Sludge state monitoring method and device, electronic equipment and storage medium

Abstract

The invention discloses a sludge state monitoring method and device, electronic equipment and a storage medium. According to the sludge state monitoring method and device, due to the fact that sludge multi-dimensional state parameters are integrated and spatial-temporal characteristic analysis is carried out, dynamic association among the parameters is mined to identify a sludge state change rule and predict a trend; therefore, the technical problem that in an existing sludge monitoring method, due to the fact that a single parameter threshold value is adopted for judgment or simple PID control is adopted, and an inter-parameter dynamic correlation model is not established, the abnormal recognition misjudgment rate is too high can be solved. The technical effects of reducing the sludge state abnormity identification misjudgment rate, improving the sludge monitoring precision and foresight, optimizing the regulation and control efficiency of the sewage treatment equipment and guaranteeing the stable operation of the sewage treatment process are achieved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a sludge condition monitoring method and apparatus, electronic equipment and storage medium. Background Technology

[0002] Sludge monitoring, as a core component of wastewater treatment processes, is widely used in municipal and industrial wastewater treatment. With the development of IoT and AI technologies, existing systems have constructed a comprehensive monitoring system from data acquisition to cloud storage through the collaborative operation of multi-parameter sensor networks, deep learning models, and intelligent control mechanisms.

[0003] Existing sludge monitoring methods directly use single-parameter threshold judgment or simple PID control, without establishing a dynamic correlation model between parameters, which may lead to an excessively high false alarm rate in anomaly identification. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for sludge condition monitoring.

[0005] According to a first aspect of this disclosure, a method for monitoring the condition of sludge is provided, comprising: Obtain multidimensional state parameters of sludge; Spatiotemporal feature analysis is performed on the multidimensional state parameters to identify the changing patterns of sludge state and predict the changing trend. Early warning information is determined based on the predicted trend of change; Control the wastewater treatment equipment based on the aforementioned early warning information.

[0006] Optionally, obtaining the multidimensional state parameters of the sludge includes: Multiple sensors are deployed at different locations within the sludge treatment unit to acquire state parameters reflecting the physicochemical properties of the sludge.

[0007] Optionally, the step of performing spatiotemporal feature analysis on the multidimensional state parameters to identify the changing patterns of the sludge state and predict the changing trend includes: The multidimensional state parameters are preprocessed to improve data quality; The preprocessed data is input into a spatiotemporal feature fusion model, which is used to extract the spatial distribution features of the state parameters and capture their evolution patterns over time.

[0008] Optionally, determining the early warning information based on the predicted change trend includes: The predicted trend of change is compared with preset conditions; Based on the comparison results, warning signals of different levels are generated.

[0009] Optionally, controlling the wastewater treatment equipment based on the early warning information includes: Based on the level of the warning information, a corresponding equipment control strategy is determined and executed. The control strategy includes at least one of adjusting the stirring parameters, regulating the material flow rate, or activating the emergency treatment unit.

[0010] Optionally, the method further includes: The multidimensional state parameters, the predicted change trends, the early warning information, and the equipment control records are encrypted and stored.

[0011] According to a second aspect of this disclosure, a sludge condition monitoring device is provided, comprising: The acquisition unit is also used to obtain multidimensional state parameters of the sludge; The analysis unit is also used to perform spatiotemporal feature analysis on the multidimensional state parameters, identify the changing patterns of sludge state, and predict the changing trends. The determining unit is also used to determine early warning information based on the predicted change trend; The control unit is also used to control the wastewater treatment equipment based on the warning information.

[0012] Optionally, the acquisition unit is further configured to: Multiple sensors are deployed at different locations within the sludge treatment unit to acquire state parameters reflecting the physicochemical properties of the sludge.

[0013] Optionally, the analysis unit is further configured to: The multidimensional state parameters are preprocessed to improve data quality; The preprocessed data is input into a spatiotemporal feature fusion model, which is used to extract the spatial distribution features of the state parameters and capture their evolution patterns over time.

