Remote control method and system for sewage treatment equipment and storage medium

Abstract

The invention relates to the technical field of equipment remote control, and discloses a remote control method and system for sewage treatment equipment and a storage medium. The method comprises the following steps: performing communication encryption and identity authentication processing on a standard data packet of a sewage treatment system, and constructing an equipment communication link; performing intelligent analysis and state diagnosis processing on the data in the equipment communication link, and generating an equipment operation state report; performing control strategy generation and parameter matching processing according to the equipment operation state report, and outputting an equipment control instruction; performing hierarchical analysis and execution state monitoring on the equipment control instruction, and establishing a control feedback data link; and performing remote optimization treatment according to the data in the control feedback data link so as to realize remote intelligent control of the sewage treatment equipment. Under the condition that the sewage treatment equipment is dispersedly arranged, the real-time monitoring of the running state of the equipment and the intelligent adjustment of the process parameters are realized, and the stability of the treatment effect is ensured.

Description

Technical Field

[0001] The present application relates to the field of remote control of equipment, and in particular to a remote control method, system and storage medium for sewage treatment equipment. Background Art

[0002] With the acceleration of urbanization, decentralized sewage treatment equipment has been widely used in rural areas of towns and villages. Existing sewage treatment equipment mainly adopts a treatment method that combines MBBR technology with membrane separation technology, and completes sewage treatment through the dual effects of biofilm degradation and membrane separation filtration. This type of equipment is usually arranged in towns or new communities with relatively concentrated populations. It has the advantages of small footprint, good effluent water quality, and stable operation. At present, the control method of this type of equipment mainly relies on on-site operators to adjust process parameters based on experience, or use a simple PLC control system for basic automation control.

[0003] However, due to the scattered distribution of sewage treatment equipment, the uneven professional level of operation and management personnel, the difficulty in real-time grasp of equipment operation status, and the lack of scientific basis for process parameter adjustment, the treatment effect is unstable, the operation cost is high, and the equipment failure response is slow. In particular, when the influent water quality fluctuates greatly and the equipment operating conditions change frequently, the existing control method is difficult to respond in time and make reasonable adjustments, which affects the treatment effect and operation efficiency of the equipment. Summary of the invention

[0004] The present application provides a remote control method, system and storage medium for sewage treatment equipment, which is used to achieve real-time monitoring of equipment operation status and intelligent adjustment of process parameters in the case of decentralized sewage treatment equipment, so as to ensure the stability of treatment effect. The present invention can effectively overcome the technical defects of the prior art such as insufficient professionalism of operators and delayed control response by establishing a control system of remote data collection, intelligent analysis and diagnosis, automatic control strategy generation and closed-loop optimization.

[0005] In the first aspect, the present application provides a remote control method for sewage treatment equipment, which includes: multi-dimensional collection and data preprocessing of operating parameters and water quality indicators of sewage treatment equipment to generate a standard data packet for the sewage treatment system; communication encryption and identity authentication processing for the standard data packet of the sewage treatment system to build a device communication link; intelligent analysis and status diagnosis processing of data in the device communication link to generate a device operation status report; control strategy generation and parameter matching processing based on the device operation status report to output device control instructions; hierarchical analysis and execution status monitoring of the device control instructions to establish a control feedback data link; remote optimization processing based on the data in the control feedback data link to achieve remote intelligent control of sewage treatment equipment.

[0006] In a second aspect, the present application provides a remote control system for a sewage treatment device, the remote control system for the sewage treatment device comprising: The acquisition module is used to collect and preprocess the operating parameters and water quality indicators of sewage treatment equipment in multiple dimensions, and generate a standard data package for the sewage treatment system; An encryption module, used to perform communication encryption and identity authentication processing on the sewage treatment system standard data packet, and to establish a device communication link; An analysis module, used to perform intelligent analysis and status diagnosis processing on the data in the device communication link, and generate a device operation status report; A control module, used to generate a control strategy and perform parameter matching processing according to the equipment operation status report, and output equipment control instructions; A monitoring module, used for performing hierarchical analysis and execution status monitoring of the device control instructions, and establishing a control feedback data link; The optimization module is used to perform remote optimization processing based on the data in the control feedback data link to achieve remote intelligent control of the sewage treatment equipment.

[0007] A third aspect of the present application provides a computer-readable storage medium, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the above-mentioned remote control method for sewage treatment equipment.

[0008] In the technical solution provided in the present application, by multi-dimensionally collecting and preprocessing the operating parameters and water quality indicators of the sewage treatment equipment, comprehensive monitoring and data standardization of the equipment operating status are achieved, providing a reliable data basis for subsequent analysis and decision-making; communication encryption and identity authentication processing are performed on the standard data packets of the sewage treatment system, effectively ensuring the security and reliability of data transmission, and preventing unauthorized access and data tampering; intelligent analysis and status diagnosis of the data in the equipment communication link are performed, and equipment operation anomalies and performance deviations are accurately identified, and potential faults are discovered in advance; control strategy generation and parameter matching are performed based on the equipment operation status report, achieving precise regulation and optimization of process parameters, and ensuring the stability of the treatment effect; a complete control feedback mechanism is established by hierarchical analysis and execution status monitoring of equipment control instructions to ensure accurate execution and timely feedback of control instructions; remote optimization processing is performed based on the data in the control feedback data link, realizing dynamic optimization and adaptive adjustment of equipment operating parameters, and improving equipment operation efficiency and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0010] Figure 1 This is a schematic diagram of an embodiment of a remote control method for sewage treatment equipment in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a remote control system for sewage treatment equipment in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a remote control method, system and storage medium for sewage treatment equipment. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0012] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the remote control method for sewage treatment equipment in the embodiment of the present application includes: Step S101, multi-dimensionally collect and pre-process the operating parameters and water quality indicators of the sewage treatment equipment to generate a standard data package for the sewage treatment system; Step S102: Perform communication encryption and identity authentication on the sewage treatment system standard data packet to establish a device communication link; Step S103: Perform intelligent analysis and status diagnosis processing on the data in the device communication link to generate a device operation status report; Step S104: Generate a control strategy and perform parameter matching processing according to the equipment operation status report, and output equipment control instructions; Step S105: Perform hierarchical analysis and execution status monitoring on the device control instructions, and establish a control feedback data link; Step S106: Perform remote optimization processing based on the data in the control feedback data link to achieve remote intelligent control of the sewage treatment equipment.

