An inverting umbrella type aeration device cloud control system and method

Abstract

The application discloses a cloud control system and method for an inverted-umbrella type aeration equipment, comprising the following steps: S1, collecting state parameters of the inverted-umbrella type aeration equipment and sewage quality parameters; S2, converting the collected parameters into digital monitoring signal sequences and performing pretreatment; S3, performing edge control reasoning by using an improved FEDformer network to generate operation control instructions; S4, executing the operation control instructions and collecting operation feedback data, and performing abnormal correction if an abnormality is detected; S5, uploading the operation control instructions, the operation feedback data, current sewage quality parameters and current equipment state parameters to a cloud processing platform to generate a structured monitoring data package; S6, performing control rule reasoning and behavior mode recognition on the structured monitoring data package by using a Mamdani fuzzy reasoning method to generate an optimized control strategy; and S7, generating operation state visual information and pushing the information to a user terminal. The application improves the control capability, energy consumption optimization efficiency and remote operation and maintenance capability of the inverted-umbrella type aeration equipment.

Claims

1. An inverting-umbrella-type aeration device cloud control method, characterized by, It comprises the following steps: S1, collecting the state parameters of the inverted umbrella type aeration equipment and the sewage water quality parameters to generate a simulated monitoring data sequence; S2, converting the simulated monitoring data sequence into a digital monitoring signal sequence, and preprocessing the digital monitoring signal sequence to form a standardized data set; S3, using an improved FEDformer network to perform edge control reasoning on the standardized data set to generate an operation control instruction for the inverted umbrella type aeration equipment; S4, executing the operation control instruction and collecting real-time operation feedback data, and if the operation feedback data is detected to be abnormal, performing abnormal correction processing; The step S4 specifically comprises: S41, issuing the operation control instruction to the inverted umbrella type aeration equipment for execution control; S42, collecting the operation feedback data of the inverted umbrella type aeration equipment in real time through the feedback injection module of the improved FEDformer network, wherein the operation feedback data includes the fan start-stop state, the operation intensity, the power consumption value and the dissolved oxygen concentration; S43, when the operation feedback data has mutation abnormal values, continuous missing values or physical inconsistency, abnormal correction processing is performed: Based on a fixed-length sliding time window, the mean and standard deviation of each type of operation feedback data are dynamically calculated, if there are operation feedback data exceeding the mean ± 3 times the standard deviation range, it is marked as a mutation abnormal value, and the missing points or abnormal values are reconstructed using time series interpolation method; If there are continuous missing values exceeding the set threshold window length, the data is completed using interpolation method; If the physical response of the operation feedback data and the operation control instruction exists logical conflict, it is marked as physical inconsistency, and an alarm signal is generated and reported to the cloud platform; S44, preprocessing the operation feedback data after abnormal correction and concatenating to the end of the standardized data set, and continuing to input to the improved FEDformer network for edge control reasoning; S5, uploading the operation control instruction, the operation feedback data, the current sewage water quality parameters and the current equipment state parameters to the cloud processing platform to generate a structured monitoring data package; S6, in the cloud processing platform, the structured monitoring data package is subjected to control rule reasoning and behavior pattern recognition by the Mamdani fuzzy reasoning method to generate an optimized control strategy for operation control; S7, the optimized control strategy and historical data are classified to generate operation state visual information, and are pushed to the user terminal.

2. The cloud control method of an inverted-umbrella type aeration apparatus according to claim 1, characterized by, The step S1 specifically comprises: S11, deploying dissolved oxygen sensors, ammonia nitrogen sensors, total nitrogen sensors, power monitoring modules, water level sensors, vibration sensors, temperature sensors and fan state sensors on the inverted umbrella type aeration equipment to synchronously collect the state parameters and sewage water quality parameters of the inverted umbrella type aeration equipment; S12, the state parameters include the fan start-stop state, the power consumption value, the shell vibration amplitude and the equipment surface temperature, and the sewage water quality parameters include the dissolved oxygen concentration, the ammonia nitrogen concentration, the total nitrogen concentration and the water level value; S13, removing mutation abnormal values, correcting baseline drift data and completing missing data of the state parameters and sewage water quality parameters, and performing time series alignment based on a unified timestamp to generate a simulated monitoring data sequence.