[0014] Optionally, the determining unit is further configured to: The predicted trend of change is compared with preset conditions; Based on the comparison results, warning signals of different levels are generated.

[0015] Optionally, the control unit is further configured to: Based on the level of the warning information, a corresponding equipment control strategy is determined and executed. The control strategy includes at least one of adjusting the stirring parameters, regulating the material flow rate, or activating the emergency treatment unit.

[0016] Optionally, the device further includes: The storage unit is also used to encrypt and store the multidimensional state parameters, the predicted change trend, the early warning information, and the equipment control records.

[0017] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0018] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0019] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0020] The sludge state monitoring method, apparatus, electronic equipment, and storage medium disclosed herein, through integrating multi-dimensional sludge state parameters and conducting spatiotemporal characteristic analysis, mines the dynamic correlation between parameters to identify the sludge state change patterns and predict trends, and then issues early warnings and coordinates the control of wastewater treatment equipment based on the prediction results, can solve the technical problem of excessively high anomaly identification misjudgment rate caused by the use of single parameter threshold judgment or simple PID control and the lack of a dynamic correlation model between parameters in existing sludge monitoring methods. This achieves the technical effect of reducing the misjudgment rate of sludge state anomaly identification, improving the accuracy and foresight of sludge monitoring, thereby optimizing the control efficiency of wastewater treatment equipment and ensuring the stable operation of wastewater treatment processes.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0022] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 This is a schematic flowchart of a sludge condition monitoring method provided in an embodiment of the present disclosure; Figure 2 This is a schematic diagram of the structure of a sludge condition monitoring device provided in an embodiment of the present disclosure; Figure 3 This is a schematic diagram of the structure of a sludge condition monitoring device provided in an embodiment of the present disclosure; Figure 4 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0023] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0024] The following description, with reference to the accompanying drawings, outlines a sludge condition monitoring method, apparatus, electronic device, and storage medium according to embodiments of the present disclosure.

[0025] Figure 1 This is a schematic flowchart of a sludge condition monitoring method provided in an embodiment of the present disclosure.

[0026] like Figure 1 As shown, the method includes the following steps: Step 101: Obtain the multidimensional state parameters of the sludge; Step 101, acquiring multidimensional state parameters of the sludge, is the foundation of the entire monitoring process. Its core purpose is to comprehensively capture key state information of the sludge during treatment, providing reliable data support for subsequent analysis and control. Multidimensional state parameters cover core indicators reflecting the physical properties and operational status of the sludge, mainly including key dimensions such as the sludge's solids content, moisture ratio, settling characteristics, and temperature. To achieve accurate acquisition of these parameters, a multi-parameter sensing module needs to be configured. This module integrates various dedicated sensors, each deployed in different specific areas of the sludge tank according to the characteristics of the detected parameters and the environmental distribution within the sludge tank, forming a comprehensive monitoring network to ensure the capture of the true state of the sludge at different depths and locations.

[0027] Sensors for detecting solid particle content interact with sludge using high-frequency electromagnetic waves, converting the reflection or absorption signals of electromagnetic waves by solid particles into identifiable detection data. Sensors measuring moisture content are based on conductivity principles, acquiring relevant parameters by sensing the impact of moisture in the sludge on the conductivity between electrodes. Sensors capturing settling characteristics utilize optical imaging technology to record the movement trajectory of sludge particles in real time and convert it into parameters reflecting settling velocity. Sensors monitoring temperature sense temperature fluctuations in the sludge environment through thermal elements, generating corresponding temperature parameters. All sensors continuously collect data during operation, outputting the acquired multi-dimensional state parameters in real time through stable transmission methods, ensuring data continuity and timeliness. This lays a solid foundation for subsequent preprocessing and analysis, ensuring the entire monitoring system can comprehensively and accurately grasp the sludge state.

