring frequency and time iteration.

[0134] The positive and negative signs of the error change values in the error change sequence are used to judge the change direction of the current disturbance error to obtain a disturbance direction sequence, and the disturbance direction sequence specifically includes:

[0135] Firstly, the perturbation error values of each level in successive multiple execution cycles are extracted and error value sequences are constructed in time sequence. Then, based on the error values of two adjacent cycles, error change values are calculated to obtain error change sequences, which represent the fluctuation amplitude and directionality of errors between adjacent cycles.

[0136] Then the system performs a sign judgment operation on each error change value and constructs a perturbation direction sequence according to the sign thereof. If the error change value is greater than zero, it indicates that the perturbation error value of the current cycle has increased compared with the previous cycle, and the system defines the change direction as positive perturbation, with the perturbation direction value being +1 in the perturbation direction sequence. If the error change value is greater than zero, it indicates that the perturbation error has decreased compared with the previous cycle, and the system defines the change direction as negative perturbation, with the perturbation direction value being -1. If the error change value is equal to zero, i.e., the perturbation error value remains unchanged between two cycles, the system defines the perturbation change of the cycle as no direction change, with the perturbation direction value being 0. Through the processing of all cycle error change values in turn, the system finally forms a complete perturbation direction sequence.

[0137] The calculation formula of the error score value is as follows:

[0138] ,

[0139] wherein, is the error score value of the i-th level, is the index of the level, is the perturbation error value of the i-th level, is the mean value of the perturbation error values of all levels, is the standard deviation of the perturbation error values of all levels, is the error change amount of the i-th level adjacent execution cycles, , is the second-order error change amount of the i-th level adjacent execution cycles, , is the change direction of the error change amount of the i-th level adjacent execution cycles, is the direction consistent segment amount of the change direction of the error change amount of the i-th level, and are weight coefficients. wherein, is the standardized deviation score item, is the perturbation error growth item, is the perturbation trend growth item, is the dynamic offset fluctuation score item.

[0140] ​​​​​​For directional consistency terms, For the normalization evaluation term of disturbance amplitude, This is a score item adjusted for trend amplitude. and These are the weighting coefficients, and their sum is 1.

[0141] In scenarios with drastic environmental fluctuations and frequent external disturbances, immediate response to disturbance errors is crucial. Due to the instability of the external environment (temperature, light, water disturbance), the intensity of the disturbance response significantly impacts algal growth. Therefore, the system needs to quickly identify and correct current errors. In this scenario, greater attention should be paid to the fluctuations, intensity, and abrupt changes in the current periodic disturbance error. Setting it to a larger value increases the sensitivity of the scoring mechanism to immediate error responses, thereby improving real-time control capabilities. Therefore... and The values ​​are 0.8 and 0.2 respectively.

[0142] In the initial operational or training phases, when initial deployments or parameters are not yet stable, the volatility of perturbation data may be significant. Errors in a single period are insufficient to reflect whether the system is on the correct control path. Therefore, a balance must be struck between short-term deviations and trends to ensure the system avoids over-adjustment while capturing effective trends. Setting the median value helps maintain the balance of scores, which in turn helps the system establish a foundation for initial stable operation. Therefore... and The values ​​are 0.5 and 0.5 respectively.

[0143] In scenarios where the system is stabilizing and long-term optimization is pursued, the control system has entered a steady state with small disturbance amplitudes and gradual error changes. The importance of real-time error fluctuations decreases, and the system should focus more on whether the control parameters exhibit a stable trend. Especially in long-term batch algae production, maintaining trend consistency helps reduce system adjustment frequency and energy consumption. In this case, the weight of trend scoring should be appropriately increased to guide the system towards a stable and predictable evolution. Therefore... and The values ​​are 0.3 and 0.7 respectively.

