Sludge drying treatment methods, apparatus, equipment and storage media

Abstract

This application provides a sludge drying treatment method, apparatus, equipment, and storage medium, relating to the field of sludge drying treatment technology. It solves the problem of unstable sludge moisture content after drying in related technologies. This solution can accurately predict the sludge moisture content based on sensor data acquisition and SVR model parameter prediction, and adjust the mesh belt interval time accordingly, which helps to improve the control accuracy of sludge moisture content by sludge drying equipment and improve drying efficiency.

Description

Technical Field

[0001] This application relates to the field of sludge drying treatment technology, and in particular to a sludge drying treatment method, apparatus, equipment and storage medium. Background Technology

[0002] Sludge is a byproduct of wastewater treatment. The amount of sludge generated during wastewater treatment is enormous, necessitating its processing. A common method is sludge drying using drying equipment. In this equipment, a conveyor belt transports the sludge into a drying chamber (or drying cavity), and hot, dry air is introduced to dry it. The moisture content of the sludge is controlled by adjusting the interval between conveyor belt cycles during the drying process.

[0003] However, in related technologies, traditional control schemes usually rely on manual experience to regulate the conveyor belt, which is time-consuming and labor-intensive, and cannot adapt to the complex changes in sludge properties and working conditions. It is difficult to accurately adjust the belt interval time, resulting in poor sludge drying effect and sludge moisture content that is too high or too low, which is not conducive to subsequent sludge processing. Summary of the Invention

[0004] This application provides a sludge drying treatment method, apparatus, equipment, and storage medium, which solves the problem of unstable sludge moisture content after drying in related technologies. This solution can accurately predict the sludge moisture content based on sensor data acquisition and SVR model parameter prediction, and adjust the mesh belt interval time accordingly, which helps to improve the control accuracy of sludge moisture content by sludge drying equipment and improve drying efficiency.

[0005] In a first aspect, this application provides a sludge drying treatment method applied to sludge drying equipment. The sludge drying equipment includes a conveyor belt, a temperature sensor, and a status sensor. The temperature sensor is used to detect the current chamber temperature, and the status sensor is used to detect the running time and start / stop times of the conveyor belt. The sludge drying treatment method includes:

[0006] The average temperature of the conveyor belt is determined based on the chamber temperature detected by the temperature sensor.

[0007] Based on the running time and start / stop times detected by the status sensors, the belt interval time and cumulative running time of the transmission belt are determined.

[0008] Based on the pre-built SVR model, the average temperature of the conveyor belt, the interval time of the conveyor belt, and the cumulative running time are used as input parameters of the SVR model. The output parameters generated by the SVR model based on the input parameters are obtained to determine the current sludge moisture content.

[0009] Based on the deviation between the sludge moisture content and the target moisture content, determine the adjustment coefficient for the corresponding conveyor belt interval time, and update the conveyor belt interval time.

[0010] Based on the updated conveyor belt interval time, control commands are generated to control the conveyor belt motor, thereby adjusting the start and stop times of the conveyor belt motor.

[0011] Secondly, this application also provides a sludge drying treatment device, applied to sludge drying equipment. The sludge drying equipment includes a conveyor belt, a temperature sensor, and a status sensor. The temperature sensor is used to detect the current chamber temperature, and the status sensor is used to detect the running time and start / stop times of the conveyor belt. The sludge drying treatment device includes:

[0012] The first data acquisition module is configured to determine the average temperature of the conveyor belt based on the chamber temperature detected by the temperature sensor.

[0013] The second data acquisition module is configured to determine the belt interval time and cumulative running time of the transmission belt based on the running time and start / stop time detected by the status sensor;

[0014] The data prediction module is configured to use a pre-built SVR model, with the average temperature of the conveyor belt, the interval time of the conveyor belt, and the cumulative running time as input parameters of the SVR model. The module then obtains the output parameters generated by the SVR model based on the input parameters to determine the current sludge moisture content.

[0015] The time update module is configured to determine the adjustment coefficient of the corresponding conveyor belt interval time based on the deviation between the sludge moisture content and the target moisture content, and then update the conveyor belt interval time.

[0016] The equipment control module is configured to generate control commands for controlling the conveyor belt motor according to the updated conveyor belt interval time, so as to adjust the start and stop times of the conveyor belt motor.

[0017] Thirdly, this application also provides a sludge drying device, which includes:

[0018] One or more processors;

[0019] Storage device for storing one or more programs.

[0020] When one or more programs are executed by one or more processors, the one or more processors implement the sludge drying treatment method of this application.

[0021] Fourthly, this application also provides a storage medium for storing computer-executable instructions, which, when executed by a processor, are used to perform the sludge drying treatment method of this application.