[0028] Step 102: Perform spatiotemporal feature analysis on the multidimensional state parameters to identify the changing patterns of sludge state and predict the changing trend; Step 102, performing spatiotemporal feature analysis on the multidimensional state parameters, is a crucial step in uncovering the essence of sludge state. Its core is to extract effective information from the collected parameters using professional analytical methods, accurately identify the inherent changing patterns of the sludge state, and scientifically predict subsequent trends. After receiving the multidimensional state parameters transmitted in the previous step, the central processing unit first performs necessary preprocessing to ensure data quality, and then inputs the preprocessed parameters into the trained deep learning model to conduct spatiotemporal feature analysis.

[0029] The deep learning model employs an architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The CNNs, through multi-layer convolution and pooling operations, delve into the spatial characteristics of sludge states, clearly revealing the distribution characteristics and correlations of sludge in different regions. The RNNs, on the other hand, focus on the temporal dimension of parameters, capturing the evolutionary patterns of sludge states over time through iterative calculations at multiple time steps, accurately reflecting the changing rhythm and correlation trends of various parameters. To improve the model's analytical accuracy and generalization ability, the deep learning model utilizes transfer learning techniques. It is first pre-trained using a large existing sludge monitoring dataset, and then fine-tuned using actual data from specific application scenarios to ensure the model can adapt to the needs of different wastewater treatment conditions.

[0030] Based on the identification of sludge state change patterns, the model also integrates trend prediction function. It uses a long short-term memory network to predict the sludge state over a future period and presents the prediction results in the form of intuitive curves. This allows operators to clearly grasp the future trend of the sludge state, providing a forward-looking basis for subsequent early warning and control, ensuring that the entire monitoring process is predictive and proactive, and further improving the intelligence and accuracy of wastewater treatment monitoring.

[0031] Step 103: Determine early warning information based on the predicted change trend; Determining early warning information based on predicted trends is a crucial step in enabling proactive intervention in sludge anomalies. The core of this approach is to use an intelligent early warning module to comprehensively analyze spatiotemporal characteristics and predictive trends, generating timely, accurate, and effective warnings. After receiving sludge state change pattern analysis results and future trend prediction data from the central processing unit, the intelligent early warning module performs a dual assessment based on preset reasonable threshold ranges. This considers both whether the current sludge state is within the normal range and, importantly, whether the predicted trend risks deviating from the abnormal range.

[0032] The built-in trend prediction submodule is constructed based on a long short-term memory network, enabling it to deeply mine the correlation logic between historical and current data and accurately output the sludge state evolution curve for a future period, providing a forward-looking basis for early warning judgment. Early warning information is divided into three levels according to the degree of anomaly and risk level: Level 1 corresponds to minor anomalies, Level 2 to moderate anomalies, and Level 3 to severe anomalies. Different levels of early warning information intuitively reflect the risk level of the sludge state. The intelligent early warning module is equipped with a visual interface that displays the historical change curves, predicted trend curves, and early warning level indicators of the current sludge state data in real time. The interface supports touchscreen operation, and operators can customize and adjust the early warning thresholds of various parameters according to actual working conditions, improving the flexibility and adaptability of the early warning system. When the system determines that the current sludge state is abnormal or that the predicted trend will trigger an anomaly, it will immediately generate the corresponding level of early warning information, issue a warning signal on-site through an audible and visual alarm device, and simultaneously send a push notification to the operator's mobile terminal, ensuring that relevant personnel are informed of the risk immediately.

[0033] This step, by combining real-time monitoring with trend prediction, achieves a shift from passive response to proactive early warning, buying time for the timely initiation of subsequent control measures, effectively reducing the impact of sludge treatment anomalies, and ensuring the stable operation of the wastewater treatment process.

[0034] Step 104: Control the sewage treatment equipment according to the warning information.

[0035] Controlling wastewater treatment equipment based on early warning information is the core execution link for timely correction of abnormal sludge conditions. Its core objective is to use the precise scheduling of the intelligent control module to initiate appropriate control measures based on the level and type of early warning information, quickly mitigating abnormal risks in the sludge treatment process and ensuring the stable and efficient operation of the wastewater treatment process. The intelligent control module, as the core execution unit, establishes a stable connection with various wastewater treatment equipment via industrial Ethernet, ensuring rapid transmission and reliable response of control signals.