[0144] In a preferred embodiment of the present invention, the first control data is iteratively updated based on the disturbance assessment data to generate the second control data, and a control command is generated based on the second control data and output to the stirring device to obtain a control dataset, including:

[0145] Based on the error score values ​​of each level in the disturbance assessment data and the stirring frequency coefficient and duration value of the corresponding level in the first control data, the control update factor set is obtained;

[0146] According to each element in the control update factor set, the parameter iteration processing is performed on the stirring frequency coefficient and the duration value in the first control data respectively to obtain an updated control parameter set;

[0147] According to the updated control parameter set, the target frequency value, the target duration value and the control cycle number are encapsulated respectively according to each level to obtain second control data;

[0148] According to the second control data, the control parameter groups of each level are packaged into an instruction format to generate a control instruction, and the control instruction is output to the stirring device, and the execution time, the level number and the parameter value are recorded to obtain a regulation and control data set.

[0149] In the embodiment of the present application, according to the error score value of each level in the disturbance evaluation data and the stirring frequency coefficient and the duration value of the corresponding level in the first control data, the control update factor set is obtained, and the level with a larger error response is compensated and adjusted more finely, so that the disturbance accuracy is corrected layer by layer; according to each element in the control update factor set, the parameter iteration processing is performed on the stirring frequency coefficient and the duration value in the first control data respectively to obtain an updated control parameter set, which significantly enhances the adaptive ability and dynamic optimization ability of the system, and ensures the system stability and equipment operation safety; according to the updated control parameter set, the target frequency value, the target duration value and the control cycle number are encapsulated respectively according to each level to obtain second control data, which realizes the structured conversion of the control strategy from the optimization process to the actual control parameter, ensures that each control adjustment has a cycle number identifier, version traceability and maintainability; according to the second control data, the control parameter groups of each level are packaged into an instruction format to generate a control instruction, and the control instruction is output to the stirring device, and the execution time, the level number and the parameter value are recorded to obtain a regulation and control data set, which effectively prevents the system control failure caused by communication abnormalities, equipment response failures and the like.

[0150] According to each element in the control update factor set, the parameter iteration processing is performed on the stirring frequency coefficient and the duration value in the first control data respectively to obtain an updated control parameter set;

[0151] Firstly, based on the hierarchical number, the error score value is matched with the stirring frequency coefficient and the duration value of the corresponding level in the first control data, wherein the first control data is a control parameter set executed in the last period, which records the stirring frequency and duration adopted by each level. After the matching is completed, the system constructs the corresponding control update factor according to the error score value of each level. The control update factor represents the adjustment weight of the disturbance deviation on the current control parameter in the form of a function, and the adjustment trend of the stirring frequency and the change direction of the stirring duration are considered in the calculation process. For example, for the level with a positive error score value, it indicates that the disturbance control of the level is insufficient, and the disturbance intensity needs to be enhanced, so the current frequency coefficient is increased in proportion and the duration is reduced in proportion; otherwise, the amplitude is reduced or extended to avoid the influence of excessive disturbance on the stability of the water body. In order to improve the control accuracy, a nonlinear weight function can be introduced to the update factor, so that the adjustment amplitude of the level with high error score is exponentially enhanced, thereby accelerating the convergence speed of the disturbance control strategy. Finally, the error score value of each level is coupled with the corresponding control parameter to calculate the control update factor set, wherein each update factor includes three key fields of level number, frequency adjustment coefficient and duration adjustment coefficient.

[0152] According to each element in the control update factor set, the stirring frequency coefficient and the duration value in the first control data are iteratively processed, and an updated control parameter set is obtained, which specifically includes:

[0153] The system reads each element in the control update factor set layer by layer, and obtains the stirring frequency coefficient and the duration value in the corresponding first control data, and iteratively updates them. The parameter iteration adopts a linear correction model to dynamically adjust the current control parameter according to the trend of the disturbance error, and the updated stirring frequency coefficient calculation formula can be expressed as: , wherein is the original frequency coefficient, is the current error score value, is the frequency adjustment gain coefficient, which is used to control the adjustment rate; and the update formula of the duration value is: , wherein is the original duration value, is the duration adjustment coefficient. During the parameter update process, boundary constraints need to be applied to the frequency and duration values, such as setting a maximum frequency threshold and a minimum duration threshold, to ensure that the updated parameters are within the physical capability range of the device, avoid the occurrence of unexecutable instructions, and the updated frequency coefficient and duration value will be packaged together with the current system running period number to form an updated control parameter set.

