Anti-backflow intercepting system and control method

Abstract

The invention provides an anti-backflow intercepting system and a control method, and relates to the technical field of city pipe discharge. The method comprises the following steps: firstly, carrying out single-intercepting-well strategy regulation and control through environmental parameters of each intercepting well, and determining a single-strategy state of each intercepting well; afterwards, on the basis of the environmental parameters and the current state of each intercepting well and the single-strategy state of each intercepting well, state compounding is carried out on each intercepting well, and the compound regulation and control state of each intercepting well is determined, so that regulation and control of each intercepting well meet the requirements of environmental parameter changes of each intercepting well; and the requirements of water discharge and stability among a plurality of catch basins are met. Compared with a manual regulation and control operation mode, rapid response of the urban intercepting system is achieved, the accuracy of intercepting well control is guaranteed, the intelligent degree of the intercepting system is improved, and urban drainage is prevented from flowing backwards.

Description

Technical Field

[0001] The invention relates to the technical field of municipal drainage, and in particular to a backflow prevention and interception system and a control method. Background Art

[0002] With the acceleration of urbanization, the pressure on urban drainage systems is increasing. Especially under extreme weather conditions such as heavy rain and floods, traditional backflow prevention and interception systems are often unable to respond to the complex and changeable hydrological environment in a timely and effective manner, resulting in frequent urban waterlogging and backflow problems.

[0003] The existing anti-backflow system mainly relies on preset rules or manual operations to control the opening and closing of interception wells, lacking intelligence and adaptability. When the water level, flow rate or rainfall changes suddenly, the system usually cannot respond in time, and the scheduling is delayed, resulting in the drainage system being overloaded, and river water or rainwater backflowing into the urban drainage pipes, posing a major threat to urban infrastructure and residents' lives. Summary of the invention

[0004] The present invention provides a backflow prevention interception system and a control method, which can realize the rapid response of the urban interception system, improve the intelligence level of the interception system, and prevent the backflow of urban drainage.

[0005] In a first aspect, the present invention provides a control method for a backflow prevention interception system, the method comprising: obtaining environmental parameters and current states of each interception well in the interception system; the environmental parameters include water level, flow velocity and rainfall; based on the environmental parameters of each interception well, performing single interception well strategy control to determine the single strategy state of each interception well; based on the environmental parameters and current states of each interception well, and the single strategy state of each interception well, performing state compounding on each interception well to determine the compound control state of each interception well; based on the compound control state and current state of each interception well, generating control instructions for each interception well; based on the control instructions for each interception well, controlling the opening and closing operations of each interception well.

[0006] In a possible implementation, based on the environmental parameters of each interception well, a single interception well strategy is controlled to determine the single strategy state of each interception well, including: for any interception well, if the water level of the interception well is less than the minimum water level, the single strategy state of the interception well is determined to be a closed state; if the water level of the interception well is greater than or equal to the minimum water level and less than the maximum water level, the single strategy state of the interception well is determined based on the flow rate and rainfall of the interception well; if the water level of the interception well is greater than or equal to the maximum water level, the single strategy state of the interception well is determined to be an open state.

[0007] In one possible implementation, the single-strategy state of the intercepting well is determined based on the flow velocity and rainfall of the intercepting well, including: calculating the flow velocity change rate and the rainfall change rate based on the flow velocity and rainfall of the intercepting well; if the flow velocity change rate is greater than the preset flow velocity, or the rainfall change rate is greater than the preset rainfall, then determining that the single-strategy state of the intercepting well is an open state; if the flow velocity change rate is less than or equal to the preset flow velocity, and the rainfall change rate is less than or equal to the preset rainfall, then determining that the single-strategy state of the intercepting well is a closed state.

[0008] In a possible implementation, based on the environmental parameters and current status of each interception well and the single strategy status of each interception well, the status of each interception well is compounded to determine the compound control status of each interception well, including: based on the current status and single strategy status of each interception well, multiple state combinations of each interception well are determined, wherein each state combination includes one or more interception wells in a modified state; based on the environmental parameters of each interception well and multiple state combinations, the drainage volume and stability corresponding to each state combination are calculated; based on the drainage volume and stability corresponding to each state combination, the optimal state combination is determined; based on the optimal state combination, the compound control status of each interception well is determined.

[0009] In a possible implementation, based on the composite regulation state and current state of each interception well, a control instruction for each interception well is generated, including: based on the composite regulation state and current state of each interception well, determining the interception well whose state has changed; based on the composite regulation state of the interception well whose state has changed, generating a control instruction, the control instruction including identification information and the composite regulation state of the interception well whose state has changed.

[0010] In a second aspect, an embodiment of the present invention provides a control device for an anti-backflow interception system, the control device comprising: a communication module, used to obtain environmental parameters and current states of each interception well in the interception system; the environmental parameters include water level, flow velocity and rainfall; a processing module, used to perform single interception well strategy regulation based on the environmental parameters of each interception well, and determine the single strategy state of each interception well; based on the environmental parameters and current states of each interception well, and the single strategy state of each interception well, perform state compounding on each interception well, and determine the compound regulation state of each interception well; based on the compound regulation state and current state of each interception well, generate control instructions for each interception well; based on the control instructions of each interception well, control the opening and closing operations of each interception well.

[0011] In one possible implementation, the processing module is specifically used to, for any interception well, if the water level of the interception well is less than the minimum water level, determine that the single strategy state of the interception well is a closed state; if the water level of the interception well is greater than or equal to the minimum water level and less than the maximum water level, determine the single strategy state of the interception well based on the flow rate and rainfall of the interception well; if the water level of the interception well is greater than or equal to the maximum water level, determine that the single strategy state of the interception well is an open state.