[0013] It is understandable that the execution subject of the present application may be a remote control system for sewage treatment equipment, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0014] Specifically, data is collected from the sewage treatment equipment through multiple sets of sensors, including water quality sensors, dissolved oxygen sensors and suspended solids sensors. The water quality sensor is arranged at the water inlet of the equipment to collect three basic water quality indicators: COD concentration, ammonia nitrogen content and pH value, with a sampling frequency of once every 5 minutes. The dissolved oxygen sensor and suspended solids sensor are arranged in the biochemical treatment unit to monitor the changes in dissolved oxygen concentration and sludge concentration in the biochemical reaction process in real time, with a sampling frequency of once every 1 minute. There are often outliers in the collected raw data, which are eliminated by the 3σ criterion, and data points that deviate from the mean by more than 3 times the standard deviation are eliminated. The data after outlier processing also needs to be standardized, and the data of different dimensions are uniformly converted to the range of 0-1 for subsequent analysis and processing. After the data preprocessing is completed, the data is encrypted for transmission and identity authentication. Data encryption adopts the AES-256 and RSA hybrid encryption scheme, in which AES-256 is used to encrypt the data itself and RSA is used to encrypt the AES key. The specific encryption process is to divide the data into several data blocks, each of which is 256 bits in size, and encrypt each data block using the AES-256 algorithm, while generating a session key for subsequent communications. Identity authentication uses zero-knowledge proof technology to verify the legitimacy of the data sender's identity through a digital signature, while also verifying the integrity of the data.

[0015] The data transmission adopts a layered network architecture, and integrates the QoS service quality assurance mechanism on the basis of the TCP / IP protocol to ensure the transmission quality of key data. Data is transmitted through the optimized MQTT protocol, which is lightweight and has low bandwidth occupancy, and is suitable for data transmission in the IoT scenario. During the transmission process, the network connection status is monitored in real time through the heartbeat packet mechanism, and the local cache mechanism is automatically started when a network anomaly is detected. After the equipment communication link is established, the transmitted data is intelligently analyzed and diagnosed. First, the five core process parameters of influent COD concentration, dissolved oxygen content, sludge return ratio, MBBR carrier filling rate, and transmembrane pressure difference of membrane modules are extracted, and the correlation between the parameters is determined through correlation analysis. Trend analysis is performed on key process parameters, the parameter change rate and fluctuation range are calculated, and abnormal fluctuations are identified. At the same time, the normal change range of process parameters is established based on historical operation data, and an early warning is issued when the parameters exceed the normal range.

[0016] Based on the analysis and diagnosis results, the corresponding control strategy is generated. The control strategy is mainly aimed at three core process units: biochemical reaction unit, membrane separation unit and sludge return unit. For the biochemical reaction unit, the aeration volume and dissolved oxygen concentration are mainly adjusted; for the membrane separation unit, the membrane flux and backwash cycle are adjusted; for the sludge return unit, the return ratio and the residual sludge discharge are adjusted. The generation of the control strategy needs to consider the coupling relationship between multiple process parameters. For example, an increase in aeration volume will affect the dissolved oxygen concentration and the biochemical reaction efficiency. The execution of control instructions adopts a hierarchical analysis and status monitoring mechanism. The control instructions are divided into three levels according to priority: emergency control instructions (such as equipment protection related), process optimization instructions (such as parameter tuning) and regular operation instructions. For each control instruction, the parameter legitimacy is first verified to ensure that the control parameters are within the range allowed by the equipment, and then they are processed in sequence according to the execution priority. During the execution of the instruction, the equipment response data, including the execution status and execution effect, are collected in real time, and these data are compressed and packaged and fed back to the control center.

[0017] For example, in a sewage treatment plant with a treatment scale of 1,000 tons / day, the raw data collected by the inlet COD concentration sensor within 10 minutes are: 320mg / L, 318mg / L, 315mg / L, 390mg / L, 322mg / L, 319mg / L, of which 390mg / L obviously deviates from other data and is determined as an abnormal value by the 3σ criterion and is eliminated. The data after eliminating the abnormal value is standardized and transmitted to the control center. The control center detected that the equipment operation status report showed that the dissolved oxygen concentration continued to be low (less than 2mg / L). Through analysis, it was found that it was caused by the increase in the inlet COD load, and the aeration volume adjustment instruction was immediately generated: the aeration volume was increased from the original 150m³ / h to 180m³ / h, and the sludge return ratio was adjusted from 100% to 120% to enhance the biochemical treatment capacity. The control instruction was encrypted and sent to the on-site controller. After the controller executed the adjustment, the dissolved oxygen concentration gradually returned to the normal level of 2.5 mg / L, and the effluent COD was maintained below 50 mg / L, achieving the expected control effect.

[0018] In the embodiment of the present application, by multi-dimensional collection and data preprocessing of sewage treatment equipment operating parameters and water quality indicators, comprehensive monitoring and data standardization of equipment operating status are achieved, providing a reliable data basis for subsequent analysis and decision-making; communication encryption and identity authentication processing are performed on sewage treatment system standard data packets, effectively ensuring the security and reliability of data transmission, and preventing unauthorized access and data tampering; intelligent analysis and status diagnosis of data in the equipment communication link are performed, and equipment operation anomalies and performance deviations are accurately identified, and potential faults are discovered in advance; control strategy generation and parameter matching are performed based on equipment operation status reports, and precise regulation and optimization of process parameters are achieved to ensure the stability of treatment effects; a complete control feedback mechanism is established by hierarchical analysis and execution status monitoring of equipment control instructions to ensure accurate execution and timely feedback of control instructions; remote optimization processing is performed based on data in the control feedback data link, and dynamic optimization and adaptive adjustment of equipment operating parameters are achieved, thereby improving equipment operation efficiency and economy.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Multiple sets of water quality sensors are used to collect COD concentration, ammonia nitrogen, and pH value at the inlet in real time to obtain the original inlet parameter data; (2) Continuously monitor the operating status of the biochemical treatment unit of the sewage treatment equipment through dissolved oxygen sensors and suspended solids sensors to obtain biochemical treatment process data; (3) Outlier elimination and data standardization are performed based on the original influent parameter data and biochemical treatment process data to generate a preprocessed data set; (4) Perform data correlation analysis and trend feature extraction on the preprocessed data set through the time series analysis algorithm to obtain the data feature set; (5) Packaging and format conversion of data are performed according to the data feature set to build a data transmission structure; (6) The integrity of the data transmission structure is verified and the redundancy is analyzed through the data verification algorithm, and the standard data packet of the sewage treatment system is output.

