Sludge drying equipment remote monitoring system based on Internet of Things

Abstract

The invention provides a sludge drying equipment remote monitoring system based on the Internet of Things, and relates to the technical field of sludge treatment.The system comprises a key node establishing module used for establishing fuzzy key nodes; the real-time monitoring module is used for executing data acquisition to generate sludge key parameters; the verification module is used for performing control parameter optimization through the sludge key parameters, the heat balance state equation and the moisture migration state equation, and performing consistency verification on the adaptive control parameters based on an optimization result; and the early warning module is used for generating remote early warning according to the consistency verification result to update the adaptive control parameters. By means of the method and device, the technical problems that due to the fact that targeted optimization and adjustment cannot be conducted on the sludge drying control parameters in time according to the sludge drying state, the flexibility and accuracy of setting of the drying control parameters are poor, the sludge drying treatment effect is poor, and the resource utilization rate is low can be solved; the technical effect of improving the sludge drying treatment quality can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of sludge treatment, and in particular to a remote monitoring system for sludge drying equipment based on the Internet of Things. Background Art

[0002] Sludge drying equipment is a special equipment used to dry sludge containing a large amount of water. This type of equipment removes the water in the sludge and converts it into solid or semi-solid substances with low moisture content for subsequent resource utilization or disposal.

[0003] At present, the existing sludge drying treatment methods are unable to timely optimize and adjust the sludge drying control parameters according to the sludge drying status, resulting in poor flexibility and accuracy in the setting of drying control parameters, causing technical problems such as poor sludge drying treatment effect and low resource utilization. Summary of the invention

[0004] The purpose of this application is to provide a remote monitoring system for sludge drying equipment based on the Internet of Things, so as to solve the technical problem that the existing sludge drying treatment method cannot timely optimize and adjust the sludge drying control parameters according to the sludge drying status, resulting in poor flexibility and accuracy in setting the drying control parameters, resulting in poor sludge drying treatment effect.

[0005] In view of the above problems, the present application provides a remote monitoring system for sludge drying equipment based on the Internet of Things. The system includes: a data acquisition module for establishing an initial sludge data set and constructing sludge parameters based on the initial sludge data set, wherein the sludge parameters include moisture content parameters, viscosity parameters, and composition parameters; an adaptive parameter output module for matching equipment operation parameters of sludge drying equipment based on the sludge parameters, and establishing adaptive control parameters according to the matching results; a key node establishment module for performing control fitting through the adaptive control parameters and the initial sludge data set, and establishing M fuzzy key nodes based on the fitting results; a real-time monitoring module for configuring a multifunctional sensor group, wherein the multifunctional sensor group includes temperature, humidity, pressure and flow rate sensors, and the multifunctional sensor group performs node data collection of regular cycles and M fuzzy key nodes to generate sludge key parameters; a central analysis module for establishing a thermal equilibrium state equation and a water migration state equation of sludge; a verification module for optimizing control parameters through the sludge key parameters, the thermal equilibrium state equation and the water migration state equation, and performing consistency verification on the adaptive control parameters based on the optimization results; an early warning module for generating a remote early warning according to the consistency verification result, and performing update processing on the adaptive control parameters based on the remote early warning.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: A remote monitoring system for sludge drying equipment based on the Internet of Things is provided, comprising: a data acquisition module, used to establish an initial sludge data set, and to construct sludge parameters based on the initial sludge data set, wherein the sludge parameters include moisture content parameters, viscosity parameters, and component parameters; an adaptive parameter output module, used to match equipment operation parameters of the sludge drying equipment based on the sludge parameters, and to establish adaptive control parameters according to the matching results; a key node establishment module, used to perform control fitting through the adaptive control parameters and the initial sludge data set, and to establish M fuzzy key nodes based on the fitting results; a real-time monitoring module, used to configure a multifunctional sensor group, the multifunctional sensor group includes temperature, humidity, pressure and flow rate sensors, and the multifunctional sensor group is used to perform node data collection of a regular cycle and M fuzzy key nodes to generate sludge key parameters; a central analysis module, used to establish a thermal equilibrium state equation and a water migration state equation of the sludge; a verification module, used to optimize control parameters through the sludge key parameters, the thermal equilibrium state equation and the water migration state equation, and to perform consistency verification on the adaptive control parameters based on the optimization results; an early warning module, used to generate a remote early warning according to the consistency verification result, and to update the adaptive control parameters based on the remote early warning. That is to say, by obtaining the initial sludge parameters, and matching the equipment operation parameters of the sludge drying equipment according to the initial sludge parameters, the adaptive control parameters are obtained; then the adaptive control parameters and the initial sludge data set are subjected to control fitting analysis, and multiple key nodes in the sludge drying process are determined to obtain M fuzzy key nodes; then when the sludge drying equipment is used for sludge drying, a multifunctional sensor group is configured to collect the real-time sludge state in the drying process under the conventional cycle and M fuzzy key nodes, wherein the collected data include temperature, humidity, pressure and flow rate, and the sludge key parameters are obtained; then the drying control parameters are optimized through the sludge key parameters, the thermal equilibrium state equation and the water migration state equation to obtain the optimized control parameters, and the adaptive control parameters are verified for consistency based on the optimized control parameters. If the deviation between the two is greater than the preset deviation threshold, a remote warning signal is generated, and finally the adaptive control parameters are optimized and corrected according to the remote warning signal. The drying control parameter deviation in the sludge drying adaptive control process can be discovered in time, so that the drying control parameters can be optimized and corrected in a targeted manner according to the control parameter deviation, so as to achieve the technical effect of improving the sludge drying quality, reducing energy consumption and emissions, and improving resource utilization.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0009] Figure 1 This is a schematic diagram of the structure of a remote monitoring system for sludge drying equipment based on the Internet of Things for this application; Figure 2 This application discloses a flow chart of updating adaptive control parameters in a remote monitoring system of sludge drying equipment based on the Internet of Things.