[0012] In one possible implementation, the processing module is specifically used to calculate the flow velocity change rate and the rainfall change rate based on the flow velocity and rainfall of the intercepting well; if the flow velocity change rate is greater than the preset flow velocity, or the rainfall change rate is greater than the preset rainfall, the single strategy state of the intercepting well is determined to be on; if the flow velocity change rate is less than or equal to the preset flow velocity, and the rainfall change rate is less than or equal to the preset rainfall, the single strategy state of the intercepting well is determined to be off.

[0013] In one possible implementation, the processing module is specifically used to determine multiple state combinations of each interception well based on the current state and single strategy state of each interception well, wherein each state combination includes one or more interception wells in a modified state; based on the environmental parameters of each interception well and multiple state combinations, calculate the drainage and stability corresponding to each state combination; based on the drainage and stability corresponding to each state combination, determine the optimal state combination; based on the optimal state combination, determine the composite control state of each interception well.

[0014] In a third aspect, an embodiment of the present invention provides a backflow prevention and interception system, which includes an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect and any possible implementation method of the first aspect.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program is executed by a processor, the steps of the method described in the first aspect and any possible implementation method of the first aspect are implemented.

[0016] The present invention provides an anti-backflow interception system and control method. The present invention firstly performs single interception well strategy regulation through environmental parameters of each interception well to determine the single strategy state of each interception well; then, based on the environmental parameters and current state of each interception well and the single strategy state of each interception well, the state of each interception well is compounded to determine the compound regulation state of each interception well, which not only ensures that the regulation of each interception well meets the needs of changes in environmental parameters of each interception well, but also meets the needs of drainage volume and stability between multiple interception wells. Compared with the manual regulation operation mode, the present invention not only realizes the rapid response of the urban interception system, but also ensures the accuracy of interception well control, improves the intelligence of the interception system, and prevents urban drainage backflow. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 It is a flow chart of a control method of a backflow prevention interception system provided by an embodiment of the present invention;

[0019] Figure 2 It is a structural schematic diagram of a control device of a backflow prevention interception system provided by an embodiment of the present invention;

[0020] Figure 3 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0022] In the description of the present invention, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" and "plurality" refer to two or more. The words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not limit them to be different.

[0023] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0024] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include other steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0025] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be described through specific embodiments in conjunction with the accompanying drawings of the present invention.

[0026] Traditional backflow prevention systems also have obvious deficiencies in equipment maintenance and fault prevention. Most existing systems rely on regular manual inspections and maintenance, and the monitoring of equipment operating status often lags behind the actual occurrence of faults, which can easily lead to sudden equipment failures and affect the normal operation of the entire system. In addition, when certain key equipment, such as electric valves or automatic gates, fail, the system often lacks an emergency switching mechanism for backup equipment, resulting in a weakening of the backflow prevention capability or complete failure. Sudden equipment failures not only threaten the backflow prevention function, but may also cause a larger range of urban drainage paralysis.

[0027] In the long-term operation of the system, it is usually difficult for traditional backflow prevention systems to provide effective feedback and optimization of system performance based on historical data. With the accumulation of data, the potential laws and influencing factors in the operation of the system gradually emerge, but the existing technology lacks the ability to fully utilize historical monitoring data for in-depth analysis, and is unable to automatically adjust and optimize system parameters based on these data. Therefore, it is difficult for existing systems to achieve self-improvement and optimization in a data-driven manner. In particular, when faced with changing environmental conditions, the system's scheduling and maintenance strategies are often still lagging behind and unable to adapt to the needs of different seasons or climate changes. To this end, the present invention provides an anti-backflow interception system that solves the problems of intelligent scheduling, equipment failure prevention and maintenance in existing interception systems.

[0028] The anti-backflow interception system includes: a monitoring system module for real-time monitoring of environmental parameters such as water level, flow rate, rainfall, etc.; a control system module for controlling the opening and closing operations of interception wells; an intelligent scheduling submodule in the control system module adopts a deep Q network algorithm to optimize the opening and closing scheduling of multiple interception wells based on real-time monitoring data through a reinforcement learning mechanism; an automatic maintenance and fault diagnosis system module for system self-inspection and fault diagnosis; and a data processing and feedback system module for data analysis and system optimization.

[0029] Preferably, the intelligent scheduling submodule includes: a scheduling server, which is used to process the water level, flow rate and rainfall data from the monitoring system module in real time; an actuator unit, connected to the interception well, which is used to control the opening and closing of the interception well according to the scheduling strategy; a state storage unit, which is used to store the current state data of the interception well and historical scheduling data; a decision engine, which is used to generate a scheduling strategy based on a deep Q network algorithm and control the actuator unit.

[0030] Preferably, the decision engine of the deep Q network algorithm generates action a based on the current state s, and optimizes the cumulative reward of each interception well through the following update formula:

[0031]

[0032] Among them, s is the current environmental parameters of the interception well, such as water level, flow rate, rainfall, a is the control instruction for the interception well, such as opening or closing), r is the effect after the action is executed, such as reducing drainage pressure or preventing backflow, s′ is the system state after the action is executed, α is the speed of controlling the Q value update, and γ balances the weight of immediate reward and future reward;

[0033] The decision engine updates the Q value based on the above formula and gradually optimizes the system's control strategy for each interception well. This algorithm can adaptively generate scheduling strategies in a dynamic and changing environment to achieve drainage coordination and backflow prevention between interception wells.

