Automatic control system of sewage treatment plant and multi-mode communication method

Abstract

The invention provides an automatic control system of a sewage treatment plant and a multi-mode communication method, which are used for converting multi-mode data of different industrial communication protocols into a standardized format and deploying the data in an edge computing gateway. The space-time synchronization module is used for carrying out multi-modal timestamps through a high-precision clock synchronization protocol and associating a data source position based on a spatial topology label; the data fusion module is used for carrying out reliability weighted fusion on the multi-modal data and dynamically adjusting the credibility score of the multi-modal data; the dynamic resource allocation module is used for switching communication paths through a software defined network according to real-time load requirements and configuring redundant channels for key links; and the abnormal detection and error correction module is used for identifying multi-modal data conflicts based on a rule engine or a machine learning model, triggering a cross-modal verification or manual intervention process and simulating a contradiction perception monitoring function of a forehead cortex layer, and the system scheme has a multi-modal integration capability and a self-adaptive mechanism and can realize technical breakthrough.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to an automatic control system and a multimodal communication method for a sewage treatment plant. Background Art

[0002] In the practical application of smart sewage treatment systems, the coordinated control of multimodal sensors and heterogeneous equipment faces significant challenges. In existing technologies, equipment from different manufacturers (such as pumps, valves, aerators, and dosing devices) usually adopt a variety of communication protocols (such as Modbus, Profinet, and OPC UA), resulting in reliance on customized middleware for data interaction, which severely limits the scalability and real-time performance of the system. At the same time, the data conflict problem caused by environmental interference or equipment drift in physical, chemical, and biological sensors has not been effectively solved. The traditional threshold alarm mechanism lacks cross-modal verification capabilities and is prone to misoperation. In addition, current control systems mostly rely on fixed rules and are difficult to dynamically adapt to sudden pollution events or equipment failures. The rigid resource allocation leads to low energy efficiency. More importantly, the timestamp deviation and spatial label mismatch of sensor data make it difficult for the digital twin model to truly reflect the state of the physical system, which in turn affects the global optimization decision. Therefore, there is an urgent need to provide a sewage treatment system with multimodal integration capabilities and adaptive mechanisms to achieve a technological breakthrough. Summary of the Invention

[0003] The purpose of the present invention is to solve the technical problems in the above background and to provide an automatic control system for a sewage treatment plant, comprising:

[0004] A protocol conversion module, used to convert multimodal data from different industrial communication protocols into a standardized format, is deployed on the edge computing gateway;

[0005] The spatiotemporal synchronization module uses a high-precision clock synchronization protocol to timestamp multiple modalities and associate data source locations based on spatial topology tags;

[0006] The data fusion module performs reliability-weighted fusion on multimodal data and dynamically adjusts the credibility score of multimodal data;

[0007] Dynamic resource allocation module, which switches communication paths through software-defined networks based on real-time load requirements and configures redundant channels for critical links;

[0008] The anomaly detection and error correction module identifies multimodal data conflicts based on a rule engine or machine learning model, and triggers cross-modal verification or manual intervention processes, simulating the conflict perception monitoring function of the prefrontal cortex.

[0009] In the preferred solution, the protocol conversion module supports the conversion of Modbus, OPC UA and Profinet protocols to MQTT or HTTP protocols, and the conversion process retains the semantic labels of the original multimodal data.

[0010] In a preferred solution, the spatiotemporal synchronization module further includes:

[0011] The time sliding window module is used to integrate multimodal data within the same preset time window;

[0012] The digital twin mapping module binds multimodal data with the spatial coordinates of the virtual sewage treatment plant three-dimensional model to achieve spatial consistency verification of multimodal data.

[0013] In a preferred solution, the reliability weighted fusion of the data fusion module includes:

[0014] Calculate dynamic credibility weights based on sensor calibration records, historical error rates, and environmental interference levels;

[0015] When a multimodal data conflict is detected, the multimodal data with high credibility is preferentially used to generate control instructions.