[0154] Embodiments of the present application also provide an efficient algae cultivation control system, which comprises:

[0155] a water body division module configured to collect water level data, oxygen concentration data and temperature data, and perform vertical direction segmentation processing according to the water level data, divide the water body into multiple equal-interval depth levels, and obtain level division data;

[0156] a disturbance tension module configured to calculate an oxygen concentration difference between adjacent levels and a gas diffusion trend value per unit depth according to the level division data and the oxygen concentration data, determine a disturbance tension value of each level, and obtain disturbance tension data;

[0157] a control parameter module configured to calculate a disturbance ratio of each level according to the disturbance tension data and the temperature data, and calculate a stirring frequency coefficient and a duration value in combination with an oxygen concentration recovery time of each level in a historical period, and obtain first control data;

[0158] a disturbance error module configured to extract an oxygen concentration recovery amplitude in different time periods according to the first control data, calculate an actual disturbance response value, and compare the actual disturbance response value with the disturbance tension data to obtain disturbance error data;

[0159] an error evaluation module configured to analyze a change direction of the disturbance error according to the disturbance error data, perform error calculation and direction superposition on the disturbance error, calculate an error score value of a disturbance error of each level, and obtain disturbance evaluation data;

[0160] a parameter updating module configured to iteratively update the first control data according to the disturbance evaluation data, generate second control data, and output a control instruction to the stirring device according to the second control data to obtain a regulation and control data set.

[0161] It should be noted that the system corresponds to the above method, and all implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0162] Embodiments of the present application also provide a computing device, comprising a processor and a memory storing a computer program, wherein the computer program is executed by the processor to perform the above method. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0163] Embodiments of the present application also provide a computer readable storage medium storing instructions, wherein the instructions are executed on a computer to make the computer perform the above method. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0164] The above is the preferred embodiment of the present application, it should be noted that for those skilled in the art, without departing from the principles described in the present application, can also be made several improvements and refinements, these improvements and refinements should also be considered the scope of protection of the present application.

Claims

1. A method for efficient cultivation and control of algae, characterized in that, The method includes: Water level data, oxygen concentration data, and temperature data are collected, and the water body is divided into multiple equally spaced depth levels based on the water level data in a vertical direction to obtain the level division data. Based on the hierarchical division data and oxygen concentration data, the oxygen concentration difference between adjacent hierarchical levels and the gas diffusion trend value per unit depth are calculated to determine the disturbance tension value of each hierarchical level and obtain the disturbance tension data. Based on the disturbance tension data and temperature data, the disturbance ratio of each level is calculated, and combined with the oxygen concentration recovery time of each level in the historical cycle, the stirring frequency coefficient and duration value are calculated to obtain the first control data. Based on the first control data, the oxygen concentration recovery amplitude in different time periods is extracted, the actual disturbance response value is calculated, and the difference is compared with the disturbance tension data to obtain the disturbance error data. Based on the disturbance error data, the direction of change of the disturbance error is analyzed, and the error is calculated and the direction is superimposed. The error score value of each level of disturbance error is calculated to obtain the disturbance assessment data. Based on the disturbance assessment data, the first control data is iteratively updated to generate the second control data, and the control command generated from it is output to the stirring device to obtain the control dataset; Based on the stratification data and oxygen concentration data, the oxygen concentration difference between adjacent strata and the gas diffusion trend per unit depth are calculated to determine the disturbance tension value of each strata, thus obtaining the disturbance tension data, including: Based on the hierarchical division data and oxygen concentration data, the oxygen concentration values ​​of adjacent levels are extracted, and the oxygen concentration difference between adjacent levels is calculated to obtain the oxygen concentration difference sequence. Based on the concentration difference sequence and the equal spacing depth of each level, the gas diffusion offset value per unit depth of each level is calculated to obtain diffusion trend data; Calculate the mean and standard deviation of the oxygen concentration difference between each level based on the oxygen concentration data; calculate the weighted density of the current level's concentration difference relative to the mean based on the mean and the oxygen concentration difference between adjacent levels to obtain the perturbation density term; calculate the degree of anomalousness of the current level's perturbation within the fluctuation range based on the gas diffusion offset value in the diffusion trend data to obtain the concentration deviation fluctuation term; calculate the gas diffusion coupling strength between two layers based on the oxygen concentration between adjacent levels to obtain the perturbation energy accumulation term. The perturbation density term, concentration deviation fluctuation term, and perturbation energy accumulation term are weighted and fused to calculate the perturbation tension value at each level, thus obtaining the perturbation tension data.