[0020] Specifically, the original inlet parameter data is collected by multiple groups of water quality sensors arranged at the inlet end. Among them, the COD concentration sensor adopts the ultraviolet absorption method, and the COD concentration is converted by measuring the light absorption value at a specific wavelength, and the sampling frequency is once every 5 minutes; the ammonia nitrogen sensor adopts the ion selective electrode method, and the ammonia nitrogen content is calculated by measuring the potential difference, and the sampling frequency is once every 10 minutes; the pH value sensor adopts the glass electrode method to monitor the changes in the pH of the inlet water in real time, and the sampling interval is 1 minute. The data collected by these sensors are summarized by the data acquisition unit to form a preliminary original inlet parameter data set. In the biochemical treatment unit, the dissolved oxygen sensor and the suspended solids sensor continuously monitor the process operation status. The dissolved oxygen sensor adopts the fluorescence measurement principle, which can accurately reflect the dissolved oxygen content in the biochemical pool, and the sampling frequency is once every 1 minute. The suspended solids sensor works based on the principle of light scattering, and determines the activated sludge concentration by measuring the scattering intensity of the mixed liquid to light, and the sampling frequency is once every 2 minutes. The amount of data generated by each sensor within 24 hours is: 1440 points of dissolved oxygen data and 720 points of suspended solids data.

[0021] The raw data collected by the sensor often contain outliers and noise interference. For the original influent parameter data and biochemical treatment process data, the sliding median method is first used to remove outliers. The sliding window size is set to 5 data points, the median in the window is calculated, and the data points that deviate from the median by more than the set threshold are marked as outliers. The data standardization process uses the maximum and minimum value standardization method to uniformly map data of different dimensions to the [0,1] interval. The standardized data is more suitable for subsequent analysis and processing, and it is also convenient for comparison between different parameters. The preprocessed data set needs to be analyzed in time series to extract data features. The time series analysis algorithm includes trend analysis and correlation analysis. Trend analysis uses the exponential smoothing method to extract the change trend of the data sequence, which can reflect the long-term change law of the parameters. The correlation analysis uses the sliding correlation coefficient calculation method to calculate the dynamic correlation between different parameters, with special attention to the relationship between influent water quality indicators and biochemical treatment effects. The features obtained through time series analysis include: parameter change trend, fluctuation cycle, correlation coefficient matrix, etc.

[0022] The data feature set needs to be structured before it can be transmitted over the network. The data packaging process organizes various feature data in a unified format, including the data header (recording data type, timestamp, data length and other information) and the data body (storing the actual feature data). Format conversion ensures the storage efficiency and transmission efficiency of the data. The data transmission structure adopts a layered design to facilitate data access and parsing. Before data transmission, the data transmission structure is verified for integrity and redundancy analysis. The data verification uses the cyclic redundancy check (CRC) algorithm to calculate the data check code and transmit it together with the data. Redundancy analysis evaluates the redundancy of the data by calculating the information entropy of the data, and compresses the data with higher redundancy. The final standard data packet contains both complete data information and high transmission efficiency.

[0023] Taking a sewage treatment plant with a treatment scale of 2,000 tons / day as an example, within a sampling period (1 hour), the raw data (mg / L) recorded by the COD sensor at the inlet end is: {380, 375, 382, ​​450, 378, 376, 381, 379,377, 425, 383, 380}. After processing by the sliding median method (window size is 5), 450mg / L and 425mg / L were found to be abnormal values ​​and were removed. The remaining data were normalized by the maximum and minimum values ​​to obtain a standardized data set: {0.57, 0.0, 0.87, 0.38, 0.13, 0.75, 0.5, 0.25, 1.0, 0.57}. At the same time, the dissolved oxygen concentration data of the biochemical pool showed a negative correlation with the inlet COD concentration, with a correlation coefficient of -0.82. These processed data are packaged into standard data packets together with timestamp, sensor number and other information, and transmitted to the control center after CRC verification, realizing efficient and reliable data transmission.

[0024] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) AES-256 and RSA dual encryption algorithms are used to perform block encryption through a hybrid encryption mechanism, and session key generation technology is used to process the sewage treatment system standard data packet to obtain a multi-level encrypted data stream; (2) Combining zero-knowledge proof with digital signature technology, the identity authentication algorithm completes the two-way verification and integrity check of multi-level encrypted data streams, and performs permission classification processing to obtain security authentication data packets; (3) Based on the QoS service quality assurance mechanism, combined with the layered protocol stack technology, the TCP / IP protocol parameters in the security authentication data packet are adaptively configured and optimized to generate multi-dimensional communication configuration parameters; (4) Use the optimized MQTT protocol and intelligent fault warning mechanism to dynamically detect the network status of multi-dimensional communication configuration parameters, integrate the delay evaluation algorithm to complete the quality analysis, and generate dynamic link status feature information; (5) Based on the load balancing principle, the self-organizing network algorithm is used to complete path selection and resource scheduling based on the dynamic link state characteristic information, and the bandwidth optimization technology is combined to perform transmission control to obtain an adaptive transmission control strategy; (6) Integrate network slicing technology and software-defined network architecture to build secure channels and isolate resources based on adaptive transmission control strategies to establish device communication links.

[0025] Specifically, the AES-256 and RSA dual encryption algorithms are used to process the sewage treatment system standard data packets. AES-256 is a symmetric encryption algorithm with fast encryption speed. It uses a 256-bit key to encrypt data; RSA is an asymmetric encryption algorithm used to encrypt AES keys. The specific encryption process is: the standard data packet is divided into blocks of 256 bytes, each data block is encrypted with AES-256, and a random session key is generated for subsequent communication. The session key is encrypted with the RSA algorithm and transmitted together with the encrypted data to form a multi-level encrypted data stream. In data security transmission, zero-knowledge proof technology is used for identity authentication. Zero-knowledge proof allows the prover to prove to the verifier that a proposition is correct without revealing any additional information. Digital signatures ensure the integrity and non-repudiation of data. The identity authentication process includes: the prover generates a key pair, signs the data with a private key, and the verifier verifies the validity of the signature with a public key. At the same time, the user permissions are graded into three levels: administrator, operator, and viewer. Different levels have different data access and control permissions.

[0026] To ensure the quality of data transmission, the QoS (Quality of Service) service quality assurance mechanism is adopted. The QoS mechanism ensures the timely transmission of key data by classifying, marking and queuing network traffic. In the TCP / IP protocol stack, different service quality levels are set for different types of data streams: real-time control data has the highest priority, status monitoring data has the second highest priority, and historical data has the lowest priority. TCP protocol parameters are optimized, including adjusting window size, timeout retransmission time, congestion control parameters, etc.