[0036] This module has a built-in priority scheduling unit that can dynamically adjust the response order of equipment based on the level of the warning information. When multiple levels of warning information are triggered simultaneously, the control measures corresponding to the highest-level warning are executed first, in descending order of warning level, to avoid resource conflicts and response delays. For different levels of warning information, the intelligent control module outputs corresponding digital commands. All control signals are sent to the controllers of the corresponding equipment using industrial standard protocol formats to ensure signal compatibility and execution accuracy. When a Level 1 warning information is received, the intelligent control module sends a command to the controller of the sludge mixing device to optimize the mixing uniformity of the sludge by adjusting the speed of the mixing device, improving the sludge condition from the source. When a Level 2 warning information is received, a command is sent to the controller of the sludge return pump to optimize the residence time of the sludge in the treatment system by adjusting the flow rate of the return pump, ensuring treatment effectiveness. When a Level 3 warning information is received, the intelligent control module simultaneously sends commands to the controllers of the sludge discharge valve and the backup storage tank, quickly closing the discharge valve to prevent sludge overflow and environmental pollution, and simultaneously activating the backup storage tank to receive the sludge, buying time for subsequent treatment.

[0037] The entire control process is fully automated, requiring no manual intervention. At the same time, operators can monitor the equipment's operating status and control effects in real time through a visual interface, and can manually intervene to adjust as needed, ensuring the precise implementation of control measures and further enhancing the wastewater treatment system's ability to respond quickly to abnormal situations and its stability.

[0038] In some embodiments, obtaining the multidimensional state parameters of the sludge includes: Multiple sensors are deployed at different locations within the sludge treatment unit to acquire state parameters reflecting the physicochemical properties of the sludge.

[0039] Obtaining multidimensional state parameters of sludge requires a reasonable sensor layout and precise parameter acquisition logic. The core is to scientifically deploy multiple sensors at different spatial locations within the sludge treatment unit to comprehensively capture key state parameters reflecting the physicochemical properties of the sludge. As the core area of ​​sludge treatment, the physicochemical properties of the sludge within the sludge treatment unit vary at different spatial locations. Therefore, multiple sensors need to be distributed and deployed at different depths and orientations according to monitoring requirements, forming a comprehensive monitoring network without blind spots.

[0040] These sensors each undertake specific parameter acquisition tasks, capturing core parameters of the sludge such as solid particle content, moisture ratio, settling characteristics, and temperature changes. Solid particle content and settling characteristics directly reflect the physical state of the sludge, while moisture ratio and temperature changes are related to the sludge's physical properties and chemical reaction environment, together forming a multi-dimensional state parameter system reflecting the sludge's physicochemical properties. The sensor deployment must balance comprehensive spatial coverage with targeted data acquisition, ensuring that each sensor accurately collects sludge parameters for its corresponding area. All sensors output the collected parameters in real time via a stable transmission link, providing comprehensive and accurate basic data for subsequent analysis and processing, ensuring all-round control over the sludge's state.

[0041] In some embodiments, performing spatiotemporal feature analysis on the multidimensional state parameters to identify the changing patterns of sludge state and predict the changing trend includes: The multidimensional state parameters are preprocessed to improve data quality; The preprocessed data is input into a spatiotemporal feature fusion model, which is used to extract the spatial distribution features of the state parameters and capture their evolution patterns over time.

[0042] When performing spatiotemporal feature analysis on multidimensional state parameters and identifying patterns of change and predicting trends, preprocessing of the collected multidimensional state parameters is essential to improve data quality and provide a reliable foundation for subsequent analysis. The preprocessing process addresses potential issues such as high-frequency noise, missing values, and dimensional differences in the original data. Targeted processing methods are employed, including wavelet transform algorithms to effectively filter out high-frequency noise, preventing noise interference with the accuracy of the analysis results. Simultaneously, interpolation algorithms are used to appropriately fill in missing data, ensuring data integrity. Finally, normalization is used to eliminate dimensional differences between different parameters, ensuring a unified analytical standard for all types of state parameters.