[0034] Preferably, the scheduling server periodically updates the status data of the interception well based on monitoring data from water level, flow rate and rainfall, and transmits the data to the decision engine.

[0035] Preferably, the actuator unit includes an electric valve and an automatic gate, and the electric valve is connected to the dispatch server through a remote controller and responds to the dispatch instruction in real time to perform opening and closing operations.

[0036] Preferably, the automatic maintenance and fault diagnosis system module includes: a self-test submodule for periodically detecting the status of sensors and control devices; a fault warning submodule for identifying potential failure modes of equipment and issuing warning signals; and a maintenance log submodule for recording the maintenance status and fault data of the equipment.

[0037] Preferably, the self-check submodule detects the device status through an intelligent fault diagnosis algorithm, and the fault warning submodule includes a backup device switching unit, which automatically switches to the backup device when a fault is detected.

[0038] Preferably, the self-check submodule in the automatic maintenance and fault diagnosis system module adopts a predictive maintenance algorithm, which is based on a time series model and predicts the possibility of equipment failure through a recursive neural network.

[0039] Preferably, the calculation formula of the predictive maintenance algorithm is:

[0040] h t =σ(W h h t-1 +W x x t +b h );

[0041] Among them, h t is the system’s memory and prediction characteristics of the current device status, h t-1 To record the historical information of the previous status of the device, x t is the sensor data at the current moment, such as water level, flow rate, opening and closing response time, W h and W x is the weight matrix, which controls the influence of historical state and current input on prediction, and b h It is used to adjust the flexibility of the prediction results. σ is the activation function, which increases the model's ability to express nonlinear relationships.

[0042] When the RNN model predicts that a valve is about to fail (for example, the opening and closing time continues to increase and exceeds the threshold), the system will issue a maintenance reminder. In addition, if the equipment is predicted to fail (for example, the torque of a gate does not meet the standard), the system will automatically switch to the backup equipment to ensure the continuous operation of the interception well.

[0043] Scheduling optimization of interception wells: The system will dynamically adjust the frequency of use and task allocation of interception wells based on the results of predictive maintenance. For example, if the valve of a certain interception well is about to fail, the system can reduce the task scheduling of the interception well, reduce the load, and increase the frequency of use of other healthy equipment to ensure that the overall drainage function is not affected.

[0044] For example, an electric valve in a city's backflow prevention system gradually experienced a delay, with its opening and closing time extended from the normal 1 second to 1.5 seconds and 2 seconds. The system collects the valve's operating data through sensors and inputs it into the RNN model.

[0045] Current input x t Includes: current opening and closing time and feedback value of the torque sensor.

[0046] History Status t-1 Includes: Time series data of previous operations.

[0047] After comprehensive analysis by the RNN model, it was found that the opening and closing time showed an accelerating trend, and it was predicted that the next operation might reach 3 seconds, which was beyond the allowable range of the system.

[0048] When the allowable range is exceeded, the system will trigger an early warning and it is recommended to inspect or replace the valve. If the maintenance cannot be completed in time, the spare valve will take over the operation task to ensure uninterrupted system operation.

[0049] Preferably, the data processing and feedback system module includes: a historical data storage unit for storing all historical monitoring data; a data analysis engine for analyzing historical data and identifying key factors affecting system operation; and an optimization parameter generation unit for generating optimization control parameters for the system.

[0050] By introducing the deep Q network algorithm, the control system module of the present invention can automatically generate the optimal interception well opening and closing strategy based on real-time monitoring data, which solves the problem of slow response or inappropriate strategy of traditional scheduling methods when facing sudden changes in water level, flow velocity and rainfall, and significantly improves the efficiency and accuracy of backflow prevention. Especially in emergency situations such as heavy rain or sudden floods, the system can automatically adjust the drainage load of each area to prevent the occurrence of backflow.

[0051] Through the predictive maintenance algorithm and intelligent fault diagnosis mechanism, the automatic maintenance and fault diagnosis system module of the present invention can predict possible equipment failures in advance and send out early warning signals, or automatically switch to backup equipment when the equipment fails. The system analyzes the historical operation data of the equipment through time series models and recursive neural networks, accurately predicts the state changes of the equipment, and avoids the situation where the system is shut down or the scheduling is interrupted due to sudden failure of the equipment. By predicting failures in advance and taking preventive measures, the system effectively extends the service life of the equipment and reduces the operational risks caused by sudden failures.

[0052] The data processing and feedback system module of the present invention identifies the key factors that affect the operation of the system through the analysis of historical monitoring data, generates optimized control parameters, and continuously adjusts the system's scheduling strategy to better adapt to changes in actual operation. Through the accumulation of historical data, the system can self-learn, gradually improve scheduling accuracy, optimize equipment maintenance plans, and ensure that the system always maintains the best operating state under different seasons and climate conditions. This mechanism solves the problem that traditional anti-backflow systems are difficult to self-adjust and lack forward-looking optimization when facing long-term and changing environments, ensuring that the system remains efficient and reliable in long-term operation.

[0053] like Figure 1 As shown, an embodiment of the present invention provides a control method for a backflow prevention and interception system. The control method includes steps S101-S105.

[0054] S101, obtaining environmental parameters and current status of each interception well in the interception system.

[0055] In the embodiment of the present application, the environmental parameters include water level, flow rate and rainfall.