[0027] MQTT (Message Queuing Telemetry Transport) protocol is a lightweight message transmission protocol, which is particularly suitable for IoT scenarios. MQTT protocol optimization includes: setting a reasonable keepalive time, selecting an appropriate service quality level (QoS level), configuring message retransmission strategy, etc. The network status is monitored through an intelligent fault warning mechanism, including detecting indicators such as network delay, packet loss rate, and connection stability. These indicators are compared with preset thresholds, and warnings are triggered when the indicators are abnormal. The load balancing principle is used to optimize network resource utilization. The self-organizing network algorithm can automatically adjust the communication path according to the network status. The specific implementation includes: collecting the load status of each communication link, calculating the link weight, and selecting the optimal transmission path. Bandwidth optimization technology improves transmission efficiency by dynamically adjusting the data transmission rate, compressing the transmission data, and selecting the best transmission time window.

[0028] Network slicing technology virtualizes physical network resources to provide customized network services for different types of businesses. The software-defined network (SDN) architecture separates the network control layer from the data forwarding layer to achieve flexible scheduling of network resources. Through the combination of these two technologies, an independent secure communication channel is established, and network resources are isolated to avoid mutual interference.

[0029] For example, in the remote control of a sewage treatment plant, the COD online monitoring data is 4KB and is transmitted every 5 minutes. First, the data is divided into 16 256-byte data blocks, each data block is encrypted using AES-256, and a 128-bit session key is generated at the same time. The session key is encrypted with the device's RSA public key and transmitted together with the data. The device verifies the legitimacy of the data source through zero-knowledge proof, decrypts the session key with the RSA private key, and then decrypts the data block with the session key. During the transmission process, the COD data stream is marked as high priority, and the QoS mechanism ensures that it is transmitted first when the network is congested. The MQTT protocol sets the QoS level to 1 to ensure that the data is transmitted at least once. When the network delay is detected to exceed 200ms, the self-organizing network algorithm automatically switches to the backup link. Network slicing technology reserves 2Mbps bandwidth resources for the COD data stream to ensure the stability of data transmission. Through the above mechanisms, the safe and reliable transmission of sewage treatment equipment control data can be ensured even in a complex network environment.

[0030] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Extract five process parameters, namely, target influent COD concentration, target dissolved oxygen content, target sludge return ratio, target MBBR carrier filling rate, and target membrane module transmembrane pressure difference, from the equipment communication link, and obtain the operation characteristic sequence of the biochemical treatment system through multidimensional correlation processing; (2) Detect hydraulic load fluctuations, calculate water quality index deviations, and analyze biofilm formation characteristics of biochemical treatment system operation characteristic sequences. After data screening and processing, obtain biochemical reactor status characteristic data. (3) Based on the calculation of biochemical degradation efficiency, the parameter threshold is determined, the treatment capacity is evaluated, and the sludge activity is analyzed for the state characteristic data of the biochemical reactor. Through feature extraction processing, the operation evaluation results of the biochemical treatment unit are generated; (4) Dynamically monitor the four process indicators of nitrification rate, phosphorus removal efficiency, dissolved oxygen concentration and sludge concentration in the operation evaluation results of the biochemical treatment unit, identify abnormal operating conditions in combination with the activity characteristics of the microbial community, and generate process diagnostic data; (5) The process diagnostic data are graded according to the three core process units of biochemical reaction, sludge sedimentation and membrane separation, and the operating status is evaluated by integrating the empirical parameters of sewage treatment to obtain the process performance indicators; (6) Through the performance evaluation of the biochemical-membrane separation coupling system, the process performance indicators are comprehensively analyzed and risk warning judgments are made, and an equipment operation status report is generated.

[0031] Specifically, five key process parameters are extracted from the equipment communication link: target influent COD concentration (mg / L), target dissolved oxygen content (mg / L), target sludge return ratio (%), target MBBR carrier filling rate (%) and target membrane module transmembrane pressure difference (kPa). The MBBR carrier filling rate refers to the percentage of the volume of the suspended carrier in the biochemical reactor to the effective volume of the reactor, which is an important indicator reflecting the growth state of the biofilm. The transmembrane pressure difference of the membrane module reflects the degree of membrane contamination and directly affects the membrane separation efficiency. These five parameters are processed through a multidimensional correlation matrix to establish the mutual influence relationship between the parameters, thereby obtaining the operation characteristic sequence of the biochemical treatment system.

[0032] The hydraulic load fluctuation detection is calculated using a mathematical model: Wherein, HLF is the hydraulic load fluctuation index, is the water inlet flow rate at the i-th moment (m³ / h), is the average flow rate, To design the maximum flow rate, is the time weight coefficient, is the flow fluctuation influence coefficient, and n is the number of sampling points in the detection period.

[0033] The calculation of biodegradation efficiency is based on the following model: Among them, BDE is the biodegradation efficiency index, is the influent concentration of the jth pollutant, is the outlet water concentration, is the temperature correction coefficient, is the pollutant weight coefficient, is the biodegradation characteristic coefficient, and m is the number of target pollutant types. In practical applications, the calculation of water quality index deviation mainly focuses on core indicators such as COD removal rate and ammonia nitrogen removal rate. The analysis of biofilm characteristics of biological fillers evaluates the biological treatment capacity by observing the growth of biofilm on the carrier surface. For sludge activity analysis, indicators such as respiration rate and specific growth rate are used for comprehensive evaluation.

[0034] Nitrification rate and phosphorus removal efficiency are important indicators for evaluating the effect of biochemical treatment. The activity characteristics of microbial communities include: nitrifying bacteria activity, denitrifying bacteria activity, polyphosphate bacteria activity, etc. Abnormal operating condition identification mainly targets phenomena such as sudden drop in dissolved oxygen, sludge swelling, and foamy sludge. This information is summarized to form process diagnostic data, which provides a basis for subsequent control decisions. The generation of process performance indicators requires consideration of the coordinated operation of three core process units. The biochemical reaction unit mainly evaluates the COD removal effect and nitrogen and phosphorus conversion efficiency; the sludge sedimentation unit focuses on sludge sedimentation performance and reflux effect; the membrane separation unit focuses on monitoring membrane flux and membrane fouling.

[0035] For example, in a sewage treatment equipment with a treatment scale of 1500 tons / day, in continuous operation monitoring, the influent COD concentration is 400mg / L, the dissolved oxygen set value is 2.5mg / L, the sludge return ratio is 120%, the MBBR carrier filling rate is 35%, and the transmembrane pressure difference of the membrane module is 30kPa. The calculation of the hydraulic load fluctuation detection model shows that HLF=0.15, indicating that the hydraulic load fluctuation is within a reasonable range. The calculation result of biochemical degradation efficiency BDE=0.85 meets the expected treatment requirements. Dynamic monitoring of dissolved oxygen and sludge concentration shows that the DO concentration fluctuates in the range of 2.3-2.7mg / L, and the MLSS concentration is maintained at 3500-4000mg / L. Based on these data, the generated operation status report shows that the equipment is in normal operation, but the transmembrane pressure difference of the membrane module has an upward trend, and it is recommended to increase the frequency of chemical cleaning appropriately.