[0022] This application's solution integrates data acquired by sensors with the constructed SVR model to accurately predict the sludge moisture content, enabling monitoring of the sludge state during the drying process. Based on the sludge moisture content and the target moisture content, the interval time of the conveyor belt is adjusted, thereby improving the control accuracy of the sludge moisture content by the sludge drying equipment, enhancing the drying efficiency of the equipment, and further reducing the equipment's energy consumption. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the steps of a sludge drying treatment method provided in an embodiment of this application;

[0024] Figure 2 This is a schematic diagram illustrating the steps of training an SVR model according to an embodiment of this application;

[0025] Figure 3 A schematic diagram illustrating the output of a trained SVR model provided in an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the sludge drying treatment device provided in one embodiment of this application;

[0027] Figure 5 This is a schematic diagram of the structure of a sludge drying device provided in an embodiment of this application. Detailed Implementation

[0028] The embodiments of this application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of this application and are not intended to limit the scope of this application. Furthermore, it should be noted that, for ease of description, the accompanying drawings only show the parts related to the embodiments of this application, not all structures. Those skilled in the art, after reading this specification, should be able to conceive that any combination of technical features can constitute an optional implementation method, provided that the technical features do not contradict each other.

[0029] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship. In the description of this application, "multiple" means two or more, and "several" means one or more.

[0030] Wastewater treatment processes generate a massive amount of sludge, which requires treatment. A common method is to dry the sludge using sludge drying equipment. In this equipment, a conveyor belt transports the sludge into a drying chamber (or drying cavity), and hot, dry air is introduced to dry it. The moisture content of the sludge is controlled by adjusting the interval between conveyor belt cycles.

[0031] However, traditional control schemes typically rely on manual experience to adjust the conveyor belt. This method cannot accurately determine the sludge moisture content during the treatment process, and the actual drying process involves complex and variable conditions, requiring adjustments to the conveyor belt based on changes in sludge moisture content. Traditional schemes cannot adapt to the complex variations in sludge properties and operating conditions, making it difficult to accurately adjust the conveyor belt interval, resulting in poor sludge drying efficiency. Furthermore, situations where the sludge moisture content is too high or too low are prone to occur, which is detrimental to subsequent sludge processing.

[0032] To address this issue, this application provides a sludge drying method applied to sludge drying equipment. The sludge drying equipment includes a conveyor belt, a temperature sensor, and a status sensor. In the sludge drying equipment, sludge is fed into the drying chamber via a conveyor belt. The temperature sensor can be located on one side of the conveyor belt, the inner wall of the drying chamber, or the side of the heating source. The status sensor is installed on the conveyor belt motor or drive shaft to detect the operating time and start / stop times of the conveyor belt.

[0033] It is conceivable that sludge drying equipment includes a processor and a memory, and the number of processors can be one or more, so as to execute one or more programs stored in the memory through the processors, thereby realizing the sludge drying treatment method of this application. Figure 1 This is a schematic diagram illustrating the steps of a sludge drying treatment method according to an embodiment of this application. The sludge drying treatment method can be executed by a processor, and the specific steps are as follows:

[0034] Step S110: Determine the average temperature of the conveyor belt based on the chamber temperature detected by the temperature sensor.

[0035] A temperature sensor is used to detect the current chamber temperature. The processor can determine the current chamber temperature based on the parameters received from the temperature sensor; this temperature corresponds to the temperature of the drying chamber in the sludge drying equipment. From this, the processor can further determine the average temperature of the conveyor belt. It is conceivable that, if only one temperature sensor is provided, the average temperature of the conveyor belt can be obtained using the detected chamber temperature.

[0036] In one embodiment, multiple temperature sensors are provided, each located at different positions, such as on one side of the conveyor belt, the inner wall of the drying chamber, or the side of the heating source. Correspondingly, each temperature sensor at a different location has a weight, which is related to the degree of influence of the temperature value detected by the sensor at that location on the average temperature of the conveyor belt. After each temperature sensor detects its corresponding chamber temperature, a weighted average is calculated according to the weight of each sensor, and this weighted average is used as the average temperature of the conveyor belt. Therefore, by calculating the temperatures detected by multiple temperature sensors using appropriate weights, the processor can more accurately obtain the average temperature of the conveyor belt, helping the sludge drying equipment to better control the moisture content of the sludge.

[0037] Step S120: Determine the belt interval time and cumulative running time of the transmission belt based on the running time and start / stop time detected by the status sensor.

[0038] It can be inferred that the belt interval time is the time between the switching of the transmission belt from a stopped working state to the start of working state. Therefore, the corresponding belt interval time can be determined by recording the start and stop times of the transmission belt. The cumulative running time is the cumulative duration of the transmission belt in working state. Therefore, after determining the running time of the transmission belt, the corresponding duration can be determined by statistically analyzing the running time of each working session, and then the cumulative running time can be calculated.