[0010] Description of reference numerals: Data acquisition module 11, adaptive parameter output module 12, key node establishment module 13, real-time monitoring module 14, central analysis module 15, verification module 16, early warning module 17. DETAILED DESCRIPTION

[0011] This application provides a remote monitoring system for sludge drying equipment based on the Internet of Things, which solves the existing sludge drying treatment method. Since the sludge drying control parameters cannot be optimized and adjusted in a targeted manner according to the sludge drying state in a timely manner, the flexibility and accuracy of the drying control parameter settings are poor, resulting in poor sludge drying treatment effects and low resource utilization. The drying control parameter deviation in the sludge drying adaptive control process can be discovered in a timely manner, so that the drying control parameters can be optimized and corrected in a targeted manner according to the control parameter deviation, thereby achieving the technical effect of improving the sludge drying treatment quality, reducing energy consumption and emissions, and improving resource utilization.

[0012] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0013] Example Please see attached Figure 1 , the present application provides a remote monitoring system for sludge drying equipment based on the Internet of Things, including: The data acquisition module 11 is used to establish an initial sludge data set and construct sludge parameters based on the initial sludge data set, wherein the sludge parameters include a moisture content parameter, a viscosity parameter, and a component parameter.

[0014] Specifically, the remote monitoring system for sludge drying equipment based on the Internet of Things includes a data acquisition module 11, wherein the data acquisition module 11 is used to establish an initial sludge data set, wherein the initial sludge data can be obtained by sludge component detection in a laboratory or a detection instrument, and then sludge parameters are constructed according to the initial sludge data set, wherein the sludge parameters include moisture content parameters, viscosity parameters, and composition parameters, wherein the moisture content parameter can be used to judge the degree of dryness of the sludge, which directly affects the fluidity, treatment efficiency, and subsequent resource utilization or disposal of the sludge; the viscosity parameter describes the fluidity and internal friction of the sludge, and is related to factors such as the solid content, particle size, and charge distribution in the sludge; the composition parameter reflects the content and proportion of various chemical substances in the sludge, such as organic matter, inorganic matter, heavy metals, etc., which is of great significance for evaluating the environmental risk of sludge and selecting a suitable treatment method. By obtaining the sludge parameters, a basis is provided for the next step of matching the drying control parameters and selecting the appropriate drying control parameters.

[0015] The adaptive parameter output module 12 is used to match the equipment operation parameters of the sludge drying equipment based on the sludge parameters, and establish adaptive control parameters according to the matching results.