[0036] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Analyze the operating conditions of the equipment operation status report, correlate and compare the three key parameters of dissolved oxygen in the biochemical reaction zone, MBBR filler loading rate, and membrane flux, and obtain the process operation parameter set; (2) Establish aeration volume control rules based on the process operation parameter set, calculate aeration intensity based on two key indicators: nitrification reaction rate and sludge load rate, and generate aeration control parameters; (3) The aeration control parameters are optimized by matching the membrane module backwash cycle, adjusting the biochemical pool reflux ratio, and setting the chemical cleaning frequency to generate a set of process control rules; (4) Dynamically calculate the three indicators of influent water quality fluctuation, biochemical degradation efficiency, and membrane fouling through the process control rule set to generate an equipment operation control plan; (5) The equipment operation control plan is decomposed into three process modules: biochemical treatment unit, membrane separation unit, and sludge return unit. The parameters are verified in conjunction with the equipment operation boundary constraints to obtain a distributed control strategy. (6) Encode control instructions, prioritize them, schedule their execution, and output device control instructions for distributed control strategies.

[0037] Specifically, the equipment operation status report includes three key parameters: dissolved oxygen in the biochemical reaction zone, MBBR filler loading rate, and membrane flux. Among them, dissolved oxygen is the core indicator that affects the biological treatment effect, reflecting the living environment of microorganisms in the biochemical reactor; MBBR filler loading rate indicates the biomass attached to the carrier per unit volume, which directly affects the treatment capacity; membrane flux indicates the amount of filtered water per unit membrane area, which is an important indicator for measuring membrane separation efficiency. These three parameters are compared and analyzed through the correlation matrix to establish the mutual influence relationship between the parameters and form a process operation parameter set.

[0038] Aeration intensity calculation is based on the following model: in, is the aeration intensity control index, is the nitrification reaction rate in the kth period (kgN / m³·d), is the sludge loading rate, is the dissolved oxygen concentration (mg / L), is the time period weight coefficient, is the temperature correction coefficient, is the sludge activity coefficient, and p is the number of time periods in the control cycle.

[0039] Aeration control parameters and membrane component operating parameters need to be optimized collaboratively. The membrane component backwash cycle is determined according to the changing trend of the transmembrane pressure difference, and the chemical cleaning frequency is calculated based on the membrane fouling rate. The adjustment of the biochemical pool reflow ratio needs to take into account the influent load and nitrification reaction requirements. The collaborative optimization of the three parameters forms a complete set of process control rules. The process control rule set dynamically compensates for the three indicators of influent water quality fluctuations, biochemical degradation efficiency, and membrane fouling degree. When the influent COD concentration suddenly increases, increase the aeration volume and reflow ratio; when the biochemical degradation efficiency decreases, adjust the MBBR carrier filling rate and dissolved oxygen concentration; when membrane fouling intensifies, increase the backwash frequency and cleaning intensity. Through these compensation measures, a complete equipment operation and control plan is generated.

[0040] The operation and control scheme needs to be reasonably allocated among the three process units. The biochemical treatment unit mainly controls the dissolved oxygen concentration and the reflux ratio; the membrane separation unit controls the membrane flux and cleaning parameters; the sludge reflow unit controls the reflux volume and the residual sludge discharge. The control parameters of each unit have their operating boundary constraints, such as the dissolved oxygen concentration between 2-4 mg / L, the membrane flux not exceeding 25 L / m²·h, and the reflux ratio within the range of 50%-150%. The generation of control instructions follows strict priority and execution sequence. Equipment protection instructions have the highest priority, such as membrane component protection instructions; process optimization instructions are second, such as dissolved oxygen adjustment instructions; daily operation instructions have the lowest priority, such as regular sludge discharge instructions. The instruction execution sequence should take into account the continuity of the process to avoid excessive parameter adjustments.

[0041] For example, a MBBR-MBR sewage treatment equipment is in operation, and the equipment operation status report shows: dissolved oxygen concentration 2.1mg / L, MBBR filler load rate 5.2kgCOD / m³·d, membrane flux 18L / m²·h. Based on these parameters, the aeration intensity control index AIC=0.78 is calculated, indicating that the aeration volume needs to be appropriately increased. Through collaborative optimization, new operating parameters are determined: the aeration volume is increased to 180m³ / h, the membrane component backwash cycle is adjusted to 2 minutes of backwashing every 45 minutes of operation, and the biochemical pool reflow ratio is maintained at 120%. After these parameters are checked by the operation boundary, they are sorted in the order of "membrane protection priority, biochemical treatment optimization, and energy consumption control optimization", and the corresponding control instruction sequence is generated, and finally the stable operation of the equipment is achieved.

[0042] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) The equipment control instructions are parsed according to the three levels of aeration system control, membrane module cleaning control, and sludge return control, and the field control parameter set is obtained through protocol conversion processing; (2) Arrange the equipment response timing according to the field control parameter set, divide the execution priority according to the equipment operation status, and generate the instruction execution scheduling plan; (3) Perform boundary restriction verification, conflict detection and determination, and safety constraint analysis on the control parameters in the instruction execution scheduling plan, and generate a control instruction sequence through logical verification processing; (4) Monitor the equipment execution status based on the control instruction sequence, collect and analyze the three key parameters of aeration intensity, membrane flux, and reflux ratio in real time, and obtain equipment response data; (5) Classify and sort the equipment response data according to the process unit, and generate feedback data stream through data compression and timestamp marking; (6) Perform data integrity check, transmission delay analysis, and response deviation calculation on the feedback data stream, and establish a control feedback data link through data link processing.

[0043] Specifically, the equipment control instructions are first parsed according to three levels: the aeration system control level is responsible for the precise regulation of dissolved oxygen concentration and aeration volume; the membrane module cleaning control level handles membrane flux control and backwashing parameters; the sludge return control level manages sludge concentration and return ratio regulation. The control instructions of each level need to be converted to a protocol, converting high-level control instructions into specific equipment execution parameters, such as converting dissolved oxygen control instructions into actual control quantities such as blower frequency and valve opening. When scheduling the execution sequence for the converted field control parameter set, the continuity of the process and the equipment response characteristics need to be considered. For example, the adjustment of the aeration system needs to consider the start and stop time of the blower, and the backwashing of the membrane module needs to be coordinated with the water production process. The division of execution priority is based on three principles: equipment safety protection has the highest priority, process parameter optimization is the second, and energy consumption control optimization has the lowest priority. In actual applications, when a sudden increase in membrane pressure difference occurs, the membrane protection instruction will be executed first, temporarily interrupting the ongoing process optimization adjustment.