[0039] Understandably, the processor can detect the operating status of the conveyor belt motor using a status sensor to determine the conveyor belt's running time and start / stop times. For example, it can record the times when the conveyor belt motor starts and stops to determine the running time and start / stop times. This solution, by using a status sensor to detect the conveyor belt's operation, can achieve more accurate detection of parameters (such as the aforementioned running time and start / stop times), thereby improving the accuracy of the sludge moisture content output by the model.

[0040] It should be noted that in some embodiments, speed sensors, Hall sensors, photoelectric sensors, encoders, etc., can be used as status sensors to detect the operating status of the transmission belt, thereby determining the corresponding running time and start / stop times.

[0041] Step S130: Based on the pre-built SVR model, the average temperature of the conveyor belt, the interval time of the conveyor belt, and the cumulative running time are used as input parameters of the SVR model. The output parameters generated by the SVR model based on the input parameters are obtained to determine the current sludge moisture content.

[0042] Support Vector Regression (SVR) models are used to determine a function that can accurately predict target values. In this application, the average temperature of the conveyor belt, the conveyor belt interval time, and the cumulative running time are used as input parameters for the SVR model, while the change in sludge moisture content is used as the output parameter to construct a corresponding training set for training the SVR model. The trained SVR model is used to output accurate sludge moisture content changes based on the corresponding input parameters. Furthermore, the trained SVR model serves as a pre-built model, allowing the processor to determine the current sludge moisture content by calling the SVR model when the input parameters are determined, such as by using the initial sludge moisture content and the corresponding change in sludge moisture content.

[0043] Understandably, in the process of drying sludge, simply detecting the sludge moisture content through a humidity sensor is insufficient to accurately reflect the overall moisture content change of the sludge. However, a mathematical model that is constructed and trained can better reflect the actual moisture content change of the sludge, and the processor can more accurately predict the current sludge moisture content based on this SVR model.

[0044] Step S140: Based on the deviation between the sludge moisture content and the target moisture content, determine the adjustment coefficient for the corresponding conveyor belt interval time and update the conveyor belt interval time.

[0045] The target moisture content serves as a preset threshold. After determining the sludge moisture content, the processor compares the current sludge moisture content with the target moisture content to determine the deviation between the two. Corresponding to different deviation values, the sludge drying equipment employs different control strategies to control the conveyor belt. For example, different adjustment coefficients are set for the corresponding conveyor belt interval time for different deviation values. The conveyor belt interval time is updated according to these adjustment coefficients, thereby achieving the effect of adjusting the sludge moisture content during the drying process.

[0046] Optionally, in one embodiment, if the deviation between the sludge moisture content and the target moisture content is greater than a preset value, and the sludge moisture content is greater than the target moisture content, an adjustment coefficient is determined as a first coefficient. The product of the first coefficient and the deviation value is used as the time increase ratio, and the conveyor belt interval time is extended according to the time increase ratio. That is, when the sludge moisture content is greater than the target moisture content and the corresponding deviation value is also greater than the preset value, it indicates that the current sludge moisture content is high. In this case, the processor further increases the drying time by extending the conveyor belt interval time to reduce the sludge moisture content.

[0047] If the deviation between the sludge moisture content and the target moisture content is greater than the preset value, but the sludge moisture content is less than the target moisture content, the adjustment coefficient is determined as the second coefficient. The product of the second coefficient and the deviation value is used as the time reduction ratio, and the conveyor belt interval time is shortened according to the time reduction ratio. That is, when the sludge moisture content is less than the target moisture content and the corresponding deviation value is still greater than the preset value, it indicates that the current sludge moisture content is low. In this case, the processor further reduces the drying time by shortening the conveyor belt interval time to avoid the sludge moisture content being too low.

[0048] For example, H p H represents the sludge moisture content determined based on the SVR model. t This indicates the target moisture content, with a set deviation value of 5%. When H... p -H t When the moisture content is >5%, it indicates that the moisture content is too high, and drying needs to be accelerated, which in turn increases the interval time between belt conveyors. The corresponding increase in time is ΔT = k1 × (H p -H t ), where k1 is the first coefficient, which can be set according to actual operating needs. And when H t -H p When the moisture content is >5%, it indicates that the moisture content is too low, thus the interval time of the conveyor belt should be reduced. The corresponding reduction in time is ΔT = k2 × (H t -H p ), where k2 is the second coefficient, which can also be set according to actual operating needs.

[0049] Step S150: Generate control commands for controlling the conveyor belt motor according to the updated conveyor belt interval time, so as to adjust the start and stop times of the conveyor belt motor.

[0050] Furthermore, after determining the corresponding mesh belt interval time, the processor generates a corresponding control instruction according to the updated mesh belt interval time. This control instruction is used to control the mesh belt motor of the conveyor belt to adjust the start and stop time of the mesh belt motor, thereby controlling the conveyor belt. For example, according to the determined mesh belt interval time, after the mesh belt motor stops running, the mesh belt motor is restarted after the timer reaches the mesh belt interval time, so as to carry the sludge out of the drying chamber through the conveyor belt.