[0016] In a preferred solution, the dynamic resource allocation module includes the following operations:

[0017] Allocate 5G network slices or redundant fiber channels to critical data streams from dissolved oxygen sensors and toxicity detectors in aeration tanks;

[0018] During off-peak hours, the standby pump status inquiry communication link is turned off to reduce network load.

[0019] In a preferred solution, the anomaly detection and error correction module further includes:

[0020] Multimodal contradiction detection rule base, used to define the physical constraint relationship between multimodal data;

[0021] Automatically trigger cross-modality validation processes for backup sensor calibration or video surveillance review.

[0022] A multimodal communication method used in an automatic control system of a sewage treatment plant comprises the following steps:

[0023] S1, converts multimodal data under heterogeneous protocols into a standardized format through an industrial gateway and adds spatiotemporal metadata tags;

[0024] S2, aligning multi-source sensor timestamps through a high-precision clock synchronization protocol and mapping spatial coordinates in the digital twin model;

[0025] S3. Calculate the joint probability distribution of multimodal data based on the Bayesian network and generate reliability-weighted control parameters;

[0026] S4. Dynamically allocate communication resources based on real-time network load to enhance bandwidth and redundancy of critical data links;

[0027] S5. After detecting multimodal data conflicts, the historical pattern library and cross-modal verification process are called to correct errors.

[0028] In a preferred solution, the generation of the reliability weighted control parameter further includes:

[0029] When it is detected that the data of a certain sensor deviates from the historical pattern by more than a threshold, its credibility weight is automatically reduced;

[0030] In the case of communication delay or data loss, an LSTM neural network is used to predict missing data values.

[0031] In a preferred solution, the operation of dynamic resource allocation includes:

[0032] In the event of sudden pollution, bandwidth is allocated first to control instructions for toxicity sensors and emergency dosing equipment;

[0033] The SDN controller routes non-urgent data to a low-priority queue.

[0034] In the preferred solution, the cross-modal verification process includes:

[0035] When the data from the pH sensor and the ORP sensor are inconsistent, the camera is used to analyze the foam morphology in the sedimentation tank to assist in decision-making;

[0036] When the flow meter data is abnormal, the pump current signal is compared with the valve opening data for physical consistency verification.

[0037] The beneficial effects of the present invention are:

[0038] (1) The protocol conversion module of the edge gateway supports lossless conversion of multiple protocols, reducing the integration cost of heterogeneous devices and improving the compatibility of the system.

[0039] (2) The reliability weighted algorithm improves the decision accuracy of multimodal conflict data.

[0040] (3)SDN-driven dynamic bandwidth allocation significantly reduces the transmission delay of key data links and improves the emergency response speed.

[0041] (4) Spatiotemporal synchronization and digital twin mapping enable physical consistency verification of cross-modal data, reducing the false alarm rate.

[0042] (5) Through LTP / LTD communication resource scheduling, the overall energy consumption of the system is reduced, and the stability under sudden load is significantly enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a system architecture diagram of the present invention.

[0044] Figure 2 It is a flow chart of dynamic resource allocation of the present invention.

[0045] Figure 3 It is a cross-modal conflict resolution decision flow chart of the present invention. DETAILED DESCRIPTION

[0046] Example 1

[0047] A sewage treatment plant automatic control system, comprising:

[0048] A protocol conversion module, used to convert multimodal data from different industrial communication protocols into a standardized format, is deployed on the edge computing gateway;

[0049] The spatiotemporal synchronization module uses a high-precision clock synchronization protocol to timestamp multiple modalities and associate data source locations based on spatial topology tags;

[0050] The data fusion module performs reliability-weighted fusion on multimodal data and dynamically adjusts the credibility score of multimodal data;

[0051] Dynamic resource allocation module, which switches communication paths through software-defined networks based on real-time load requirements and configures redundant channels for critical links;

[0052] The anomaly detection and error correction module identifies multimodal data conflicts based on a rule engine or machine learning model, and triggers cross-modal verification or manual intervention processes, simulating the conflict perception monitoring function of the prefrontal cortex.