[0016] Specifically, first, a parameter matching model is constructed, and the sludge drying equipment information can be used as a constraint condition, wherein the sludge drying equipment information includes equipment model, equipment specifications, etc., and information retrieval is performed based on big data technology to obtain a sample operation parameter set, wherein the sample operation parameter set includes multiple sample sludge parameters and multiple sample equipment operation parameters, wherein the equipment operation parameters include sludge flow rate, temperature parameters, steam parameters, etc., and the sample sludge parameters and the sample equipment operation parameters correspond one to one. Then, an initial parameter matching model is constructed based on a BP neural network, wherein the initial parameter matching model is a neural network model that can be iteratively optimized in machine learning, wherein the input data of the initial parameter matching model is the sludge parameter, and the output data is the equipment operation parameter; then, the sample operation parameter set is used as training data, and the initial parameter matching model is supervised and trained to obtain a parameter matching model that meets the expected convergence constraints. Then, through the parameter matching model, the sludge parameters are analyzed for equipment operation parameter matching of the sludge drying equipment, and the output matching equipment operation parameters are set as adaptive control parameters.

[0017] The key node establishment module 13 is used to perform control fitting through the adaptive control parameters and the initial sludge data set, and establish M fuzzy key nodes based on the fitting results.

[0018] Specifically, the fuzzy logic control algorithm is a control algorithm based on fuzzy set theory, fuzzy linguistic variables and fuzzy logic reasoning, which is used to deal with complex, uncertain and fuzzy system control problems that are difficult to solve with traditional control methods, including fuzzification, building a fuzzy rule base, fuzzy reasoning and defuzzification. Based on the fuzzy logic control algorithm, the adaptive control parameters and the initial sludge data set are controlled and fitted, that is, the sludge drying process is modeled and fitted, and the control parameters are continuously adjusted so that the model can accurately reflect the behavior of the actual drying process. Then, based on the fitting results, fuzzy logic technology is used to establish fuzzy key nodes, where the fuzzy key nodes represent the key states or turning points in the drying process, which are used to guide the formulation and adjustment of control strategies, and M fuzzy key nodes are obtained.

[0019] By performing control fitting analysis on the adaptive control parameters and the initial sludge data set based on the fuzzy logic control algorithm, the accuracy of fuzzy key node setting can be improved, providing strong support for the subsequent optimization and adjustment of sludge drying control parameters.

[0020] The real-time monitoring module 14 is used to configure a multifunctional sensor group, which includes temperature, humidity, pressure and flow rate sensors. The multifunctional sensor group performs node data collection of regular cycles and M fuzzy key nodes to generate sludge key parameters.

[0021] Specifically, first, a multifunctional sensor group is configured, wherein the multifunctional sensor group includes temperature, humidity, pressure and flow rate sensors; then a regular cycle is configured, wherein the regular cycle is a preset time interval for data collection, and technical personnel in this field can set it according to actual conditions, for example: setting the regular cycle to 1 minute, that is, data collection is performed every 1 minute; then, through the multifunctional sensor group, under the regular cycle and the M fuzzy key nodes, data is collected on the real-time sludge status during the drying process, and the data collection results are used as the sludge key parameters.

[0022] The central analysis module 15 is used to establish the thermal equilibrium state equation and the water migration state equation of the sludge.

[0023] The verification module 16 is used to optimize the control parameters through the sludge key parameters, the thermal equilibrium state equation and the water migration state equation, and verify the consistency of the adaptive control parameters based on the optimization results.

[0024] Specifically, first, the thermal equilibrium state equation and the moisture migration state equation of the sludge are established, and the thermal equilibrium state equation and the moisture migration state equation are used to optimize the sludge drying control parameters, such as optimizing the use of thermal energy, and reducing energy waste by precisely controlling the heating temperature and time. For example, the optimal temperature setting calculated by the thermal equilibrium equation can maximize thermal efficiency while keeping the heat-sensitive components of the sludge intact; according to the output of the moisture migration equation, the control parameter optimization can adjust the air flow and stirring speed during the drying process, which helps to speed up or slow down the evaporation and diffusion of water, thereby achieving the ideal drying rate and drying uniformity.