[0051] In practical applications, for the same continuous treatment of 30 batches of sludge with a target moisture content of 30%, the comparison of the traditional solution and this solution in terms of effectiveness (final moisture content, drying time, and monthly energy consumption) is shown in the table below:

[0052] Final moisture content Drying time Monthly energy consumption Traditional solution 33±7％ 12h 7000kW·h This plan 30.5±2％ 8.5h 5000kW·h

[0053] As can be seen from the above scheme, this scheme accurately predicts the moisture content of sludge by fusing the data acquired by the sensors with the constructed SVR model, thereby enabling the monitoring of the sludge state during the drying process. Based on the sludge moisture content and the target moisture content, the interval time of the conveyor belt is adjusted, thereby improving the control accuracy of sludge moisture content by the sludge drying equipment, improving the drying efficiency of the equipment, and further reducing the energy consumption of the equipment.

[0054] In some embodiments, the SVR model is constructed using radial basis functions (RBFs) as the kernel function, and the SVR model is used to determine the decision function by minimizing the objective function. Correspondingly, the radial basis function is represented as follows:

[0055]

[0056] Here, γ is the kernel function parameter. The use of the kernel function allows the SVR model to find the optimal regression plane in high-dimensional space, thereby handling complex nonlinear relationships. It should be noted that, in one embodiment, the kernel function used in the SVR model can also be a linear kernel function, a polynomial kernel function, or a sigmoid kernel function.

[0057] It is understood that the aforementioned decision function is a function used to describe the mapping relationship between output parameters and input parameters, where the output parameter is the change in sludge moisture content, and the input parameters include the average temperature of the conveyor belt, the conveyor belt interval time, and the cumulative running time. Furthermore, the objective function is a function related to weights, penalty parameters, a first relaxation variable, and a second relaxation variable. The weights are the weights corresponding to the regression plane (corresponding to the decision function), the first and second relaxation variables represent the allowable error, and the penalty coefficient is used to control the complexity of the model. All the aforementioned parameters (weights, penalty parameters, first relaxation variables, and second relaxation variables) satisfy preset constraints. For example, in one embodiment, the specific form of the objective function is as follows:

[0058]

[0059] Where w is the weight (which can be represented as a vector), C is the penalty parameter, and ξ i As the first slack variable, This is the second slack variable. It's conceivable that constraints can be placed on the values ​​of parameters, or on the relationships between any number of parameters, etc. The corresponding constraints would then be:

[0060]

[0061] Where b is the bias term, used to represent the offset of the regression plane, Φ(x iThe function is a nonlinear mapping function. To address this, this scheme constructs an SVR model based on radial basis functions. This allows the SVR model to find the optimal regression plane in high-dimensional space, thus handling complex nonlinear relationships. Furthermore, by minimizing the objective function, the decision function is constructed, ensuring that the output parameters calculated by the SVR model accurately reflect the actual sludge moisture content.

[0062] Figure 2 This is a schematic diagram illustrating the steps of training an SVR model according to an embodiment of this application. In one embodiment, model training typically includes training and validation steps. For the training and validation of the SVR model, the acquired historical data is classified to divide it into two datasets: a training set and a validation set. The specific steps are as follows:

[0063] Step S210: Divide the historical data into training set and validation set according to a preset ratio. Each set of parameters in the historical data includes the average temperature of the conveyor belt, the interval time of the conveyor belt, the cumulative running time, and the change value of sludge moisture content.

[0064] Step S220: According to the Lagrange multiplier method, select the training set as input data and use the constraints as the condition function to optimize the objective function to generate the decision function.

[0065] Step S230: Validate the parameters of the decision function based on the validation set.

[0066] Understandably, historical data is grouped according to input and output parameters. Each group includes the average conveyor belt temperature, conveyor belt interval time, cumulative runtime, and sludge moisture content change. The input parameters are the average conveyor belt temperature, interval time, and cumulative runtime, while the output parameter is the sludge moisture content change, which can be obtained using a humidity sensor. When dividing the historical data set, a preset ratio can be used, such as using 70% of the historical data as the training set and the remaining 30% as the validation set.

[0067] Furthermore, using the training set data, the optimization objective (i.e., the objective function mentioned above) in the SVR model is solved using the Lagrange multiplier method. This involves selecting the training set as input data and using constraints as conditional functions to optimize the objective function, thereby determining the corresponding decision function. The specific form of this decision function is as follows:

[0068]

[0069] Among them, a i , All are Lagrange multipliers, K(x) ix) is the kernel function, and b is the bias term.

[0070] Accordingly, after training using the training set, the decision function generated by the model needs to be validated using the validation set to verify the accuracy of the generated decision function. Figure 3 This is a schematic diagram of the output of a trained SVR model provided in an embodiment of this application. The horizontal axis represents the predicted samples, and the vertical axis represents the predicted results (including the predicted values ​​output by the model and the true values ​​in the samples). As shown in the figure, the predicted values ​​and the true values ​​in the prediction results are close, and the RSME (root mean square error) of both is 0.36.