[0056] Exemplarily, the embodiments of the present invention can monitor environmental parameters such as water level, flow rate, rainfall, etc. in real time through the monitoring system module.

[0057] Specifically, the module obtains accurate hydrological information through multiple sensor networks deployed in rivers, drainage networks, and rainfall areas. First, the water level sensor is used to monitor the water level changes in rivers and groundwater to ensure that a signal can be issued in time when the water level rises to the warning value. The flow velocity sensor is installed in the drainage pipes and rivers to monitor the changes in water flow speed to determine abnormal conditions of water flow, such as water backflow or a sudden increase in flow rate. The rainfall sensor records the intensity and duration of rainfall in real time to predict the impact of heavy rain on the load of the drainage system.

[0058] The monitoring system module works closely with the control system module to realize intelligent scheduling decisions by transmitting data to the scheduling server of the control system in real time. When the water level, flow rate or rainfall exceeds the preset safety range, the monitoring system will transmit the abnormal data to the control system, triggering the deep Q network algorithm of the intelligent scheduling submodule to generate the optimal interception well opening and closing strategy based on the current hydrological data. In addition, the monitoring system will continue to provide updated status information according to changes in environmental parameters to ensure that the scheduling system can respond to environmental changes in real time and dynamically adjust the operation plan. By receiving monitoring data, the control system can quickly execute the corresponding opening and closing instructions to ensure the timeliness and effectiveness of the system.

[0059] The linkage with the automatic maintenance and fault diagnosis system module is reflected in the monitoring of equipment status. The monitoring system is not only used to collect hydrological data, but also monitors the operating status of the sensor to determine whether it has a fault. Once the sensor data is found to be abnormal or invalid, the monitoring system will immediately send an alarm message to the automatic maintenance module. The automatic maintenance system will detect specific problems through the self-check module and analyze the equipment status in combination with the intelligent fault diagnosis algorithm. If a sensor failure is detected, the system will trigger the backup sensor or perform emergency maintenance operations to ensure the continuity and accuracy of the monitoring data. The normal operation of the monitoring system is an important guarantee for the maintenance system to effectively prevent the expansion of faults.

[0060] In connection with the data processing and feedback system module, the monitoring system module regularly transmits historical monitoring data to the data processing module to provide basic data for subsequent optimization analysis. The data processing system performs cluster analysis and dimensionality reduction processing on historical water level, flow rate, rainfall and other data to extract key factors affecting the operation of the backflow prevention system. Through these analysis results, the monitoring system can obtain feedback optimization suggestions and adjust the monitoring thresholds and warning lines according to different seasons or climate conditions, thereby improving the overall protection capability and flexibility of the system. The monitoring system not only provides real-time data support for current backflow prevention decisions, but also lays a solid foundation for the long-term optimization operation of the system.

[0061] In general, the monitoring system module is the core data source of the entire backflow prevention and interception system. It collects hydrological and environmental parameters in real time to ensure that each module can make the best decision based on accurate real-time information. It forms a complete backflow prevention decision-making and optimization feedback chain through close cooperation with the control system module, automatic maintenance and fault diagnosis system module, and data processing and feedback system module, greatly improving the system's operating efficiency and ability to cope with complex environments.

[0062] S102: Based on the environmental parameters of each interception well, single interception well strategy control is performed to determine the single strategy state of each interception well.

[0063] As a possible implementation manner, step S102 may be specifically implemented as steps S1021 - S1023 .

[0064] S1021. For any interception well, if the water level of the interception well is less than the minimum water level, determine that the single strategy state of the interception well is a closed state.

[0065] S1022: If the water level of the interception well is greater than or equal to the minimum water level and less than the maximum water level, determine the single strategy state of the interception well based on the flow velocity and rainfall of the interception well.

[0066] Exemplarily, the embodiment of the present invention may determine the single strategy state of the interception well based on steps A1-A3.

[0067] A1. Based on the flow velocity and rainfall of the intercepting well, the flow velocity change rate and the rainfall change rate are calculated.

[0068] A2. If the flow velocity change rate is greater than the preset flow velocity, or the rainfall change rate is greater than the preset rainfall, the single strategy state of the interception well is determined to be the open state.

[0069] A3. If the flow velocity change rate is less than or equal to the preset flow velocity, and the rainfall change rate is less than or equal to the preset rainfall, the single strategy state of the intercepting well is determined to be a closed state.

[0070] S1023: If the water level of the interception well is greater than or equal to the maximum water level, determine that the single strategy state of the interception well is an open state.

[0071] S103 , based on the environmental parameters and current status of each interception well and the single strategy status of each interception well, compound the status of each interception well to determine the compound control status of each interception well.

[0072] As a possible implementation manner, step S103 may be specifically implemented as steps S1031 - S1034 .

[0073] S1031. Determine multiple state combinations of each interception well based on the current state and single strategy state of each interception well.

[0074] Each state combination includes one or more interception wells that modify the state.

[0075] S1032. Based on the environmental parameters of each interception well and a plurality of state combinations, the discharge volume and stability corresponding to each state combination are calculated.

[0076] S1033. Determine the optimal state combination based on the displacement and stability corresponding to each state combination.

[0077] S1034. Determine the composite control state of each interception well based on the optimal state combination.

[0078] S104: Generate control instructions for each interception well based on the composite control state and current state of each interception well.

[0079] As a possible implementation manner, step S104 may be specifically implemented as steps S1041 - S1042 .

[0080] S1041. Based on the composite control state and current state of each interception well, determine the interception well whose state has changed.