2. The method for efficient algae cultivation and control according to claim 1, characterized in that, Based on the disturbance tension data and temperature data, the disturbance ratio of each level is calculated. Combined with the oxygen concentration recovery time of each level within the historical cycle, the stirring frequency coefficient and duration are calculated to obtain the first control data, including: Based on the disturbance tension data, the maximum and average disturbance tension values ​​are extracted, and the ratios of the disturbance tension values ​​at each level are normalized to obtain a disturbance ratio sequence. Based on the perturbation ratio sequence and temperature data, the product of the perturbation ratio and the temperature value is calculated to obtain the thermal perturbation score data; Based on the thermal disturbance score data, the difference in thermal disturbance scores between adjacent layers is calculated to obtain the water layer thermal disturbance difference sequence; Based on the water layer thermal disturbance difference sequence, the thermal disturbance difference of each level and its corresponding thermal disturbance score are normalized to obtain the disturbance adjustment sequence. Based on the disturbance regulation sequence, the regulation level of each level is determined, and the disturbance regulation factor data is obtained.

3. The method for efficient algae cultivation and control according to claim 2, characterized in that, Based on the disturbance tension data and temperature data, the disturbance ratio of each level is calculated. Combined with the oxygen concentration recovery time of each level within the historical cycle, the stirring frequency coefficient and duration are calculated to obtain the first control data, which also includes: Obtain the oxygen concentration recovery time of each level within the historical period, and combine it with the perturbation adjustment factor data to calculate the basic frequency value, thus obtaining the basic frequency data; Based on the fundamental frequency data, extract the minimum and maximum fundamental frequency values, determine the numerical range, and divide the numerical range into multiple frequency level ranges. Based on the basic frequency data, determine the frequency level range of each basic frequency value and assign it a corresponding frequency level label to obtain the stirring frequency coefficient sequence. The stirring duration is obtained by inversely multiplying the stirring frequency coefficient sequence and its corresponding perturbation ratio. Linear mapping is performed based on the stirring duration value to limit its maximum and minimum stirring duration values, thereby obtaining the duration value. Based on the duration value and the stirring frequency coefficient sequence, the first control data is obtained.

4. The method for efficient algae cultivation and control according to claim 3, characterized in that, Based on the first control data, the oxygen concentration recovery amplitude within different time periods is extracted, the actual disturbance response value is calculated, and the difference is compared with the disturbance tension data to obtain the disturbance error data, including: Based on the stirring frequency coefficient and duration value of each level in the first control data, the stirring device is controlled to perform stirring operation at the corresponding level, and the execution cycle number is recorded. Based on the execution cycle number, oxygen concentration data is collected within a fixed time window before and after the execution cycle, and the difference between the data before and after is calculated to obtain the oxygen concentration recovery amplitude sequence. Based on the oxygen concentration recovery amplitude sequence, the oxygen concentration recovery values ​​per unit time at each level are standardized to obtain the actual disturbance response value data; The actual disturbance response value data and the disturbance tension values ​​at each level in the disturbance tension data are calculated by difference to obtain the actual disturbance difference sequence; Based on the actual disturbance difference sequence, the disturbance error value between the control execution result and the expected disturbance result is determined, and the disturbance error data is obtained.