[0071] In this regard, the constructed SVR model can accurately reflect the actual moisture content of sludge, thus ensuring that the sludge moisture content obtained based on the SVR model is accurately reflected in the actual drying process. This helps to improve the control accuracy of sludge moisture content by sludge drying equipment and enhance drying efficiency.

[0072] In one embodiment, during the construction of the SVR model, the SVR parameters can also be optimized based on the firefly algorithm. It is conceivable that the SVR parameters are the parameters required for the SVR model to generate the decision function. A set of SVR parameters includes multiple parameter items, such as the penalty parameter, kernel function parameter, first slack variable, second slack variable, weight, and bias term shown in the above embodiment. SVR parameters with different values ​​will affect the complexity of the SVR model and its ability to fit the data. That is, different SVR models will produce different values ​​based on the SVR model.

[0073] As an optimization algorithm, the core idea of ​​the firefly algorithm is to simulate the behavior of fireflies in nature. It has at least the following key points: First, brightness (or attraction). In the algorithm, the brightness of each firefly represents its objective function value. In the optimization problem, this can be the maximum or minimum value of the function, and higher brightness indicates a better solution. Second, attraction and movement. The algorithm's core idea is that fireflies are attracted to brighter fireflies and move towards them, adjusting their position based on the "best" solutions around them. In the optimization process, this corresponds to movement within the parameter space.

[0074] During parameter optimization, based on the parameter values ​​corresponding to multiple randomly generated SVR parameters, the SVR model is iteratively optimized according to a preset position update formula within a preset number of iterations using the firefly algorithm. It can be understood that the parameter values ​​corresponding to the multiple randomly generated SVR parameters can be used to represent fireflies in the initialized firefly swarm; that is, in the parameter space, each specific parameter value of an SVR parameter corresponds to a firefly position. Furthermore, the brightness of the fireflies is represented by a fitness value in the parameter space. This fitness value is used to represent the performance of the SVR model corresponding to this set of SVR parameter values. Optionally, the mean square error between the actual value corresponding to the sludge moisture content in the validation set and the predicted value determined by the SVR model constructed according to the parameter values ​​corresponding to the SVR parameters is calculated. This mean square error is then used as the firefly brightness corresponding to the parameter position of the parameter value in the parameter space corresponding to the firefly algorithm, i.e., as the specific value of the aforementioned fitness value. It can be understood that the smaller the mean square error, the higher the prediction accuracy of the model corresponding to this set of SVR parameters, i.e., the higher the firefly brightness.

[0075] Then, the parameters are optimized according to the preset number of iterations. In each iteration, the fitness value of the SVR model obtained after training is compared with the fitness value of the SVR model corresponding to the specific parameters of the initialized multiple sets of SVR parameters. If the latter is more optimized, the parameter position corresponding to the parameters of the SVR model obtained after training is moved in the parameter space according to the preset position update formula, that is, the position of the firefly is moved, so as to update the parameters of the SVR model, and then the current iteration ends and the next iteration begins.

[0076] For example, in one embodiment, the movement distance in the position update formula depends on the brightness difference and distance. This formula is related to the current position, the target position, the attraction coefficient of the corresponding attractive force, the light absorption coefficient, the random walk coefficient, and the random vector. The specific position update formula is as follows:

[0077]

[0078] in, Let be the position of the firefly in iteration t+1. Let β0 be the position of the firefly in iteration t, γ be the maximum absorption coefficient, and r be the light absorption coefficient. ij Let denoted as the distance between fireflies, and 'a' be the random walk coefficient. The vector is a random vector. Specifically, the maximum absorption coefficient and the light absorption coefficient can be 1, while the random walk coefficient can be 0.2. The random vector is a vector that is uniformly distributed in the range [-0.5, 0.5].

[0079] Therefore, by optimizing the parameters of the SVR model, the sludge drying equipment can optimize the SVR model, thereby further improving the prediction accuracy of the model. This not only improves the accuracy of the model prediction but also helps to enhance the effectiveness of the control strategy for the conveyor belt.

[0080] In one embodiment, the abnormal data can be further processed before being input into the SVR model as input parameters. Specifically, when abnormal data exists, multiple neighboring data points are selected and a weighted average is calculated according to their corresponding weights to replace the abnormal data. That is, the abnormal parameters are processed based on the neighboring data points to eliminate the abnormality. The neighboring data points are the data detected at adjacent sampling times or adjacent sampling points.