[0081] S1042: Generate a control instruction based on the composite control state of the interception well whose state has changed, where the control instruction includes identification information of the interception well whose state has changed and the composite control state.

[0082] S105, based on the control instructions of each interception well, controlling the opening and closing operations of each interception well.

[0083] The present invention provides a control method for an anti-backflow interception system, which firstly performs single interception well strategy regulation through environmental parameters of each interception well to determine the single strategy state of each interception well; then, based on the environmental parameters and current state of each interception well and the single strategy state of each interception well, the state of each interception well is compounded to determine the compound regulation state of each interception well, which not only ensures that the regulation of each interception well meets the needs of changes in environmental parameters of each interception well, but also meets the needs of drainage volume and stability between multiple interception wells. Compared with the manual regulation operation mode, the present invention not only realizes the rapid response of the urban interception system, but also ensures the accuracy of interception well control, improves the intelligence of the interception system, and prevents urban drainage backflow.

[0084] Exemplarily, the control system module is used to control the opening and closing operation of the interception well; the intelligent scheduling submodule in the control system module adopts a deep Q network algorithm to optimize the opening and closing scheduling of multiple interception wells based on real-time monitoring data through a reinforcement learning mechanism; the intelligent scheduling submodule includes:

[0085] The dispatching server is used to process the water level, flow rate and rainfall data from the monitoring system module in real time; the dispatching server periodically updates the status data of the interception well according to the monitoring data from the water level, flow rate and rainfall, and transmits the data to the decision engine;

[0086] The actuator unit is connected to the interception well and is used to control the opening and closing of the interception well according to the scheduling strategy; the actuator unit includes an electric valve and an automatic gate. The electric valve is connected to the scheduling server through a remote controller and responds to the scheduling instructions in real time to perform opening and closing operations;

[0087] A state storage unit, used to store the state data of the current interception well and historical scheduling data;

[0088] The decision engine is used to generate a scheduling strategy based on the deep Q-network algorithm and control the actuator unit. The decision engine of the deep Q-network algorithm generates an action a based on the current state s and optimizes the cumulative reward of each interception well through the following update formula:

[0089]

[0090] Among them, s is the current environmental parameters of the interception well, such as water level, flow rate, rainfall, a is the control instruction for the interception well, such as opening or closing), r is the effect after the action is executed, such as reducing drainage pressure or preventing backflow, s′ is the system state after the action is executed, α is the speed of controlling the Q value update, and γ balances the weight of immediate reward and future reward;

[0091] Specifically, first, the intelligent scheduling submodule receives the water level, flow rate and rainfall data from the monitoring system module in real time through the scheduling server. The scheduling server periodically analyzes and processes this data, and updates the status data of each interception well in the system based on the real-time monitored hydrological changes. The scheduling server not only undertakes the task of data processing and monitoring, but is also responsible for transmitting these updated status data to the core decision-making part of the system - the decision engine. Through close integration with the monitoring system module, the scheduling server can accurately capture the changes in the real-time hydrological environment, provide data support for the system, and ensure the timeliness and effectiveness of decision-making.

[0092] The decision engine is the core decision-making unit in the control system module, and uses the deep Q network algorithm (DQN) for scheduling optimization. The DQN algorithm continuously updates and generates the optimal scheduling strategy based on real-time input data through a reinforcement learning mechanism. Specifically, the decision engine generates action a based on the current environmental state (such as water level, flow rate and other parameters), that is, the opening and closing instructions for one or more interception wells.

[0093] The actuator unit is responsible for executing the instructions generated by the scheduling engine, which is used to actually control the opening and closing of the interception well. The actuator unit includes electric valves and automatic gates, which are connected to the scheduling server through a remote controller. When the scheduling server receives the action instructions generated by the decision engine, the actuator unit can respond immediately and perform the corresponding operation. For example, when the water level in a certain area is monitored to rise rapidly, the actuator unit will receive an instruction to close the interception well in the area, thereby preventing the river water from flowing back into the urban drainage network. The high-speed response capability of the actuator unit ensures that the scheduling strategy can be implemented quickly, giving the system good real-time and adaptability.

[0094] The state storage unit is used to store the current state data of the interception well and historical scheduling data. By recording the opening and closing status of each interception well and the results of the scheduling strategy execution, the state storage unit provides historical data support for subsequent analysis and optimization. These data not only help the decision engine to make more accurate scheduling based on past experience, but also serve as a basis for system maintenance and troubleshooting. In addition, the existence of the state storage unit enables the system to save key scheduling information in the event of power outages or short-term communication interruptions, ensuring the stability and continuity of the system.

[0095] The control system module works closely with other modules (such as the monitoring system module, the automatic maintenance and fault diagnosis system module, and the data processing and feedback system module). During the dispatching process, the control system will receive hydrological data from the monitoring system in real time, and generate the optimal dispatching strategy after processing these data through the dispatching server. The actuator unit operates the interception well according to the dispatching instructions to ensure the reasonable regulation of the water flow. In addition, when the monitoring system detects that the sensor or actuator is operating abnormally, the control system will work with the automatic maintenance module to start the maintenance program or switch to the backup equipment to prevent dispatching delays due to faults. At the same time, the control system module will also transmit the historical data of each dispatching operation to the data processing and feedback module for subsequent system optimization analysis to ensure that the system can continuously improve itself based on historical dispatching experience.