[0044] The logic check of the control instruction includes three aspects: boundary limit verification ensures that the control parameters are within the allowable range of the equipment, such as the aeration volume does not exceed the rated air volume of the blower; conflict detection and judgment avoid mutual interference between parameter adjustments, such as preventing membrane backwashing and chemical cleaning from being carried out at the same time; safety constraint analysis focuses on equipment protection requirements, such as the maximum transmembrane pressure difference limit of the membrane assembly. After these checks are passed, a complete control instruction sequence is formed, which clearly stipulates the specific values ​​and execution order of each parameter adjustment. Equipment execution status monitoring mainly targets three key parameters: aeration intensity reflects the oxygenation status of biochemical treatment, which is collected in real time through online monitoring of dissolved oxygen and air volume meter; membrane flux represents the filtration efficiency of the membrane separation unit, which is monitored by flow meter and pressure sensor; reflux ratio is calculated by reflux pump speed and flow rate. The collection frequency of these parameters varies depending on the monitored object. Dissolved oxygen is collected once a minute, membrane flux is recorded every 5 minutes, and reflux ratio is updated every 10 minutes.

[0045] The classification and arrangement of equipment response data is carried out according to the process unit. The data of the biochemical treatment unit includes parameters such as dissolved oxygen, MLSS, and temperature; the membrane separation unit records information such as membrane flux, transmembrane pressure difference, backwash frequency, etc.; the sludge return unit contains data such as return volume and residual sludge discharge. Data compression uses a differential storage method to record only data points where the parameter change exceeds the set threshold, and adds an accurate timestamp mark to each data point. The processing of the feedback data stream includes three steps: data integrity check ensures data integrity through a cyclic redundancy check (CRC) algorithm; transmission delay analysis calculates the time difference from data acquisition to reception, which is used to evaluate the real-time control; response deviation calculation compares the difference between the actual operating parameters and the set values ​​to evaluate the control effect.

[0046] For example, a MBBR-MBR device with a treatment scale of 800 tons / day received a set of control instructions during operation: adjust the dissolved oxygen to 2.5mg / L, reduce the membrane flux to 20L / m²·h, and increase the reflow ratio to 130%. This set of instructions is converted into specific parameters after hierarchical analysis: blower frequency 45Hz, membrane suction pump frequency 38Hz, and reflow pump frequency 42Hz. According to the equipment response characteristics, the execution sequence is arranged as follows: first adjust the reflow pump (t=0s), adjust the blower after the reflow is stable (t=30s), and finally adjust the membrane suction pump (t=60s). During the execution process, real-time monitoring data showed that the dissolved oxygen gradually increased from 2.0mg / L to 2.4mg / L, the membrane flux decreased from 22L / m²·h to 20.2L / m²·h, and the reflow ratio reached 128%. These response data are compressed (only data points with a change of more than 5% are retained) and timestamped to generate feedback data streams. Through data analysis, it was found that the average response time of control instructions was 45 seconds, and the parameter adjustment deviation was within the range of ±5%, achieving the expected control effect.

[0047] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) The data in the control feedback data link is classified and sorted in multiple dimensions, and the equipment operation optimization parameter set is obtained through energy consumption index calculation, processing efficiency statistics, and operation stability analysis; (2) The three core parameters of aeration volume, membrane flux and reflux ratio in the equipment operation optimization parameter set are dynamically corrected through the feedback compensation algorithm to generate a process parameter optimization plan; (3) Match the operating conditions with the process parameter optimization plan, conduct verification and analysis based on three key indicators: influent water quality fluctuation, hydraulic load change, and sludge characteristic change, and generate a process operation optimization strategy; (4) Based on the process operation optimization strategy, the equipment operation boundary conditions are constrained and updated, and the parameters are adjusted through biochemical degradation efficiency calculation, membrane module operation status evaluation, and system energy consumption analysis to obtain equipment operation control parameters; (5) Reconstruct the control instructions of the equipment operation control parameters according to the three process modules of biochemical treatment unit, membrane separation unit and sludge return unit to generate the equipment optimization control instruction set; (6) The equipment optimization control instruction set is remotely issued and processed, and closed-loop control is performed through control instruction verification, execution status confirmation, and operation effect feedback to complete the remote optimization control of the sewage treatment equipment.

[0048] Specifically, energy consumption indicators mainly include total power consumption, energy consumption per unit of treated water, and energy consumption proportion of each equipment unit. Treatment efficiency statistics focus on indicators such as effluent water quality compliance rate, pollutant removal rate, and equipment treatment load. Operation stability analysis includes the fluctuation range of key parameters, equipment failure frequency, maintenance requirements, and other situations. After integration and analysis, these data form a complete set of equipment operation optimization parameters. The feedback compensation algorithm mainly makes dynamic adjustments to core operating parameters. When there is a deviation between the actual operating parameters and the target values, the correction amount is calculated through proportional integral adjustment. The adjustment of aeration volume takes into account the trend of dissolved oxygen changes and oxygen demand changes; membrane flux correction is based on the rate of change of transmembrane pressure difference; and the adjustment of reflux ratio needs to consider the changing law of sludge concentration. These correction parameters together constitute the process parameter optimization plan.

[0049] The generation of process operation optimization strategy needs to consider multiple key factors. Fluctuation of influent water quality directly affects the treatment load, and process parameters need to be adjusted in time; changes in hydraulic load will affect the hydraulic retention time, and membrane flux and return flow need to be adjusted accordingly; changes in sludge characteristics require adjustment of aeration strategy and sludge discharge cycle. Comprehensive analysis of these factors ensures the adaptability of process operation. Process operation optimization strategy requires parameter adjustment under the boundary conditions of equipment operation. The calculation of biochemical degradation efficiency is based on the removal rate of indicators such as influent and effluent COD and ammonia nitrogen; the operating status of membrane components mainly evaluates the membrane fouling rate and cleaning effect; system energy consumption analysis focuses on the total energy consumption per unit of treated water and the energy consumption distribution of each unit. Based on these analysis results, the operating parameters are adjusted to meet the treatment requirements and ensure economic operation.