[0081] Understandably, adjacent data points can be selected in the time domain. That is, for a sensor exhibiting numerical anomalies, the sampling data from moments before and after the corresponding sampling time (the time of the anomaly) can be used as adjacent data points. Of course, the selected before-and-after sampling times can be the sampling times encompassed by one or more sampling cycles, thus obtaining multiple adjacent data points. Similarly, when multiple sensors of the same type exist, data collected by sensors located spatially adjacent to each other can also be used as adjacent data points.

[0082] Furthermore, different weights are assigned to each adjacent data point. Specifically, the weight of adjacent data points is negatively correlated with the sampling time interval or spatial distance between the adjacent data points and the abnormal data. In the time domain, within the same sensor, the sampling time interval between adjacent data points and the abnormal data can be understood as the time interval between the sampling time of the adjacent data points and the sampling time of the abnormal data. Therefore, the smaller the sampling time interval between adjacent data points and the abnormal data, the greater their weight; conversely, the larger the sampling time interval between adjacent data points and the abnormal data, the smaller their weight.

[0083] Spatially, among different sensors, the spatial distance between adjacent data points and abnormal data can be understood as the spatial interval between the sensor collecting adjacent data points and the sensor collecting abnormal data points. Based on this, the smaller the spatial distance between the locations of the sensors collecting adjacent data points and the sensors collecting abnormal data points, the greater their weight; conversely, the larger the spatial distance between the locations of the sensors collecting adjacent data points and the sensors collecting abnormal data points, the smaller their weight.

[0084] For example, taking temperature as a parameter, if the temperature collected at a certain moment is abnormal data, such as 65℃, and the temperatures at the previous two sampling moments were 45℃ and 46℃, with weights of 0.3 (for the sampling moment of 45℃) and 0.7 (for the sampling moment of 46℃) respectively, then the weighted average obtained after calculating the adjacent data points according to the corresponding weights is 45×0.3+46×0.7=45.7 (℃), and this value (45.7℃) is used to replace the abnormal data.

[0085] In response, this solution eliminates the impact of abnormal data on the final predicted sludge moisture content by processing the abnormal data, which helps ensure the accuracy of the data. This allows the sludge moisture content determined based on the SVR model to better reflect the actual moisture content, facilitating the control of the conveyor belt according to the sludge moisture content and improving the control accuracy of the sludge drying equipment on the sludge moisture content.

[0086] Figure 4 This is a schematic diagram of the structure of a sludge drying treatment device according to an embodiment of this application. This device is used to execute the sludge drying treatment method provided in the above embodiment and has corresponding functional modules and beneficial effects for executing the method. The sludge drying treatment device is applied to sludge drying equipment, which includes a conveyor belt, a temperature sensor, and a status sensor. The temperature sensor is used to detect the current chamber temperature, and the status sensor is used to detect the running time and start / stop times of the conveyor belt. As shown in the figure, the sludge drying treatment device includes a first data acquisition module 401, a second data acquisition module 402, a data prediction module 403, a time update module 404, and an equipment control module 405.

[0087] The first data acquisition module 401 is configured to determine the average temperature of the conveyor belt based on the chamber temperature detected by the temperature sensor.

[0088] The second data acquisition module 402 is configured to determine the belt interval time and cumulative running time of the transmission belt based on the running time and start / stop time detected by the status sensor.

[0089] The data prediction module 403 is configured to use a pre-built SVR model, with the average temperature of the conveyor belt, the interval time of the conveyor belt, and the cumulative running time as input parameters of the SVR model, and to obtain the output parameters generated by the SVR model based on the input parameters in order to determine the current sludge moisture content.

[0090] The time update module 404 is configured to determine the adjustment coefficient of the corresponding mesh belt interval time based on the deviation between the sludge moisture content and the target moisture content, and update the mesh belt interval time.

[0091] The equipment control module 405 is configured to generate control commands for controlling the conveyor belt motor according to the updated conveyor belt interval time, so as to adjust the start and stop times of the conveyor belt motor.

[0092] Based on the above embodiments, the first data acquisition module 401 is specifically configured as follows:

[0093] When multiple temperature sensors are set, the weighted average value of the chamber temperature detected by each temperature sensor is calculated according to the preset weight corresponding to each temperature sensor, and the weighted average value is used as the average temperature of the mesh belt.

[0094] Based on the above embodiments, the time update module 404 is specifically configured as follows:

[0095] If the deviation between the sludge moisture content and the target moisture content is greater than the preset value, and the sludge moisture content is greater than the target moisture content, the adjustment coefficient is determined as the first coefficient, the product of the first coefficient and the deviation value is used as the time increase ratio, and the mesh belt interval time is extended according to the time increase ratio.

[0096] If the deviation between the sludge moisture content and the target moisture content is greater than the preset value, and the sludge moisture content is less than the target moisture content, the adjustment coefficient is determined as the second coefficient. The product of the second coefficient and the deviation value is used as the time reduction ratio, and the mesh belt interval time is shortened according to the time reduction ratio.