[0096] The automatic maintenance and fault diagnosis system module is used for self-inspection and fault diagnosis of the system; the automatic maintenance and fault diagnosis system module includes: a self-inspection submodule, which is used to periodically detect the status of sensors and control devices; the self-inspection submodule detects the equipment status through an intelligent fault diagnosis algorithm, and the fault warning submodule includes a backup equipment switching unit, which automatically switches to the backup equipment when a fault is detected; the self-inspection submodule in the automatic maintenance and fault diagnosis system module adopts a predictive maintenance algorithm, which is based on a time series model and predicts the possibility of equipment failure through a recursive neural network; the calculation formula of the predictive maintenance algorithm is:

[0097] h t =σ(W h h t-1 +W x x t +b h );

[0098] Among them, h t is the system’s memory and prediction characteristics of the current device status, h t-1 To record the historical information of the previous status of the device, x t is the sensor data at the current moment, such as water level, flow rate, opening and closing response time, W h and W xis the weight matrix, which controls the influence of historical state and current input on prediction, and b h It is used to adjust the flexibility of the prediction results. σ is the activation function, which increases the model's ability to express nonlinear relationships.

[0099] The fault warning submodule is used to identify the potential failure modes of the equipment and issue a warning signal.

[0100] The maintenance log submodule is used to record the maintenance status and fault data of the equipment.

[0101] Specifically, the self-check submodule is the core of the automatic maintenance and fault diagnosis system module, which is used to regularly detect the operating status of sensors and control devices in the system. The self-check submodule uses an integrated intelligent fault diagnosis algorithm to perform a comprehensive status check on the equipment and identify whether the equipment has potential faults. The algorithm combines historical operating data with the current equipment status, and detects the normal operating parameters and deviations of the equipment by analyzing the working data of various sensors (such as water level and flow rate sensors) and actuators (such as electric valves and gates). Once the self-check submodule finds that the working status of a device deviates from the normal range, the system will immediately mark the device as a potential fault device and further diagnose the problem.

[0102] The predictive maintenance algorithm is an important technical support for this module. It is based on a time series model and uses a recurrent neural network (RNN) to predict possible future equipment failures. RNN analyzes the historical data of the equipment, captures the long-term trend of changes in the equipment's operating status, and warns of equipment failure risks in advance. RNN can use previous equipment status information and current input data to predict the future equipment's operating status and determine whether the equipment will fail at a certain point in the future. When the predictive maintenance algorithm detects that a device is about to fail, the self-check submodule will immediately issue a maintenance reminder to ensure that the system can take appropriate preventive measures before the failure occurs.

[0103] The fault warning submodule is used to issue a timely warning signal when the equipment status is abnormal or the predictive maintenance algorithm predicts a fault. The warning mechanism is based on the analysis results of the intelligent fault diagnosis algorithm. When the operating status of a certain device deviates from the normal value, or the recursive neural network predicts that the device is about to fail, the fault warning submodule will issue an alarm to the system administrator to prompt the abnormal status of the equipment. In addition, this submodule also includes a backup device switching unit, which is used to automatically switch to the preset backup device when a device failure occurs. For example, when an actuator (such as an electric valve) fails, the backup device switching unit will take over the operating function of the actuator to ensure that the backflow prevention system can continue to operate and avoid system interruption or protection failure due to a single device failure.

[0104] The maintenance log submodule is used to record the maintenance status and fault data of each device. All equipment fault diagnosis, maintenance operations and fault warning records will be archived in detail to form a system maintenance log. The maintenance log submodule provides rich data support for the long-term operation and maintenance management of the equipment through systematic log records. These log data can not only provide a basis for future equipment maintenance plans, but also input maintenance logs and fault data into the data analysis engine through cooperation with the data processing and feedback system module to help optimize the system's maintenance strategy. Through this data feedback mechanism, the maintenance log submodule can help the system continuously improve the maintenance efficiency and diagnostic accuracy of the equipment.

[0105] The automatic maintenance and fault diagnosis system module is closely connected with the monitoring system module and the control system module. When the monitoring system module finds abnormal equipment data, the automatic maintenance system will immediately start the self-check program to conduct a detailed inspection of the equipment status. If the fault warning submodule sends a fault signal, the control system module can automatically switch to the backup equipment after receiving the warning to ensure the continuity and stability of the backflow prevention operation. At the same time, the operation results of the automatic maintenance system module will also be transmitted to the data processing and feedback system module to provide basic data for the long-term optimization and improvement of the system.

[0106] The data processing and feedback system module is used for data analysis and system optimization. The data processing and feedback system module includes: a historical data storage unit for storing all historical monitoring data; a data analysis engine for analyzing historical data and identifying key factors affecting system operation; and an optimization parameter generation unit for generating optimization control parameters for the system.

[0107] Specifically, the historical data storage unit is used to store all historical monitoring data, including environmental parameters such as water level, flow rate, rainfall, and status information of each submodule in the system. This unit is responsible for the data storage of each operation, scheduling, and sensor output during the operation of the system. Through continuous recording, the system can obtain detailed operating conditions under different time and climate conditions. This not only provides sufficient basis for future data analysis, but also provides historical data reference for tracing the source of equipment failures and scheduling optimization. The historical data storage unit can also effectively classify and manage these data, making subsequent data retrieval, analysis, and call more efficient. In addition, the unit works in conjunction with the automatic maintenance and fault diagnosis system module to store equipment status and fault data from the maintenance log, providing rich historical records for system maintenance optimization.