5. The method for efficient algae cultivation and control according to claim 4, characterized in that, Based on the disturbance error data, the direction of change of the disturbance error is analyzed, and the error is calculated and the direction is superimposed. The error score value of each level of disturbance error is calculated to obtain the disturbance assessment data, including: Based on the perturbation error values ​​at each level in the perturbation error data, the perturbation error values ​​within consecutive execution cycles are extracted, and the error change values ​​between adjacent execution cycles are calculated to obtain the error change sequence. Based on the sign of the error change values ​​in the error change sequence, determine the direction of change of the current disturbance error and obtain the disturbance direction sequence; Based on the disturbance direction sequence, the number of consecutive segments with the same direction in adjacent time periods is statistically analyzed to obtain the number of segments with the same direction. Based on the disturbance error value and its mean, calculate the standardized deviation score term of the disturbance error; based on the error increment and error change direction of the disturbance error in adjacent periods of the time series, calculate the disturbance error growth term; calculate the second-order change ratio of the disturbance error to obtain the disturbance trend growth term; based on the standardized deviation score term, the disturbance error growth term, and the disturbance trend growth term, obtain the dynamic offset fluctuation score term. Calculate the direction consistency term based on the direction of change of the disturbance error and the quantity of direction consistency segments; calculate the disturbance amplitude normalization evaluation term based on the proportion of the error amplitude relative to the overall disturbance system; and obtain the trend amplitude adjustment score term based on the direction consistency term and the disturbance amplitude normalization evaluation term. By fusing the dynamic offset fluctuation score item and the trend amplitude adjustment score item, the error score value of each level is calculated to obtain the disturbance assessment data.

6. The method for efficient algae cultivation and control according to claim 5, characterized in that, Based on the disturbance assessment data, the first control data is iteratively updated to generate the second control data, and control commands are generated and output to the stirring device based on the second control data to obtain the control dataset, including: Based on the error score values ​​of each level in the disturbance assessment data and the stirring frequency coefficient and duration value of the corresponding level in the first control data, the control update factor set is obtained; Based on each element in the control update factor set, the stirring frequency coefficient and duration value in the first control data are subjected to parameter iteration processing to obtain the updated control parameter set. Based on the updated control parameter set, the target frequency value, target duration value, and control cycle number are encapsulated for each level to obtain the second control data; Based on the second control data, the control parameters of each level are combined and packaged into an instruction format to generate control instructions, which are then output to the stirring device. The execution time, level number, and parameter value are recorded to obtain the control dataset.

7. A highly efficient algae cultivation control system, characterized in that, The system is used to perform the method as described in any one of claims 1 to 6, the system comprising: The water body segmentation module is used to collect water level data, oxygen concentration data, and temperature data, and to perform vertical segmentation based on the water level data, dividing the water body into multiple equally spaced depth levels to obtain the level segmentation data. The perturbation tension module is used to calculate the oxygen concentration difference between adjacent layers and the gas diffusion trend per unit depth based on the hierarchical division data and oxygen concentration data, determine the perturbation tension value of each layer, and obtain the perturbation tension data. The control parameter module is used to calculate the disturbance ratio of each level based on the disturbance tension data and temperature data, and to calculate the stirring frequency coefficient and duration value by combining the oxygen concentration recovery time of each level in the historical cycle, so as to obtain the first control data. The disturbance error module is used to extract the oxygen concentration recovery amplitude in different time periods based on the first control data, calculate the actual disturbance response value, and compare it with the disturbance tension data to obtain the disturbance error data. The error assessment module is used to analyze the direction of change of the disturbance error based on the disturbance error data, perform error calculation and direction superposition, calculate the error score value of each level of disturbance error, and obtain disturbance assessment data. The parameter update module is used to iteratively update the first control data based on the disturbance assessment data, generate the second control data, and generate control commands based on the second control data to output to the stirring device, thereby obtaining the control dataset.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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