3. The cloud control method of an inverted-umbrella type aeration apparatus according to claim 1, characterized by, The step S2 specifically comprises: S21, performing an analog-to-digital conversion operation on the analog monitoring data sequence by an analog-to-digital converter to generate a corresponding digital monitoring signal sequence; S22, the preprocessing comprises: performing unit conversion, format regularization and dimension unification on the digital monitoring signal sequence, and performing missing data completion, abnormal value limiting processing and noise filtering; S23, adding a unified time stamp label to the preprocessed digital monitoring signal sequence and performing timing synchronization to form a standardized data set, the standardized data set comprising numerical variables and categorical variables.

4. The inverted-umbrella type aeration device cloud control method according to claim 1, characterized by, The step S3 specifically comprises: S31, the improved FEDformer network comprises a fusion encoding module, a trend-period-disturbance decomposition module, a frequency domain attention block, a control instruction decoding module and a feedback injection module; S32, the fusion encoding module performs feature encoding operation on the standardized data set, specifically: performing linear projection on the numerical variables through a fully connected layer to map them to an embedded space with unified dimensions to generate a numerical embedded tensor, performing one-hot encoding on the categorical variables and performing vector mapping through a pre-set embedded lookup matrix to generate a categorical embedded tensor; based on the position index of each time step, constructing a position encoding tensor through a sine function and a cosine function; splicing the numerical embedded tensor, the categorical embedded tensor and the position encoding tensor in the feature dimension to construct a fusion feature tensor; S33, the trend-period-disturbance decomposition module performs multi-scale decomposition processing on the fusion feature tensor, specifically: performing sequence slicing on the fusion feature tensor based on a fixed-length sliding time window to obtain a plurality of local subsequence blocks; for each local subsequence block, performing convolution operation along the time axis direction by using one-dimensional convolution to extract the smooth change trend component to generate a trend component tensor, identifying the main periodic pattern based on a band-pass filter and a period detection kernel to extract the periodic change structure to generate a period component tensor, and subtracting the corresponding trend component tensor and period component tensor from the local subsequence block respectively to obtain a disturbance residual component tensor; splicing the trend component tensor, the period component tensor and the disturbance residual tensor in the feature dimension to generate an enhanced time series decomposition tensor; S34, the frequency domain attention block performs fast Fourier transform on the enhanced time series decomposition tensor to obtain a frequency domain mapping tensor; extracting the modulus values of each frequency component in the frequency domain mapping tensor and performing descending order sorting according to the modulus values, selecting the first K frequency indexes with the largest modulus values to form a main frequency component set, and extracting the corresponding frequency components from the frequency domain mapping tensor according to the main frequency component set to form a main frequency tensor; performing amplitude normalization processing on the main frequency tensor to generate a frequency domain attention matrix through a Softmax function, performing element-wise weighted combination on the main frequency tensor and the frequency domain attention matrix to generate a frequency domain enhanced tensor, and performing inverse fast Fourier transform operation on the frequency domain enhanced tensor to generate a time series reconstruction tensor; S35, the control instruction decoding module comprises four parallel control prediction branches, a boundary constraint transformer and an instruction encoding unit; The four parallel control prediction branches respectively receive the timing reconstruction tensor, and perform linear compression, nonlinear mapping and activation output in the respective structures, respectively generating fan start-stop control quantity, running intensity control quantity, power optimization coefficient and target dissolved oxygen set value; The boundary constraint transformer performs threshold judgment and binary operation on the fan start-stop control quantity, and when the fan start-stop control quantity exceeds the set start threshold, outputs the fan start-stop control flag as 1: start signal, otherwise outputs the control flag as 0: stop signal; The boundary constraint transformer performs inverse normalization mapping, upper and lower limit amplitude limiting processing and numerical format conversion operation on the running intensity control quantity to generate running intensity control instruction, performs range mapping on the power optimization coefficient to generate power optimization control instruction, and performs linear amplitude limiting operation on the target dissolved oxygen set value to generate dissolved oxygen set control instruction; The instruction encoding unit performs field identification, splicing and communication protocol packaging on the fan start-stop control flag, running intensity control instruction, power optimization control instruction and dissolved oxygen set control instruction to form the running control instruction.