[0025] Then, based on the key parameters of the sludge, the control parameters are optimized using the thermal equilibrium state equation and the moisture migration state equation to obtain the optimized control parameters; and based on the optimized control parameters, the adaptive control parameters are verified for consistency, that is, the deviation of the optimized control parameters and the adaptive control parameters is calculated, and it is determined whether the deviation of the two is greater than the preset deviation threshold. If it is greater, it indicates that the consistency verification has failed; if it is less than or equal to, it indicates that the consistency verification has passed. By configuring a multifunctional sensor group to monitor parameters such as temperature and humidity during the drying process in real time, and optimizing control parameters based on the thermal equilibrium state equation and the moisture migration state equation, it is possible to respond to changes in sludge characteristics or external environment in a timely manner, ensure the maximization of thermal balance and moisture migration efficiency, and thus improve the accuracy and rationality of parameter optimization result settings.

[0026] The warning module 17 is used to generate a remote warning according to the consistency verification result, and perform an update process of the adaptive control parameters based on the remote warning.

[0027] Specifically, if the deviation between the two is greater than the preset deviation threshold, that is, when the consistency verification result is not passed, a remote warning signal is generated, and the adaptive control parameters are optimized and corrected in a targeted manner according to the remote warning signal. The above method can timely discover the deviation of the drying control parameters in the adaptive control process of sludge drying, so that the drying control parameters can be optimized and corrected in a targeted manner according to the control parameter deviation, thereby achieving the technical effect of improving the quality of sludge drying treatment, reducing energy consumption and emissions, and improving resource utilization.

[0028] A remote monitoring system for sludge drying equipment based on the Internet of Things is provided, comprising: a data acquisition module, used to establish an initial sludge data set, and to construct sludge parameters based on the initial sludge data set, wherein the sludge parameters include moisture content parameters, viscosity parameters, and component parameters; an adaptive parameter output module, used to match equipment operation parameters of the sludge drying equipment based on the sludge parameters, and to establish adaptive control parameters according to the matching results; a key node establishment module, used to perform control fitting through the adaptive control parameters and the initial sludge data set, and to establish M fuzzy key nodes based on the fitting results; a real-time monitoring module, used to configure a multifunctional sensor group, the multifunctional sensor group includes temperature, humidity, pressure and flow rate sensors, and the multifunctional sensor group is used to perform node data collection of a regular cycle and M fuzzy key nodes to generate sludge key parameters; a central analysis module, used to establish a thermal equilibrium state equation and a water migration state equation of the sludge; a verification module, used to optimize control parameters through the sludge key parameters, the thermal equilibrium state equation and the water migration state equation, and to perform consistency verification on the adaptive control parameters based on the optimization results; an early warning module, used to generate a remote early warning according to the consistency verification result, and to update the adaptive control parameters based on the remote early warning. The present invention can solve the technical problem that the flexibility and accuracy of the drying control parameter setting are poor due to the inability to timely optimize and adjust the sludge drying control parameters according to the sludge drying state, resulting in poor sludge drying effect. The initial sludge parameters are obtained, and the equipment operation parameters of the sludge drying equipment are matched according to the initial sludge parameters to obtain the adaptive control parameters; then the adaptive control parameters and the initial sludge data set are subjected to control fitting analysis to determine multiple key nodes in the sludge drying process, and M fuzzy key nodes are obtained; then when the sludge drying equipment is used for sludge drying, a multifunctional sensor group is configured to collect the real-time sludge state in the drying process under the conventional cycle and M fuzzy key nodes, wherein the collected data includes temperature, humidity, pressure and flow rate, and the sludge key parameters are obtained; then the drying control parameters are optimized through the sludge key parameters, the thermal equilibrium state equation and the water migration state equation to obtain the optimized control parameters, and the adaptive control parameters are verified for consistency based on the optimized control parameters. If the deviation between the two is greater than the preset deviation threshold, a remote warning signal is generated, and finally the adaptive control parameters are optimized and corrected according to the remote warning signal. The deviation of drying control parameters in the adaptive control process of sludge drying can be discovered in time, so that the drying control parameters can be optimized and corrected in a targeted manner according to the deviation of control parameters, so as to achieve the technical effect of improving the quality of sludge drying treatment, reducing energy consumption and emissions, and improving resource utilization.

[0029] Further, as attached Figure 2 As shown, the real-time monitoring module 14 is also used for: The key parameters of the sludge collected by the fuzzy key nodes are fed back to the edge computing nodes, wherein the edge computing nodes are edge processing nodes integrated in the sludge drying equipment; the edge computing nodes perform data analysis based on the received data, execute control strategy optimization under the corresponding fuzzy key nodes, and update the adaptive control parameters based on the optimization results.