[0043] The preprocessing process relies on field-programmable gate array (FPGA) chips to achieve real-time and efficient processing, ensuring the timeliness of data processing and saving time for subsequent model analysis. After preprocessing, high-quality parameter data is input into the spatiotemporal feature fusion model. This model adopts an architecture combining convolutional neural networks and recurrent neural networks, specifically designed to extract the spatial distribution features of state parameters and capture their evolution patterns over time.

[0044] Convolutional neural networks, through the synergistic effect of multiple convolutional and pooling layers, delve into the correlation characteristics of state parameters at different spatial locations, generating feature maps that clearly reflect the spatial distribution characteristics of sludge. Recurrent neural networks, using these feature maps as input, accurately capture the trajectory and evolution trend of state parameters over time through iterative calculations at multiple time steps, thereby comprehensively identifying the intrinsic changing patterns of sludge state.

[0045] To further enhance the model's analytical performance and adaptability, the spatiotemporal feature fusion model employs transfer learning technology. It first completes pre-training using a large existing sludge monitoring dataset, and then performs targeted fine-tuning based on actual data from specific application scenarios. This effectively improves the model's generalization ability and prediction accuracy, ensuring that it can accurately predict future trends in sludge conditions and providing solid analytical support for the determination of subsequent early warning information.

[0046] In some embodiments, determining the early warning information based on the predicted trend includes: The predicted trend of change is compared with preset conditions; Based on the comparison results, warning signals of different levels are generated.

[0047] The core of determining early warning information based on predicted trends is to ensure the accuracy and relevance of the warnings through scientific comparison and hierarchical judgment. After receiving the predicted trend data output from the spatiotemporal feature analysis, the intelligent early warning module first retrieves preset conditions within the system for comprehensive comparison. These preset conditions are a reasonable threshold system formulated based on the normal operating parameter range of wastewater treatment process standards and actual operating conditions. They cover the normal range critical values ​​and abnormal range definition standards for various multi-dimensional state parameters such as sludge concentration, moisture content, settling velocity, and temperature, providing a clear basis for comparison and judgment. During the comparison process, the system dynamically matches the predicted change curves of each parameter with the corresponding threshold ranges of the preset conditions. It not only focuses on whether the predicted value of a single parameter exceeds the preset limit, but also comprehensively analyzes the synergistic change relationship between multiple parameters to determine whether there are compound anomaly risks, ensuring the comprehensiveness and rigor of the comparison results.

[0048] Based on the comparison results, the system will generate warning signals of corresponding levels. When the predicted trend deviates slightly from the preset conditions, the sludge state is in a slightly abnormal range and there are no signs of continuous deterioration, a level 1 warning signal is generated; when the predicted trend significantly exceeds the preset conditions, the sludge state shows a moderate abnormality and has a further development trend, a level 2 warning signal is generated; when the predicted trend deviates seriously from the preset conditions, the sludge state faces a serious risk of abnormality that may affect the normal operation of the wastewater treatment process, a level 3 warning signal is generated.

[0049] Different levels of early warning signals will be displayed in real time through a visual interface. At the same time, the sound and light alarm device will issue warnings of corresponding intensity on site and send them to the operator's mobile terminal in the form of push notifications. This ensures that relevant personnel can quickly grasp the degree of risk according to the warning level, providing clear guidance for taking targeted control measures and effectively ensuring the stable operation of the sewage treatment system.

[0050] In some embodiments, controlling the wastewater treatment equipment based on the early warning information includes: Based on the level of the warning information, a corresponding equipment control strategy is determined and executed. The control strategy includes at least one of adjusting the stirring parameters, regulating the material flow rate, or activating the emergency treatment unit.