[0108] The data analysis engine is the core functional unit of the data processing and feedback system module. It is responsible for analyzing the stored historical data and extracting the key factors that affect the operation of the system. The engine discovers potential patterns and anomalies in historical data by applying advanced data analysis methods, such as cluster analysis, pattern recognition, and dimensionality reduction. For example, the data analysis engine can identify the risk area of ​​backflow at a certain water level or flow rate, or discover the failure mode of certain equipment under specific environmental conditions by analyzing multiple rainstorm data. These analysis results can provide valuable decision support for the future operation of the system. The data analysis engine identifies the relationship between the hydrological environment, equipment operating status and system performance by correlating monitoring data from different time periods, finds out the main factors affecting the anti-backflow effect, and provides data support for further optimization of the system.

[0109] The optimization parameter generation unit is responsible for generating the optimization control parameters of the system based on the output results of the data analysis engine. Through the conclusions drawn from the analysis, the unit can make specific control suggestions for the system to ensure that the system's scheduling strategy can better adapt to changes in actual operation. For example, the optimization parameter generation unit can generate new interception well opening and closing strategies based on the rules found in historical data, and adjust the reaction threshold or response time of the scheduling system. In some cases, the optimization parameter generation unit can also dynamically adjust the current scheduling strategy in combination with the real-time data provided by the monitoring system, so that the system always maintains an efficient working state in actual operation. In addition, the optimization parameter generation unit is combined with the decision engine of the control system module to provide optimized parameters in real time, thereby improving the scheduling efficiency and intelligence level of the system. In this way, the system can continuously adapt to complex and changeable hydrological conditions in a data-driven manner, and improve the accuracy and flexibility of backflow prevention.

[0110] The data processing and feedback system module not only works closely with the control system module, but also with the monitoring system module and the automatic maintenance and fault diagnosis system module. First, the real-time data and historical data from the monitoring system module are saved through the historical data storage unit and transmitted to the data analysis engine for in-depth analysis to ensure that the system can be continuously optimized based on actual operating conditions. In addition, the equipment status and fault records of the automatic maintenance system module will also enter the historical data storage unit for the data analysis engine to further analyze the operating rules of the equipment and help generate a smarter maintenance plan. Through the analysis of fault data, the optimization parameter generation unit can also generate more effective equipment maintenance parameters to help the system better predict equipment failures and further improve the stability of equipment operation.

[0111] Example, intelligent scheduling and anti-backflow system based on deep Q network algorithm. Real-world background: The drainage system of a certain city is prone to sudden rise of river water level and excessive pressure of urban drainage network during heavy rain season, resulting in backflow. To solve this problem, an anti-backflow interception system is deployed, and an intelligent scheduling strategy is used to manage the opening and closing of interception wells. System process: Monitoring system module: Water level sensors, flow rate sensors and automatic rain gauges are installed in the drainage system of the city. When heavy rain comes, the system will monitor the river water level, flow rate in the drainage network, and rainfall intensity in real time.

[0112] Control system module: The intelligent scheduling submodule combines the deep Q network (DQN) algorithm to generate the optimal interception well opening and closing strategy in real time based on the monitored data.

[0113] When the system detects that the river water level has risen above the warning line, the dispatch server will instruct the closure of the interception wells in a certain area based on the action instructions generated by the DQN algorithm to prevent the river water from flowing back into the city's drainage pipes.

[0114] At the same time, the system will adjust the status of interception wells in other areas to balance the drainage pressure in each area.

[0115] Actuator unit: Electric valves and automatic gates are connected to each interception well. When the dispatch server issues a dispatch command, the actuator responds immediately to control the opening and closing of the interception well. Through this automated operation, the system can respond quickly and adjust dynamically when water level, flow rate and rainfall conditions change.

[0116] Problem solving in actual operation: When a certain area is at serious risk of backflow due to a sudden rise in river water, the system will close the interception wells in the area according to the decision made by the DQN algorithm to prevent backflow. At the same time, the system will open the interception wells in other areas to guide the drainage in the city to flow into safe areas, balance the water flow pressure in the pipe network, and avoid waterlogging.

[0117] Equipment predictive maintenance system based on recursive neural network. Realistic background: Due to the long-term operation of the urban anti-backflow interception system, the actuators (such as electric valves, automatic gates) and sensor equipment of some interception wells are aging and prone to failure, resulting in the system being unable to prevent backflow risks in a timely manner.

[0118] System process: Automatic maintenance and fault diagnosis system module: Self-check submodule: Predictive maintenance through recursive neural network (RNN) model. This model analyzes historical equipment status data, predicts equipment status changes through time series, and determines possible equipment failures in advance.

[0119] For example, the RNN model detects that the opening and closing operation time of a certain electric valve is gradually increasing. The system analyzes that the valve may fail in the next few operations and issues a maintenance reminder in advance.

[0120] Fault warning submodule: When the RNN predicts that a certain device (such as the valve of the interception well) is about to fail, the system issues a warning and automatically starts the backup device switching unit to transfer the control of the valve to the backup actuator to ensure that the backflow prevention operation is not affected.

[0121] Maintenance log submodule: Each equipment failure and maintenance data will be automatically recorded in the log. Managers can view historical maintenance records through the system to understand which equipment has had problems and arrange corresponding maintenance and replacement plans.

[0122] Problem solving in actual operation: Through the RNN algorithm, the system can predict possible equipment failures in advance, avoiding the problem of reduced drainage capacity or inability to open and close the interception well due to sudden equipment failure. The system can automatically switch to backup equipment to ensure the continuous operation of the backflow prevention system and avoid backflow accidents caused by equipment failure.