[0030] Specifically, the real-time monitoring module 14 is also used to, first, feed back the key parameters of the sludge collected by the fuzzy key node to the edge computing node, wherein the edge computing node is an edge processing node integrated in the sludge drying equipment, and the edge processing node has the ability to store data and analyze and calculate, and can analyze, process and make decisions on the key parameters of the sludge in real time. This processing method can greatly reduce the delay in data transmission, improve the response speed, and reduce bandwidth requirements.

[0031] Then, in the edge computing node, the received sludge key parameters are analyzed, and the control strategy under the fuzzy key node is optimized according to the data analysis results, that is, the process of equipment adaptive optimization. In the sludge drying equipment, when the edge computing node finds that the equipment state or environmental conditions have changed, it will update the parameters of the adaptive controller based on the results of the control strategy optimization to ensure that the control parameters can make the best decision according to the current situation, thereby improving the performance and stability of the system. The equipment adaptive optimization can build a parameter optimization model based on machine learning, and embed the parameter optimization model into the edge computing node, where the input data of the parameter optimization model is the sludge key parameters, and the output data is the optimized control parameters.

[0032] Furthermore, the central analysis module 15 also includes: The thermal balance verification submodule is used to construct the thermal balance state equation as shown below: ;in, is the input heating power, is the convective heat transfer coefficient, is the dry surface area, is the sludge temperature, is the ambient temperature, is the sludge quality, is the specific heat capacity of sludge, is the additional heat transfer coefficient caused by stirring, is the local temperature field generated by stirring.

[0033] The moisture verification submodule is used to construct the moisture migration state equation as shown below: ;in, is the heat transfer coefficient, is the dry surface area, is the dry air temperature, is the air flow rate, Air flow Index of the effect on water evaporation or heat transfer, is the diffusion coefficient of water in sludge, is the convection term caused by stirring, and are the second-order spatial derivative and the first-order spatial derivative of the moisture content, respectively.

[0034] Specifically, the central analysis module 15 further includes a heat balance verification submodule and a moisture verification submodule. The heat balance verification submodule is used to construct a heat balance state equation. The heat balance state equation is expressed as follows: ; In the thermal equilibrium state equation, is the input heating power, is the convective heat transfer coefficient, is the dry surface area, is the sludge temperature, is the ambient temperature, is the sludge quality, is the specific heat capacity of sludge, is the additional heat transfer coefficient caused by stirring, which is related to the stirring speed and stirring type, The local temperature field generated by stirring is related to the average temperature near the stirrer. By constructing the thermal equilibrium state equation, the optimal temperature setting can be calculated to maximize the thermal efficiency while keeping the heat-sensitive components of the sludge intact.

[0035] The moisture verification submodule is used to construct a moisture migration state equation, and the expression of the moisture migration state equation is: ; In the water migration state equation, is the heat transfer coefficient, is the dry surface area, is the drying air temperature, which refers to the temperature of the hot air used to dry the sludge, is the air flow rate, Air flow The index of the effect on water evaporation or heat transfer, where in the water migration or heat transfer equation of sludge drying, the form is The expression is usually used to express air flow The effect of drying rate here It is an experimentally determined index that reflects the nonlinear response of air flow changes to drying efficiency. Its specific meaning and value depend on the design and operating conditions of the drying system, including air flowability, drying chamber design characteristics, and material properties. is the diffusion coefficient of water in sludge, is the convection term caused by stirring, and are the second-order spatial derivative and the first-order spatial derivative of the moisture content, respectively. By constructing the moisture migration state equation, the air flow rate and stirring speed in the drying process can be adjusted during the optimization of control parameters, which helps to speed up or slow down the evaporation and diffusion of moisture, thereby achieving the ideal drying rate and drying uniformity.

[0036] Furthermore, the verification module 16 also includes: The optimization module is used to perform sludge state evaluation and generate node state evaluation results after receiving the key sludge parameters; the communication module is used to establish digital communication between the optimization module and the thermal balance verification submodule and the moisture verification submodule, and transmit the node state evaluation results to the thermal balance verification submodule and the moisture verification submodule to perform control parameter optimization under maximum thermal efficiency and optimal water molecule migration rate; the output module is used to output the optimization results.