[0053] Preferably, the protocol conversion module supports conversion of Modbus, OPC UA and Profinet protocols to MQTT or HTTP protocols, and the conversion process retains the semantic labels of the original multimodal data.

[0054] Preferably, the spatiotemporal synchronization module further includes:

[0055] The time sliding window module is used to integrate multimodal data within the same preset time window;

[0056] The digital twin mapping module binds multimodal data with the spatial coordinates of the virtual sewage treatment plant three-dimensional model to achieve spatial consistency verification of multimodal data.

[0057] Preferably, the reliability weighted fusion of the data fusion module includes:

[0058] Calculate dynamic credibility weights based on sensor calibration records, historical error rates, and environmental interference levels;

[0059] When a multimodal data conflict is detected, the multimodal data with high credibility is preferentially used to generate control instructions.

[0060] Preferably, the dynamic resource allocation module includes the following operations:

[0061] Allocate 5G network slices or redundant fiber channels to critical data streams from dissolved oxygen sensors and toxicity detectors in aeration tanks;

[0062] During off-peak hours, the standby pump status inquiry communication link is turned off to reduce network load.

[0063] Preferably, the anomaly detection and error correction module further includes:

[0064] Multimodal contradiction detection rule base, used to define the physical constraint relationship between multimodal data;

[0065] Automatically trigger cross-modality validation processes for backup sensor calibration or video surveillance review.

[0066] A multimodal communication method used in an automatic control system of a sewage treatment plant comprises the following steps:

[0067] S1, converts multimodal data under heterogeneous protocols into a standardized format through an industrial gateway and adds spatiotemporal metadata tags;

[0068] S2, aligning multi-source sensor timestamps through a high-precision clock synchronization protocol and mapping spatial coordinates in the digital twin model;

[0069] S3. Calculate the joint probability distribution of multimodal data based on the Bayesian network and generate reliability-weighted control parameters;

[0070] S4. Dynamically allocate communication resources based on real-time network load to enhance bandwidth and redundancy of critical data links;

[0071] S5. After detecting multimodal data conflicts, the historical pattern library and cross-modal verification process are called to correct errors.

[0072] Preferably, the generation of the reliability weighted control parameter further includes:

[0073] When it is detected that the data of a certain sensor deviates from the historical pattern by more than a threshold, its credibility weight is automatically reduced;

[0074] In the case of communication delay or data loss, an LSTM neural network is used to predict missing data values.

[0075] Preferably, the operation of dynamic resource allocation includes:

[0076] In the event of sudden pollution, bandwidth is allocated first to control instructions for toxicity sensors and emergency dosing equipment;

[0077] The SDN controller routes non-urgent data to a low-priority queue.

[0078] Preferably, the cross-modal verification process includes:

[0079] When the data from the pH sensor and the ORP sensor are inconsistent, the camera is used to analyze the foam morphology in the sedimentation tank to assist in decision-making;

[0080] When the flow meter data is abnormal, the pump current signal is compared with the valve opening data for physical consistency verification.

[0081] Example 2

[0082] like Figures 1 to 3 As shown, a multimodal communication system for sewage treatment based on brain-like mechanism includes:

[0083] Protocol conversion module: used to convert data of different industrial communication protocols into a standardized format. The protocol conversion module is deployed in the edge computing gateway. The conversion delay is T conv Satisfies the following formula:

[0084]

[0085] Among them, T base is the basic protocol parsing time, L j is the data field length, θ j is the semantic label complexity factor, R is the gateway processing rate;

[0086] Spatiotemporal synchronization module: This module aligns the timestamps of multimodal sensor data using a high-precision clock synchronization protocol (IEEE 1588) and associates the data source locations based on spatial topology tags. Verification rules include:

[0087]

[0088] Data fusion module: Use the following credibility weighted formula to fuse multimodal data:

[0089]