[0050] The reconstruction of control instructions needs to be targeted at the characteristics of different process units. The instructions for the biochemical treatment unit include aeration volume adjustment, dissolved oxygen control, MBBR filler addition, etc.; the instructions for the membrane separation unit involve membrane flux adjustment, backwash cycle setting, chemical cleaning arrangement, etc.; the sludge return unit includes return volume adjustment, residual sludge discharge, etc. These instructions are organized according to priority and execution order to form a complete control instruction set.

[0051] For example, during the operation of a certain MBBR-MBR device, data analysis revealed that the energy consumption per unit of treated water was 0.45kWh / m³, the COD removal rate was 88%, and the dissolved oxygen fluctuation range was 2.0-2.8mg / L. Based on these data, the feedback compensation algorithm calculated that the aeration volume needed to be adjusted from 180m³ / h to 165m³ / h, the membrane flux increased from 20L / m²·h to 22L / m²·h, and the reflow ratio was maintained at 120%. Considering that the influent COD concentration fluctuates between 350-450mg / L, the daily treated water volume fluctuates by ±15%, and the sludge concentration is stable at around 4000mg / L, these adjustment parameters are verified and confirmed to be feasible. The control instructions finally generated include: the blower frequency is reduced to 42Hz, the membrane suction pump frequency is increased to 40Hz, and the reflow pump maintains the existing frequency. After this set of instructions was executed, the equipment operation results showed that the unit energy consumption was reduced to 0.42kWh / m³, and the processing efficiency was maintained at the original level, achieving the goal of optimized control.

[0052] The above describes the remote control method for sewage treatment equipment in the embodiment of the present application. The following describes the remote control system for sewage treatment equipment in the embodiment of the present application. Figure 2 In the embodiments of the present application, one embodiment of a remote control system for a sewage treatment device includes: The acquisition module is used to collect and preprocess the operating parameters and water quality indicators of sewage treatment equipment in multiple dimensions, and generate a standard data package for the sewage treatment system; An encryption module, used to perform communication encryption and identity authentication processing on the sewage treatment system standard data packet, and to establish a device communication link; An analysis module, used to perform intelligent analysis and status diagnosis processing on the data in the device communication link, and generate a device operation status report; A control module, used to generate a control strategy and perform parameter matching processing according to the equipment operation status report, and output equipment control instructions; A monitoring module, used for performing hierarchical analysis and execution status monitoring of the device control instructions, and establishing a control feedback data link; The optimization module is used to perform remote optimization processing based on the data in the control feedback data link to achieve remote intelligent control of the sewage treatment equipment.

[0053] Through the coordinated cooperation of the above-mentioned components, through multi-dimensional collection and data preprocessing of sewage treatment equipment operating parameters and water quality indicators, comprehensive monitoring and data standardization of equipment operating status are achieved, providing a reliable data basis for subsequent analysis and decision-making; communication encryption and identity authentication processing of sewage treatment system standard data packets effectively ensure the security and reliability of data transmission, and prevent unauthorized access and data tampering; intelligent analysis and status diagnosis of data in the equipment communication link are carried out to accurately identify equipment operation anomalies and performance deviations, and discover potential faults in advance; control strategy generation and parameter matching are carried out based on equipment operation status reports, achieving precise regulation and optimization of process parameters and ensuring the stability of treatment effects; through hierarchical analysis and execution status monitoring of equipment control instructions, a complete control feedback mechanism is established to ensure accurate execution and timely feedback of control instructions; remote optimization processing is carried out based on the data in the control feedback data link, dynamic optimization and adaptive adjustment of equipment operating parameters are achieved, and equipment operation efficiency and economy are improved.

[0054] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer executes the steps of the remote control method for sewage treatment equipment.

[0055] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0056] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0057] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application 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 embodiments of the present application.

Claims

1. A remote control method for sewage treatment equipment, characterized in that: The remote control method for sewage treatment equipment comprises: Carry out multi-dimensional collection and data preprocessing of the operating parameters and water quality indicators of sewage treatment equipment to generate a standard data package for the sewage treatment system; Perform communication encryption and identity authentication processing on the sewage treatment system standard data packet to build a device communication link; Perform intelligent analysis and status diagnosis processing on the data in the device communication link to generate a device operation status report; Generate control strategy and perform parameter matching processing according to the equipment operation status report, and output equipment control instructions; Perform hierarchical analysis and execution status monitoring on the device control instructions, and establish a control feedback data link; Remote optimization processing is performed based on the data in the control feedback data link to achieve remote intelligent control of the sewage treatment equipment.

2. The remote control method for sewage treatment equipment according to claim 1, characterized in that: The multi-dimensional collection and data preprocessing of the operating parameters and water quality indicators of the sewage treatment equipment to generate a standard data package for the sewage treatment system includes: Multiple groups of water quality sensors are used to collect COD concentration, ammonia nitrogen and pH value at the water inlet in real time to obtain the original water inlet parameter data; The operating status of the biochemical treatment unit of the sewage treatment equipment is continuously monitored through dissolved oxygen sensors and suspended solids sensors to obtain biochemical treatment process data; Perform outlier elimination and data standardization processing on the original influent parameter data and the biochemical treatment process data to generate a preprocessed data set; Performing data correlation analysis and trend feature extraction on the preprocessed data set by a time series analysis algorithm to obtain a data feature set; Perform data packaging and format conversion processing according to the data feature set to construct a data transmission structure; The data transmission structure is verified for integrity and analyzed for redundancy through a data verification algorithm, and the sewage treatment system standard data packet is output.

3. The remote control method for sewage treatment equipment according to claim 1, characterized in that: The communication encryption and identity authentication processing is performed on the sewage treatment system standard data packet to build a device communication link, including: AES-256 and RSA dual encryption algorithms are used to perform block encryption processing through a hybrid encryption mechanism, and the standard data packet of the sewage treatment system is processed in conjunction with session key generation technology to obtain a multi-level encrypted data stream; Combining zero-knowledge proof with digital signature technology, the identity authentication algorithm completes the two-way verification and integrity check of the multi-level encrypted data stream, and performs permission classification processing at the same time to obtain a security authentication data packet; Based on the QoS service quality assurance mechanism, combined with the layered protocol stack technology, the TCP / IP protocol parameters in the security authentication data packet are adaptively configured and optimized to generate multi-dimensional communication configuration parameters; The optimized MQTT protocol and intelligent fault warning mechanism are used to dynamically detect the network status of the multi-dimensional communication configuration parameters, and the delay evaluation algorithm is integrated to complete the quality analysis and generate dynamic link status feature information; Based on the load balancing principle, the path selection and resource scheduling are completed for the dynamic link state characteristic information through the self-organizing network algorithm, and the transmission control is performed in combination with the bandwidth optimization technology to obtain an adaptive transmission control strategy; By integrating network slicing technology and software-defined network architecture, secure channel construction and resource isolation processing are performed according to the adaptive transmission control strategy to establish the device communication link.