[0037] Specifically, the verification module 16 also includes an optimization module, a communication module and an output module, wherein the optimization module is used to evaluate the sludge state after receiving the sludge key parameters to obtain a node state evaluation result, for example: sludge state evaluation indicators can be set, including sludge moisture content, sludge fluidity, sludge quality, etc.; then the sludge state is evaluated based on the sludge state evaluation indicators to obtain a node state evaluation result.

[0038] The communication module is used to establish digital communication between the optimization module and the thermal balance verification submodule and the moisture verification submodule, and then transmit the node status evaluation results to the thermal balance verification submodule and the moisture verification submodule, and optimize the control parameters with the optimization purpose of maximizing thermal efficiency and optimal water molecule migration rate to obtain optimized control parameters; the output module is used to output the optimized control parameters.

[0039] Furthermore, the system also includes a key point updating module, which is used to: After the adaptive control parameters are updated, the sludge control fitting is performed again, and a fine-tuning coefficient is established based on the fitting result; the fuzzy key nodes are updated by the fine-tuning coefficient, and the real-time monitoring module is called for monitoring and management based on the updated fuzzy key nodes.

[0040] Specifically, the system also includes a key point update module. After the adaptive control parameters are updated, the sludge control fitting is re-performed based on the fuzzy logic control algorithm, and a fine-tuning coefficient is generated according to the fitting result. The fine-tuning coefficient refers to the step size for optimizing the fuzzy key node; that is, the quality of the fitting result is evaluated according to the difference between the output of the fuzzy logic controller after fitting and the actual output, and then the fine-tuning coefficient is calculated based on the fitting result; then the fuzzy key node is optimized and adjusted based on the fine-tuning coefficient, and the real-time monitoring module is called for monitoring and management according to the updated fuzzy key node. By re-performing the sludge control fitting and optimizing and updating the fuzzy key node based on the fitting result, the accuracy and rationality of the fuzzy key node setting can be improved, thereby improving the accuracy of monitoring data acquisition.

[0041] Furthermore, the system also includes an abnormal warning module, which is used to: Based on the fine-tuning coefficient, the fine-tuning adaptation evaluation of the fuzzy key nodes is performed; if the fine-tuning adaptation value cannot meet the preset adaptation threshold, the equipment abnormality is reported and the abnormal characteristics are recorded; according to the reported equipment abnormality, the system self-check is activated, and the shutdown self-check processing of the sludge drying equipment based on the abnormal characteristics is executed.

[0042] Specifically, the system also includes an abnormal warning module. First, the fuzzy key nodes are optimized and adjusted according to the fine-tuning coefficients, and the optimized fuzzy key nodes are adapted and evaluated, that is, whether the adjusted fuzzy key nodes are accurate and reasonable, and a fine-tuning adaptation value is obtained, wherein the fine-tuning adaptation value represents the accuracy of the adjustment of the fuzzy key nodes, wherein the higher the accuracy, the larger the corresponding fine-tuning adaptation value.

[0043] Obtain a preset adaptation threshold, which can be set by a person skilled in the art according to actual conditions; then evaluate the fine-tuning adaptation value based on the preset adaptation threshold, and if the fine-tuning adaptation value is less than the preset adaptation threshold, report an equipment abnormality and record the current equipment abnormality characteristics; then activate the system self-check based on the reported equipment abnormality, and perform shutdown self-check processing of the sludge drying equipment based on abnormal characteristics, such as: after the equipment is shut down, activate the equipment's self-check system, where the self-check system usually includes a series of preset diagnostic programs and tests for detecting whether there are faults or abnormal conditions in various parts of the equipment; then, by analyzing the abnormal characteristics, the self-check system can locate the source of the fault and determine which part or component has failed. By performing shutdown self-check processing on the drying equipment, potential fault threats can be discovered in a timely manner to avoid major operational failures.

[0044] Furthermore, the system also includes an image acquisition module, which is used to: Execute real-time image data acquisition of sludge and establish an acquired image set; perform sludge agglomeration evaluation based on a convolution kernel on the acquired image set and establish an evaluation result; activate an additional processing strategy library through the evaluation result to perform agglomeration processing, wherein the additional processing strategy library includes a stirring strategy, a crushing strategy, a vibration screening strategy, and a dispersant addition strategy.