[0090] Among them, w i0 is the initial reliability of the sensor (determined by factory accuracy), tt cal,i is the time interval from the last calibration (days), E hist,i is the historical average error rate, E max The maximum allowable error is S envis the environmental interference level (such as temperature fluctuation and electromagnetic noise, which are sensed by IoT nodes), λ and α are the attenuation coefficient and compensation coefficient (empirical values: λ = 0.01, α = 0.2);

[0091] Dynamic resource allocation module: optimizes bandwidth allocation based on the objective function:

[0092]

[0093] Among them, P k is the data flow priority (toxicity detection P = 10, log transmission P = 1), B k is the bandwidth allocated to the kth link, E k (B k ) is the bandwidth B k Corresponding energy consumption (measured fitting function, such as E = 0.5B 1.2 ), β is the energy consumption penalty coefficient (default is 0.3);

[0094] Anomaly detection and error correction module: Detect multimodal data conflicts based on joint probability models:

[0095]

[0096] Preferably, in the protocol conversion module:

[0097] θ j The value selection rule is: simple label θ j =1, composite label θ j =2.5;

[0098] Supports conversion from Modbus, OPC UA, and Profinet protocols to MQTT or HTTP protocols, and the conversion process retains the semantic tags of the original data.

[0099] Preferably, the spatiotemporal synchronization module further includes:

[0100] In the flow conservation check, γ = 0.1, and the pollutant diffusion continuity check satisfies:

[0101]

[0102] The digital twin mapping unit binds the sensor data with the spatial coordinates of the virtual sewage treatment plant 3D model.

[0103] Preferably, in the weight calculation of the data fusion module:

[0104] Initial credibility w i0 Determined by the sensor's factory accuracy level;

[0105] Historical error rate E hist,iBased on the data statistics of the past 30 days, the attenuation coefficient λ = 0.01.

[0106] Preferably, in the dynamic resource allocation module:

[0107] Data flow priority P k The set rules are: toxicity detection P = 10, log transmission P = 1;

[0108] Energy consumption function E k (B k ) is fitted to E = 0.5B by actual measurement 1.2 , energy consumption penalty coefficient β = 0.3.

[0109] Preferably, in the anomaly detection and error correction module:

[0110] Cross-modal correlation function f(x k ,x l ) including pH-turbidity regression curves or flow-level conservation relationships;

[0111] When I cross =0.1, triggering the backup sensor calibration or video review process.

[0112] Preferably, a multimodal communication method for sewage treatment based on a brain-like mechanism comprises the following steps:

[0113] The protocol conversion module converts heterogeneous protocol data into a standardized format and calculates the latency.

[0114] The spatiotemporal synchronization module is used to verify the flow conservation and pollutant diffusion continuity, and the constraints are:

[0115] Verification rules:

[0116]

[0117] Device space coordinates∈preset topology map;

[0118]

[0119] Among them, Q in is the water inlet and outlet flow (m 3 / h), γ is the permissible fluctuation coefficient (usually 0.1), C i is the concentration of the i-th pollutant, v i is the corresponding flow rate.

[0120] Generate fusion control parameters based on credibility weighted formula and dynamically adjust weight rules;

[0121] Optimize bandwidth allocation according to objective functions, priorities and energy consumption constraints;

[0122] After detecting multimodal conflicts, the joint probability model and cross-modal verification process are called to correct the errors.

[0123] Preferably, the credibility weighted control parameter generation includes:

[0124] When the sensor data deviates from the historical mean by more than 3σ i When , its weight is automatically reduced to below 0.2;

[0125] When data is missing, the LSTM neural network is used to predict the missing values, and the prediction results are used in the fusion calculation.

[0126] Preferably, the dynamic resource allocation includes:

[0127] In the event of sudden pollution, the allocated bandwidth meets the requirement of B toxicity ≥ 80%;

[0128] Non-critical data is routed to the low-priority queue with a bandwidth limit of B. min =10Mbps.