[0051] The core of controlling wastewater treatment equipment based on early warning information is to establish a precise matching mechanism between early warning levels and control strategies. Through intelligent control modules, targeted equipment control strategies can be quickly determined and executed based on the level of early warning information to ensure that sludge treatment anomalies are resolved efficiently.

[0052] The intelligent control module maintains a stable connection with the wastewater treatment equipment via an industrial Ethernet network. It can receive and analyze early warning information in real time, and then match the corresponding preset control strategies. These strategies include at least one of adjusting stirring parameters to regulate material flow or activating the emergency treatment unit, which can be flexibly selected according to abnormal situations. When a Level 1 early warning is received, the intelligent control module focuses on adjusting stirring parameters, sending digital commands to the controller of the sludge mixing device. By changing the rotation speed of the mixing device, it optimizes the mixing uniformity of the sludge, improving the minor abnormal state of the sludge from the source. When the early warning is Level 2, the control strategy switches to regulating material flow. The module sends commands to the controllers of related equipment such as the sludge return pump, precisely adjusting the flow rate of the material delivery to optimize the residence time of the sludge in the treatment system and ensure the stability of the treatment process. When a Level 3 early warning is triggered, the emergency treatment unit is immediately activated. The intelligent control module simultaneously sends control commands to emergency equipment such as the sludge discharge valve and the backup storage tank, quickly closing the discharge valve to prevent sludge overflow and environmental pollution, and simultaneously activating the backup storage tank to receive the sludge, creating conditions for subsequent emergency treatment.

[0053] The built-in priority scheduling unit of the intelligent control module ensures the orderly execution of control strategies. If multiple warning messages are triggered simultaneously, the control strategy corresponding to the highest-level warning is executed first. All control signals are transmitted using industrial standard protocol formats to ensure the accuracy and compatibility of equipment responses. Operators can monitor the execution status of control strategies and equipment operating parameters in real time through a visual interface. Manual intervention and adjustments are possible when necessary, ensuring both automation and efficiency while maintaining flexibility. This comprehensively guarantees the stable operation of wastewater treatment equipment under various abnormal scenarios and effectively enhances the system's ability to handle various sludge treatment anomalies.

[0054] In some embodiments, the method further includes: The multidimensional state parameters, the predicted change trends, the early warning information, and the equipment control records are encrypted and stored.

[0055] The data to be stored includes various raw and processed data acquired during the multi-dimensional state parameter acquisition phase; data on sludge state change patterns and predicted trends output during the spatiotemporal feature analysis phase; early warning information at all levels generated by the intelligent early warning module, including details such as the basis for early warning level determination and early warning trigger time; and equipment control records executed by the intelligent control module, including control strategy type, control parameter values, and control execution duration. This data is synchronously uploaded to a cloud database via a real-time transmission link. The cloud database employs distributed storage technology to construct a multi-node data backup system, ensuring data storage stability and resilience.

[0056] To achieve encrypted storage, the system employs blockchain technology to encrypt all data. Each data block contains a unique timestamp, complete data content, and the hash value of the previous data block, forming an interlocking encrypted chain. This technically eliminates the possibility of data tampering while ensuring end-to-end data traceability. Data access permissions are managed through a strict identity authentication system. Operators must log in using a valid username and password. Only authorized users can view and download relevant data according to their permissions, effectively preventing the risk of data leakage.

[0057] The cloud database is also equipped with a dedicated data analysis interface, which allows external systems to access stored data under authorized conditions. This provides comprehensive data support for the optimization and upgrading of wastewater treatment processes and the formulation of equipment maintenance plans, allowing the value of data to be fully realized and further improving the overall operation and management level of the wastewater treatment system.