[0045] Specifically, the system also includes an image acquisition module, which includes an image sensor, and is used to perform real-time image data acquisition on the sludge at a preset time node to obtain a sludge image data set; a sludge agglomeration evaluation model is constructed based on a convolutional neural network, the input data of the sludge agglomeration evaluation model is a sludge image, and the output data is a sludge agglomeration coefficient, wherein the sludge agglomeration coefficient represents the degree of sludge agglomeration, and the larger the sludge agglomeration coefficient, the greater the degree of sludge agglomeration; then, the sludge image data set is evaluated for sludge agglomeration by the sludge agglomeration evaluation model to obtain a sludge agglomeration evaluation coefficient.

[0046] Determine whether the sludge agglomeration evaluation coefficient is greater than a preset agglomeration coefficient threshold. If so, activate an additional processing strategy library, wherein the additional processing strategy library includes stirring strategy, crushing strategy, vibration screening strategy, and dispersant addition strategy; and perform agglomeration treatment based on matching the adaptive processing strategy of the sludge agglomeration evaluation coefficient.

[0047] Furthermore, the system further comprises a recording module, which is used to: Based on remote early warning, equipment processing records are recorded for the sludge drying equipment, and equipment status evaluation results of the sludge drying equipment are generated according to the recording results; the regular cycle is updated according to the equipment status evaluation results, and subsequent monitoring and management of sludge processing by the sludge drying equipment is performed based on the updated regular cycle.

[0048] Specifically, the system also includes a recording module. First, a remote warning is obtained to record the equipment processing of the sludge drying equipment, and the equipment processing frequency is analyzed according to the equipment processing record to generate the equipment processing frequency. The equipment status evaluation result of the sludge drying equipment is obtained according to the equipment processing frequency, wherein the equipment processing frequency is inversely proportional to the equipment status evaluation result, that is, the smaller the equipment processing frequency is, the better the equipment status evaluation result is, that is, the better the equipment operation stability and the sludge drying quality are.

[0049] Then, the routine cycle is optimized according to the equipment status evaluation result, wherein the routine cycle is proportional to the equipment status evaluation result, that is, the better the equipment status evaluation result, the longer the corresponding monitoring node time interval can be, and the updated routine cycle is obtained. Finally, the subsequent sludge drying equipment is used to monitor and manage the sludge. By optimizing the routine cycle based on the equipment status evaluation result, the accuracy of the routine cycle setting can be improved, thereby saving monitoring resource consumption while ensuring the monitoring quality.

[0050] In summary, the remote monitoring system for sludge drying equipment based on the Internet of Things provided by this application has the following technical effects: 1. By obtaining the initial sludge parameters and matching the equipment operation parameters of the sludge drying equipment according to the initial sludge parameters, the adaptive control parameters are obtained; then the adaptive control parameters and the initial sludge data set are controlled and fitted to analyze, multiple key nodes in the sludge drying process are determined, and M fuzzy key nodes are obtained; then when the sludge drying equipment is used for sludge drying, a multifunctional sensor group is configured to collect the real-time sludge state in the drying process under the conventional cycle and M fuzzy key nodes, where the collected data includes temperature, humidity, pressure and flow rate, and the sludge key parameters are obtained; then the drying control parameters are optimized through the sludge key parameters, the thermal equilibrium state equation and the water migration state equation to obtain the optimized control parameters, and the consistency of the adaptive control parameters is verified based on the optimized control parameters. If the deviation between the two is greater than the preset deviation threshold, a remote warning signal is generated, and finally the adaptive control parameters are optimized and corrected according to the remote warning signal. The drying control parameter deviation in the sludge drying adaptive control process can be discovered in time, so that the drying control parameters can be optimized and corrected in a targeted manner according to the control parameter deviation, so as to achieve the technical effect of improving the sludge drying quality, reducing energy consumption and emissions, and improving resource utilization.

[0051] 2. By configuring a multifunctional sensor group to monitor the temperature, humidity and other parameters in the drying process in real time, and optimizing the control parameters based on the thermal equilibrium state equation and the moisture migration state equation, it is possible to respond to changes in sludge characteristics or the external environment in a timely manner, ensure the maximization of thermal balance and moisture migration efficiency, and thus improve the accuracy and rationality of the parameter optimization result setting.