[0129] Preferably, the cross-modal verification process includes:

[0130] When the pH sensor and camera data conflict, the following rules are used for decision making:

[0131]

[0132] If the flow meter is abnormal, check the physical relationship between the pump current I and the valve opening θ:

[0133]

[0134] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A sewage treatment plant automatic control system, characterized by: include: A protocol conversion module, used to convert multimodal data from different industrial communication protocols into a standardized format, is deployed on the edge computing gateway; The spatiotemporal synchronization module uses a high-precision clock synchronization protocol to timestamp multiple modalities and associate data source locations based on spatial topology tags; The data fusion module performs reliability-weighted fusion on multimodal data and dynamically adjusts the credibility score of multimodal data; Dynamic resource allocation module, which switches communication paths through software-defined networks based on real-time load requirements and configures redundant channels for critical links; The anomaly detection and error correction module identifies multimodal data conflicts based on a rule engine or machine learning model, and triggers cross-modal verification or manual intervention processes, simulating the conflict perception monitoring function of the prefrontal cortex.

2. A sewage treatment plant automatic control system according to claim 1, characterized in that: The protocol conversion module supports the conversion of Modbus, OPC UA and Profinet protocols to MQTT or HTTP protocols, and the conversion process retains the semantic labels of the original multimodal data.

3. The automatic control system for a sewage treatment plant according to claim 1, characterized in that: The spatiotemporal synchronization module also includes: The time sliding window module is used to integrate multimodal data within the same preset time window; The digital twin mapping module binds multimodal data with the spatial coordinates of the virtual sewage treatment plant three-dimensional model to achieve spatial consistency verification of multimodal data.

4. The automatic control system for a sewage treatment plant according to claim 1, characterized in that: The reliability weighted fusion of the data fusion module includes: Calculate dynamic credibility weights based on sensor calibration records, historical error rates, and environmental interference levels; When a multimodal data conflict is detected, the multimodal data with high credibility is preferentially used to generate control instructions.

5. The automatic control system for a sewage treatment plant according to claim 1, characterized in that: The dynamic resource allocation module includes the following operations: Allocate 5G network slices or redundant fiber channels to critical data streams from dissolved oxygen sensors and toxicity detectors in aeration tanks; During off-peak hours, the standby pump status inquiry communication link is turned off to reduce network load.

6. The automatic control system for a sewage treatment plant according to claim 1, characterized in that: The anomaly detection and error correction module also includes: Multimodal contradiction detection rule base, used to define the physical constraint relationship between multimodal data; Automatically trigger cross-modality validation processes for backup sensor calibration or video surveillance review.

7. A multimodal communication method for the automatic control system of a sewage treatment plant according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1, converts multimodal data under heterogeneous protocols into a standardized format through an industrial gateway and adds spatiotemporal metadata tags; S2, aligning multi-source sensor timestamps through a high-precision clock synchronization protocol and mapping spatial coordinates in the digital twin model; S3. Calculate the joint probability distribution of multimodal data based on the Bayesian network and generate reliability-weighted control parameters; S4. Dynamically allocate communication resources based on real-time network load to enhance bandwidth and redundancy of critical data links; S5. After detecting multimodal data conflicts, the historical pattern library and cross-modal verification process are called to correct errors.

8. The multimodal communication method used in the automatic control system of a sewage treatment plant according to claim 7, characterized in that: The generation of reliability weighted control parameters also includes: When it is detected that the data of a certain sensor deviates from the historical pattern by more than a threshold, its credibility weight is automatically reduced; In the case of communication delay or data loss, an LSTM neural network is used to predict missing data values.

9. The multimodal communication method used in the automatic control system of a sewage treatment plant according to claim 7, characterized in that: Dynamic resource allocation operations include: In the event of sudden pollution, bandwidth is allocated first to control instructions for toxicity sensors and emergency dosing equipment; The SDN controller routes non-urgent data to a low-priority queue.

10. The multimodal communication method used in the automatic control system of a sewage treatment plant according to claim 7, characterized in that: The cross-modal validation process includes: When the data from the pH sensor and the ORP sensor are inconsistent, the camera is used to analyze the foam morphology in the sedimentation tank to assist in decision-making; When the flow meter data is abnormal, the pump current signal is compared with the valve opening data for physical consistency verification.

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