4. The remote control method for sewage treatment equipment according to claim 1, characterized in that: The intelligent analysis and status diagnosis processing of the data in the device communication link to generate a device operation status report includes: Extracting five process parameters, namely, target influent COD concentration, target dissolved oxygen content, target sludge return ratio, target MBBR carrier filling rate, and target membrane module transmembrane pressure difference, from the equipment communication link, and obtaining the operation characteristic sequence of the biochemical treatment system through multi-dimensional correlation processing; The hydraulic load fluctuation detection, water quality index deviation calculation, and biofilm formation characteristic analysis are performed on the operation characteristic sequence of the biochemical treatment system, and the state characteristic data of the biochemical reactor is obtained through data screening and processing; Based on the calculation of biochemical degradation efficiency, parameter threshold determination, treatment capacity evaluation, and sludge activity analysis are performed on the state characteristic data of the biochemical reactor, and a biochemical treatment unit operation evaluation result is generated through feature extraction processing; Dynamically monitor the four process indicators of nitrification rate, phosphorus removal efficiency, dissolved oxygen concentration and sludge concentration in the operation evaluation results of the biochemical treatment unit, identify abnormal operating conditions in combination with the activity characteristics of the microbial community, and generate process diagnostic data; The process diagnostic data is graded according to the three core process units of biochemical reaction, sludge sedimentation and membrane separation, and the operating status is evaluated by integrating the empirical parameters of sewage treatment to obtain the process performance indicators; Through the performance evaluation of the biochemical-membrane separation coupling system, the process performance indicators are comprehensively analyzed and risk warning judgments are made, and the equipment operation status report is generated.

5. The remote control method for sewage treatment equipment according to claim 4, characterized in that: The generating of control strategy and parameter matching processing according to the equipment operation status report and outputting equipment control instructions include: The operating condition analysis is performed on the equipment operating status report, and three key parameters, namely, dissolved oxygen in the biochemical reaction zone, MBBR filler loading rate, and membrane flux, are correlated and compared to obtain a process operating parameter set; Based on the process operation parameter set, an aeration volume control rule is established, and the aeration intensity is calculated by combining the two key indicators of nitrification reaction rate and sludge load rate to generate aeration control parameters; The aeration control parameters are optimized by three processes: membrane module backwash cycle matching, biochemical pool reflux ratio adjustment, and chemical cleaning frequency setting, to generate a process control rule set; Through the process control rule set, dynamic compensation calculation is performed on the three indicators of influent water quality fluctuation, biochemical degradation efficiency, and membrane pollution degree to generate an equipment operation control plan; The equipment operation control scheme is decomposed into three process modules: biochemical treatment unit, membrane separation unit and sludge return unit, and verified with equipment operation boundary constraints to obtain a distributed control strategy; Control instruction encoding, priority sorting, and execution timing scheduling are performed according to the distributed control strategy, and the device control instruction is output.

6. The remote control method for sewage treatment equipment according to claim 1, characterized in that: The step of performing hierarchical analysis and execution status monitoring on the device control instructions and establishing a control feedback data link includes: The equipment control instructions are parsed according to three levels: aeration system control, membrane module cleaning control, and sludge return control, and a field control parameter set is obtained through protocol conversion processing; Arrange the equipment response timing according to the field control parameter set, divide the execution priority according to the equipment operation status, and generate the instruction execution scheduling plan; Perform boundary restriction verification, conflict detection and determination, and safety constraint analysis on the control parameters in the instruction execution scheduling scheme, and generate a control instruction sequence through logic verification processing; Based on the control instruction sequence, the equipment execution status is monitored, and the three key parameters of aeration intensity, membrane flux and reflux ratio are collected and analyzed in real time to obtain equipment response data; Classify and sort the equipment response data according to the process unit division, and generate a feedback data stream through data compression processing and timestamp marking; Data integrity check, transmission delay analysis, and response deviation calculation are performed on the feedback data stream, and the control feedback data link is established through data link processing.

7. The remote control method for sewage treatment equipment according to claim 1, characterized in that: The remote optimization processing is performed based on the data in the control feedback data link to realize remote intelligent control of the sewage treatment equipment, including: The data in the control feedback data link is classified and sorted in multiple dimensions, and an equipment operation optimization parameter set is obtained through energy consumption index calculation, processing efficiency statistics, and operation stability analysis; Dynamically correct the three core parameters of aeration volume, membrane flux and reflux ratio in the equipment operation optimization parameter set through a feedback compensation algorithm to generate a process parameter optimization plan; Match the operating conditions with the process parameter optimization scheme, conduct verification and analysis based on three key indicators: influent water quality fluctuation, hydraulic load change, and sludge characteristic change, and generate a process operation optimization strategy; Based on the process operation optimization strategy, the equipment operation boundary conditions are constrained and updated, and the parameters are adjusted through biochemical degradation efficiency calculation, membrane component operation status evaluation, and system energy consumption analysis to obtain equipment operation control parameters; Reconstruct the control instructions of the equipment operation control parameters according to the three process modules of the biochemical treatment unit, the membrane separation unit and the sludge return unit to generate an equipment optimization control instruction set; The optimized control instruction set for the equipment is remotely issued and processed, and closed-loop control is performed through control instruction verification, execution status confirmation, and operation effect feedback to complete the remote optimized control of the sewage treatment equipment.

8. A remote control system for sewage treatment equipment, used to implement the remote control method for sewage treatment equipment according to any one of claims 1 to 7, characterized in that: The remote control system for sewage treatment equipment comprises: The acquisition module is used to collect and preprocess the operating parameters and water quality indicators of sewage treatment equipment in multiple dimensions, and generate a standard data package for the sewage treatment system; An encryption module, used to perform communication encryption and identity authentication processing on the sewage treatment system standard data packet, and to establish a device communication link; An analysis module, used to perform intelligent analysis and status diagnosis processing on the data in the device communication link, and generate a device operation status report; A control module, used to generate a control strategy and perform parameter matching processing according to the equipment operation status report, and output equipment control instructions; A monitoring module, used for performing hierarchical analysis and execution status monitoring of the device control instructions, and establishing a control feedback data link; The optimization module is used to perform remote optimization processing based on the data in the control feedback data link to achieve remote intelligent control of the sewage treatment equipment.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the remote control method for sewage treatment equipment according to any one of claims 1 to 7 is implemented.

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