[0052] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. The remote monitoring system of sludge drying equipment based on the Internet of Things is characterized by: The system comprises: A data acquisition module, used to establish an initial sludge data set, and construct sludge parameters based on the initial sludge data set, wherein the sludge parameters include a moisture content parameter, a viscosity parameter, and a composition parameter; An adaptive parameter output module, used for matching equipment operation parameters of the sludge drying equipment based on the sludge parameters, and establishing adaptive control parameters according to the matching results; A key node establishment module, used for performing control fitting through the adaptive control parameters and the initial sludge data set, and establishing M fuzzy key nodes based on the fitting results; A real-time monitoring module is used to configure a multifunctional sensor group, the multifunctional sensor group includes temperature, humidity, pressure and flow rate sensors, and the multifunctional sensor group performs node data collection of a regular cycle and M fuzzy key nodes to generate sludge key parameters; Central analysis module, used to establish the thermal equilibrium state equation and water migration state equation of sludge; A verification module, used to optimize the control parameters through the key parameters of the sludge, the thermal equilibrium state equation and the water migration state equation, and to verify the consistency of the adaptive control parameters based on the optimization results; The early warning module is used to generate a remote early warning according to the consistency verification result, and perform an update process of the adaptive control parameters based on the remote early warning.

2. The system according to claim 1, characterized in that The real-time monitoring module is also used for: Feedback the key parameters of sludge collected by the fuzzy key node to the edge computing node, wherein the edge computing node is an edge processing node integrated in the sludge drying equipment; The edge computing node performs data analysis based on the received data, executes control strategy optimization under the corresponding fuzzy key node, and updates the adaptive control parameters based on the optimization results.

3. The system according to claim 1, characterized in that The central analysis module also includes: The thermal balance verification submodule is used to construct the thermal balance state equation as shown below: ； in, is the input heating power, is the convective heat transfer coefficient, is the dry surface area, is the sludge temperature, is the ambient temperature, is the sludge quality, is the specific heat capacity of sludge, is the additional heat transfer coefficient caused by stirring, The local temperature field generated by stirring; The moisture verification submodule is used to construct the moisture migration state equation as shown below: ； in, is the heat transfer coefficient, is the dry surface area, is the dry air temperature, is the air flow rate, Air flow Index of the effect on water evaporation or heat transfer, is the diffusion coefficient of water in sludge, is the convection term caused by stirring, and are the second-order spatial derivative and the first-order spatial derivative of the moisture content, respectively.

4. The system according to claim 3, characterized in that The verification module also includes: An optimization module, configured to perform sludge state evaluation and generate node state evaluation results after receiving the sludge key parameters; The communication module is used to establish digital communication between the optimization module and the thermal balance verification submodule and the moisture verification submodule, and transmit the node status evaluation results to the thermal balance verification submodule and the moisture verification submodule to perform control parameter optimization under the conditions of maximizing thermal efficiency and optimal water molecule migration rate; Output module, used to output optimization results.

5. The system according to claim 1, wherein: The system also includes a key point updating module, which is used to: After the adaptive control parameters are updated, the sludge control fitting is performed again, and the fine-tuning coefficient is established based on the fitting results; The fuzzy key nodes are updated by using the fine-tuning coefficients, and the real-time monitoring module is called to perform monitoring and management based on the updated fuzzy key nodes.

6. The system according to claim 5, characterized in that The system also includes an abnormal warning module, which is used to: Fine-tuning adaptation evaluation of fuzzy key nodes based on the fine-tuning coefficients; If the fine-tuned adaptation value cannot meet the preset adaptation threshold, the device abnormality is reported and the abnormal characteristics are recorded; The system self-check is activated according to the reported equipment abnormality, and the shutdown self-check processing of the sludge drying equipment based on the abnormal characteristics is performed.

7. The system according to claim 1, characterized in that The system also includes an image acquisition module, which is used to: Perform real-time image data collection of sludge and establish a collection image set; Performing convolution kernel-based sludge agglomeration evaluation on the collected image set and establishing an evaluation result; The additional processing strategy library is activated according to the evaluation result to perform agglomeration processing, wherein the additional processing strategy library includes stirring strategy, crushing strategy, vibration screening strategy, and dispersant addition strategy.

8. The system of claim 1, wherein: The system also includes a recording module, configured to: Record the equipment processing of the sludge drying equipment based on remote early warning, and generate the equipment status evaluation results of the sludge drying equipment according to the record results; The routine cycle is updated by the equipment status evaluation result, and the subsequent sludge drying equipment is monitored and managed for sludge treatment based on the updated routine cycle.

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