5. The inverted-umbrella type aeration device cloud control method according to claim 1, characterized by, The step S5 specifically includes: S51, calling the communication interface to package the running control instruction, running feedback data, current sewage water quality parameters and current equipment state parameters to form a structured monitoring data packet; S52, encrypting the structured monitoring data packet by using the SM4 symmetric encryption algorithm, and sending it to the cloud processing platform through the wireless communication network; S53, performing field-level analysis and classified storage on the structured monitoring data packet in the cloud processing platform, and constructing a mapping table of the running control instruction field, the running feedback field, the sewage water quality parameter field and the equipment state parameter field.

6. The inverted-umbrella type aeration device cloud control method according to claim 1, characterized by, The step S6 specifically includes: S61, constructing fuzzy input variables, the fuzzy input variables including dissolved oxygen concentration, ammonia nitrogen concentration, fan start-stop state and running intensity; S62, the dissolved oxygen concentration, ammonia nitrogen concentration and running intensity adopt a triangular membership function, and the fan start-stop state adopts a trapezoidal membership function; S63, constructing "low", "medium" and "high" fuzzy language values for the dissolved oxygen concentration and ammonia nitrogen concentration, constructing "on" and "off" fuzzy language values for the fan start-stop state, and constructing "weak", "medium" and "strong" fuzzy language values for the running intensity; S64, setting the fuzzy rules between the input and output based on expert knowledge and operation experience, constructing a fuzzy rule base, the fuzzy rule base including at least 27 fuzzy rules for outputting an optimization control strategy, the optimization control strategy including improving fan running intensity, reducing fan running intensity, maintaining the current running state or triggering an abnormal flag; S65, synthesizing all fuzzy rules by using the minimum-maximum synthesis reasoning method according to the fuzzy input variables and the fuzzy rule base to obtain a fuzzy membership distribution function, the minimum-maximum synthesis reasoning specifically being that the fuzzy premise membership degree is minimum and the output conclusion synthesis is maximum; S66, performing a de-fuzzing operation on the fuzzy membership distribution function by using the barycenter method to obtain and output the corresponding optimization control strategy.

7. The inverted-umbrella type aeration device cloud control method according to claim 1, characterized by, The step S7 specifically includes: S71, the historical data includes: historical operation control instructions, historical operation feedback data, sewage water quality parameters, equipment state parameters and historical optimization control strategies; S72, the operation state visualization information includes operation trend curves, energy consumption analysis charts and alarm records; S73, the operation state visualization information is pushed to the user terminal in real time through an APP client, a webpage or a WeChat public account.

8. An inverting-umbrella-shaped aeration device cloud control system, which executes the inverting-umbrella-shaped aeration device cloud control method according to any one of claims 1 to 7, characterized by, Comprise: a data acquisition module for acquiring state parameters and sewage water quality parameters of the inverted-umbrella-shaped aeration equipment and generating simulation monitoring data sequences; a data preprocessing module for converting the simulation monitoring data sequences into digital monitoring signal sequences and performing preprocessing operations to generate standardized data sets; an edge control reasoning module for performing edge control reasoning to generate operation control instructions; a control execution and feedback module for executing operation control instructions, collecting operation feedback data, detecting abnormalities and performing correction processing; a cloud data processing module for receiving operation control instructions, operation feedback data, current sewage water quality parameters and current equipment state parameters, and constructing structured monitoring data packets; a fuzzy reasoning module for fuzzy rule reasoning and behavior pattern recognition to generate optimization control strategies; a visualization pushing module for generating operation state visualization information and pushing to the user terminal in real time.

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