[0097] Based on the above embodiments, the SVR model uses the radial basis function as the kernel function. The SVR model is used to determine the decision function by minimizing the objective function. The decision function is used to represent the mapping relationship between the output parameters and the input parameters. The objective function is a function related to the weights, penalty parameters, first slack variables and second slack variables. The weights, penalty parameters, first slack variables and second slack variables in the objective function all satisfy the preset constraints.

[0098] Based on the above embodiments, the sludge drying treatment device further includes a model training module, which is configured as follows:

[0099] Historical data is divided into training and validation sets according to a preset ratio. Each set of parameters in the historical data includes the average temperature of the conveyor belt, the interval time of the conveyor belt, the cumulative running time, and the change value of sludge moisture content.

[0100] Based on the Lagrange multiplier method, the training set is selected as the input data and the constraints are used as the conditional function to optimize the objective function and generate the decision function.

[0101] The decision function is validated based on the validation set.

[0102] Based on the above embodiments, the model training module is further configured as follows:

[0103] Based on the parameter values ​​corresponding to multiple randomly generated SVR parameters, the SVR model is iteratively optimized according to the preset position update formula within a preset number of iterations using the firefly algorithm. The SVR parameters are the parameters required by the SVR model to generate the decision function.

[0104] The mean square error between the actual value of sludge moisture content in the validation set and the predicted value determined by the SVR model constructed according to the parameter values ​​corresponding to the SVR parameters is used as the firefly brightness corresponding to the parameter position of the parameter value in the parameter space corresponding to the firefly algorithm.

[0105] Based on the above embodiments, the sludge drying treatment device further includes a data anomaly processing module, which is configured as follows:

[0106] In the presence of abnormal data, multiple adjacent data points are selected and a weighted average is calculated according to their corresponding weights to replace the abnormal data. The adjacent data points are the data detected at adjacent sampling times or adjacent sampling points. The weights of the adjacent data points are negatively correlated with the sampling time interval or spatial distance between the adjacent data points and the abnormal data.

[0107] It is worth noting that in the embodiments of the above-mentioned device, the modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each module are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0108] Figure 5 This is a schematic diagram of a sludge drying device provided in an embodiment of this application. The device is used to execute the sludge drying treatment method provided in the above embodiment and has corresponding functional modules and beneficial effects for executing the method. As shown in the figure, the sludge drying device includes a processor 501, a memory 502, an input device 503, and an output device 504. The number of processors 501 can be one or more; one processor 501 is shown as an example in the figure. The processor 501, memory 502, input device 503, and output device 504 can be connected via a bus or other means; a bus connection is shown as an example in the figure. The memory 502, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the sludge drying treatment method in the embodiments of this application. The processor 501 executes various corresponding functional applications and data processing by running the software programs, instructions, and modules stored in the memory 502, thereby realizing the above-mentioned sludge drying treatment method.

[0109] The memory 502 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data recorded or created during use. Furthermore, the memory 502 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 502 may further include memory remotely configured relative to the processor 501, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0110] The input device 503 can be used to input corresponding digital or character information to the processor 501, and to generate key signal inputs related to the user settings and function control of the device; the output device 504 can be used to send or display key signal outputs related to the user settings and function control of the device.

[0111] This application also provides a storage medium storing computer-executable instructions, which, when executed by a processor, are used to perform relevant operations in the sludge drying treatment method provided in any embodiment of this application.

[0112] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0113] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0114] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for sludge drying treatment, characterized in that, This invention relates to a sludge drying equipment, which includes a conveyor belt, a temperature sensor, and a status sensor. The temperature sensor detects the current chamber temperature, and the status sensor detects the running time and start / stop times of the conveyor belt. The sludge drying method includes: The average temperature of the conveyor belt is determined based on the chamber temperature detected by the temperature sensor. Based on the running time and start / stop times detected by the status sensor, the belt interval time and cumulative running time of the transmission belt are determined; Based on the pre-built SVR model, the average temperature of the conveyor belt, the interval time of the conveyor belt, and the cumulative running time are used as input parameters of the SVR model to obtain the output parameters generated by the SVR model based on the input parameters, so as to determine the current sludge moisture content. Based on the deviation between the sludge moisture content and the target moisture content, determine the adjustment coefficient corresponding to the mesh belt interval time, and update the mesh belt interval time; Based on the updated conveyor belt interval time, control commands are generated to control the conveyor belt motor, so as to adjust the start and stop times of the conveyor belt motor; The step of determining the average temperature of the conveyor belt based on the chamber temperature detected by the temperature sensor includes: When multiple temperature sensors are provided, a weighted average value is calculated for the chamber temperature detected by each temperature sensor according to a preset weight corresponding to each temperature sensor, and the weighted average value is used as the average temperature of the mesh belt. The step of determining the adjustment coefficient corresponding to the conveyor belt interval time based on the deviation between the sludge moisture content and the target moisture content, and updating the conveyor belt interval time, includes: If the deviation between the sludge moisture content and the target moisture content is greater than a preset value, and the sludge moisture content is greater than the target moisture content, the adjustment coefficient is determined as the first coefficient, the product of the first coefficient and the deviation value is used as the time increase ratio, and the mesh belt interval time is extended according to the time increase ratio. If the deviation between the sludge moisture content and the target moisture content is greater than a preset value, and the sludge moisture content is less than the target moisture content, the adjustment coefficient is determined as the second coefficient, the product of the second coefficient and the deviation value is used as the time reduction ratio, and the conveyor belt interval time is shortened according to the time reduction ratio.