[0058] Corresponding to the sludge condition monitoring method described above, this invention also proposes a sludge condition monitoring device. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0059] Figure 2 This is a schematic diagram of the structure of a sludge condition monitoring device provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes: The acquisition unit 21 is also used to acquire multidimensional state parameters of the sludge; The analysis unit 22 is also used to perform spatiotemporal feature analysis on the multidimensional state parameters, identify the change pattern of sludge state and predict the change trend; The determining unit 23 is also used to determine early warning information based on the predicted change trend; The control unit 24 is also used to control the sewage treatment equipment based on the warning information.

[0060] Furthermore, in one possible implementation of this disclosure, the acquisition unit 21 is further configured to: Multiple sensors are deployed at different locations within the sludge treatment unit to acquire state parameters reflecting the physicochemical properties of the sludge.

[0061] Furthermore, in one possible implementation of this disclosure, the analysis unit 22 is further configured to: The multidimensional state parameters are preprocessed to improve data quality; The preprocessed data is input into a spatiotemporal feature fusion model, which is used to extract the spatial distribution features of the state parameters and capture their evolution patterns over time.

[0062] Furthermore, in one possible implementation of this disclosure, the determining unit 23 is further configured to: The predicted trend of change is compared with preset conditions; Based on the comparison results, warning signals of different levels are generated.

[0063] Furthermore, in one possible implementation of this disclosure embodiment, the control unit 24 is further configured to: Based on the level of the warning information, a corresponding equipment control strategy is determined and executed. The control strategy includes at least one of adjusting the stirring parameters, regulating the material flow rate, or activating the emergency treatment unit.

[0064] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the device further includes: Storage unit 25 is also used to encrypt and store the multidimensional state parameters, the predicted change trend, the early warning information and the equipment control records.

[0065] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0066] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0067] Figure 4A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0068] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O (Input / Output) interface 405 is also connected to bus 404.

[0069] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0070] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as sludge condition monitoring methods. For example, in some embodiments, the sludge condition monitoring method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned sludge condition monitoring method by any other suitable means (e.g., by means of firmware).

[0071] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0072] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0073] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0074] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0075] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0076] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0077] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0078] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0079] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for monitoring the state of sludge, characterized in that, include: Obtain multidimensional state parameters of sludge; Spatiotemporal feature analysis is performed on the multidimensional state parameters to identify the changing patterns of sludge state and predict the changing trend. Early warning information is determined based on the predicted trend of change; Control the wastewater treatment equipment based on the aforementioned early warning information.

2. The method according to claim 1, characterized in that, The acquisition of multidimensional state parameters of the sludge includes: Multiple sensors are deployed at different locations within the sludge treatment unit to acquire state parameters reflecting the physicochemical properties of the sludge.

3. The method according to claim 1, characterized in that, The step of performing spatiotemporal feature analysis on the multidimensional state parameters to identify the changing patterns of sludge state and predict the changing trends includes: The multidimensional state parameters are preprocessed to improve data quality; The preprocessed data is input into a spatiotemporal feature fusion model, which is used to extract the spatial distribution features of the state parameters and capture their evolution patterns over time.

4. The method according to claim 1, characterized in that, The determination of early warning information based on the predicted trend includes: The predicted trend of change is compared with preset conditions; Based on the comparison results, warning signals of different levels are generated.

5. The method according to claim 1, characterized in that, The step of controlling the wastewater treatment equipment based on the early warning information includes: Based on the level of the warning information, a corresponding equipment control strategy is determined and executed. The control strategy includes at least one of adjusting the stirring parameters, regulating the material flow rate, or activating the emergency treatment unit.

6. The method according to claim 1, characterized in that, The method further includes: The multidimensional state parameters, the predicted change trends, the early warning information, and the equipment control records are encrypted and stored.

7. A sludge condition monitoring device, characterized in that, include: The acquisition unit is also used to obtain multidimensional state parameters of the sludge; The analysis unit is also used to perform spatiotemporal feature analysis on the multidimensional state parameters, identify the changing patterns of sludge state, and predict the changing trends. The determining unit is also used to determine early warning information based on the predicted change trend; The control unit is also used to control the wastewater treatment equipment based on the warning information.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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