2. The sludge drying treatment method according to claim 1, characterized in that, The SVR model uses a radial basis function as its kernel function. The SVR model is used to determine a decision function by minimizing an objective function. The decision function represents the mapping relationship between the output parameters and the input parameters. The objective function is a function associated with weights, penalty parameters, a first slack variable, and a second slack variable. The weights, penalty parameters, the first slack variable, and the second slack variable in the objective function all satisfy preset constraints.

3. The sludge drying treatment method according to claim 2, characterized in that, The training process of the SVR model includes: Historical data is divided into training set and validation set according to a preset ratio. Each set of parameters in the historical data includes the average temperature of the conveyor belt, the interval time of the conveyor belt, the cumulative running time and the change value of sludge moisture content. According to the Lagrange multiplier method, the training set is selected as input data and the constraints are used as conditional functions to optimize the objective function in order to generate the decision function; Based on the validation set, the decision function is validated for parameters.

4. The sludge drying treatment method according to claim 3, characterized in that, After performing parameter validation on the decision function based on the validation set, the method further includes: Based on the parameter values ​​corresponding to multiple randomly generated SVR parameters, the SVR model is iteratively optimized according to the firefly algorithm within a preset number of iterations and a preset position update formula. The SVR parameters are the parameters required by the SVR model to generate the decision function. The mean square error between the actual value of sludge moisture content in the validation set and the predicted value determined by the SVR model constructed according to the parameter values ​​corresponding to the SVR parameters is used as the firefly brightness corresponding to the parameter position of the parameter value in the parameter space corresponding to the firefly algorithm.

5. The sludge drying treatment method according to claim 2, characterized in that, The sludge drying treatment method further includes: In the presence of abnormal data, multiple adjacent data points are selected and a weighted average is calculated according to their corresponding weights to replace the abnormal data. The adjacent data points are data detected at adjacent sampling times or adjacent sampling points. The weights of the adjacent data points are negatively correlated with the sampling time interval or spatial distance of the adjacent data points relative to the abnormal data.

6. A sludge drying treatment device, characterized in that, This is applied to sludge drying equipment, which includes a conveyor belt, a temperature sensor, and a status sensor. The temperature sensor detects the current chamber temperature, and the status sensor detects the running time and start / stop times of the conveyor belt. The sludge drying treatment device includes: The first data acquisition module is configured to determine the average temperature of the conveyor belt based on the chamber temperature detected by the temperature sensor. The second data acquisition module is configured to determine the belt interval time and cumulative running time of the transmission belt based on the running time and start / stop time detected by the status sensor. The data prediction module is configured to use a pre-built SVR model as input parameters of the SVR model, with the average temperature of the conveyor belt, the interval time of the conveyor belt, and the cumulative running time as input parameters of the SVR model, to obtain the output parameters generated by the SVR model based on the input parameters, so as to determine the current sludge moisture content. The time update module is configured to determine the adjustment coefficient corresponding to the mesh belt interval time based on the deviation between the sludge moisture content and the target moisture content, and update the mesh belt interval time. The equipment control module is configured to generate control commands for controlling the conveyor belt motor according to the updated conveyor belt interval time, so as to adjust the start and stop times of the conveyor belt motor; The first data acquisition module is further configured as follows: When multiple temperature sensors are provided, a weighted average value is calculated for the chamber temperature detected by each temperature sensor according to a preset weight corresponding to each temperature sensor, and the weighted average value is used as the average temperature of the mesh belt. The time update module is also configured to: If the deviation between the sludge moisture content and the target moisture content is greater than a preset value, and the sludge moisture content is greater than the target moisture content, the adjustment coefficient is determined as the first coefficient, the product of the first coefficient and the deviation value is used as the time increase ratio, and the mesh belt interval time is extended according to the time increase ratio. If the deviation between the sludge moisture content and the target moisture content is greater than a preset value, and the sludge moisture content is less than the target moisture content, the adjustment coefficient is determined as the second coefficient, the product of the second coefficient and the deviation value is used as the time reduction ratio, and the conveyor belt interval time is shortened according to the time reduction ratio.

7. A sludge drying device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the sludge drying treatment method as described in any one of claims 1-5.

8. A storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a processor, are used to perform the sludge drying treatment method as described in any one of claims 1-5.

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