[0123] Example, system optimization and feedback based on historical data analysis. Realistic background: In the long-term operation of a city's anti-backflow interception system, it is necessary to continuously adjust the scheduling strategy of the interception well according to different seasons and weather conditions (such as flood season and dry season) to cope with complex environmental changes.

[0124] System process: Historical data storage unit: The anti-backflow system records the water level, flow rate, rainfall and other data of each operation for a long time, as well as the opening and closing status of each interception well and the execution results of the scheduling strategy. These data are stored completely for subsequent analysis.

[0125] Data analysis engine: Using historical data, the system identifies key factors that affect system operation through cluster analysis and pattern recognition. For example, the system may find that the river water rises slowly during a certain time period (such as at night), while rainfall in a certain season is prone to sudden waterlogging.

[0126] Optimization parameter generation unit: By analyzing the rules identified by the engine, the system generates a new set of optimization parameters. Based on these parameters, the system adjusts the opening and closing strategy of the interception well. For example:

[0127] During the flood season, when rainfall reaches a certain intensity, the system will close some interception wells in advance based on the control parameters generated by historical data to prevent backflow.

[0128] During the dry season, the system may optimize the scheduling strategy to reduce the frequency of opening and closing of interception wells in each area and reduce equipment wear.

[0129] Problem solving in actual operation: The system can optimize the anti-backflow strategy under different seasons and hydrological conditions through historical data analysis. For example, in historical data, the system found that the drainage load in some areas was too large and backflow was prone to occur. The system generates optimized parameters and dispatches interception wells in advance to avoid problems.

[0130] The optimized control parameters not only improve the effectiveness of system operation, but also make scheduling more forward-looking and reduce energy and equipment losses.

[0131] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0132] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0133] Figure 2 The schematic diagram of the structure of a control device of a backflow prevention and interception system provided by an embodiment of the present invention is shown. The control device 200 includes a communication module 201 and a processing module 202 .

[0134] The communication module 201 is used to obtain the environmental parameters and current status of each interception well in the interception system; the environmental parameters include water level, flow rate and rainfall.

[0135] The processing module 202 is used to perform single interception well strategy control based on the environmental parameters of each interception well, and determine the single strategy state of each interception well; based on the environmental parameters and current state of each interception well, and the single strategy state of each interception well, perform state compounding on each interception well, and determine the compound control state of each interception well; based on the compound control state and current state of each interception well, generate control instructions for each interception well; based on the control instructions of each interception well, control the opening and closing operations of each interception well.

[0136] Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, the electronic device 300 includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, the steps in the above-mentioned method embodiments are implemented, for example Figure 1 Alternatively, when the processor 301 executes the computer program 303, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 2 The functions of the communication module 201 and the processing module 202 are shown.

[0137] Exemplarily, the computer program 303 may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments that can implement specific functions, and the instruction segments are used to describe the execution process of the computer program 303 in the electronic device 300. For example, the computer program 303 may be divided into Figure 2 The communication module 201 and the processing module 202 are shown.

[0138] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0139] The memory 302 may be an internal storage unit of the electronic device 300, such as a hard disk or memory of the electronic device 300. The memory 302 may also be an external storage device of the electronic device 300, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 300. Further, the memory 302 may also include both an internal storage unit of the electronic device 300 and an external storage device. The memory 302 is used to store the computer program and other programs and data required by the terminal. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0140] The embodiments described above 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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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 invention, and should all be included in the protection scope of the present invention.

Claims

1. A control method for a backflow prevention and interception system, characterized in that: include: Obtaining environmental parameters and current status of each interception well in the interception system; the environmental parameters include water level, flow rate and rainfall; Based on the environmental parameters of each interception well, single interception well strategy control is performed to determine the single strategy state of each interception well; Based on the environmental parameters and current status of each interception well and the single strategy status of each interception well, the status of each interception well is compounded to determine the compound control status of each interception well; Based on the composite control state of each interception well and the current state, generating control instructions for each interception well; Based on the control instructions of each interception well, the opening and closing operations of each interception well are controlled.

2. The control method of the anti-backflow interception system according to claim 1, characterized in that: The single interception well strategy control is performed based on the environmental parameters of each interception well to determine the single strategy state of each interception well, including: For any interception well, if the water level of the interception well is less than the minimum water level, the single strategy state of the interception well is determined to be a closed state; If the water level of the interception well is greater than or equal to the minimum water level and less than the maximum water level, then based on the flow rate and rainfall of the interception well, the single strategy state of the interception well is determined; If the water level of the interception well is greater than or equal to the maximum water level, the single strategy state of the interception well is determined to be an open state.

3. The control method of the anti-backflow interception system according to claim 2 is characterized in that: Determining the single strategy state of the interception well based on the flow velocity and rainfall of the interception well includes: Based on the flow velocity and rainfall of the interception well, the flow velocity change rate and the rainfall change rate are calculated; If the flow velocity change rate is greater than the preset flow velocity, or the rainfall change rate is greater than the preset rainfall, the single strategy state of the interception well is determined to be the open state; If the flow velocity change rate is less than or equal to the preset flow velocity, and the rainfall change rate is less than or equal to the preset rainfall, the single strategy state of the intercepting well is determined to be a closed state.

4. The control method of the anti-backflow interception system according to claim 1, characterized in that: Based on the environmental parameters and current status of each interception well and the single strategy status of each interception well, the status of each interception well is compounded to determine the compound control status of each interception well, including: Based on the current state and the single strategy state of each interception well, a plurality of state combinations of each interception well are determined, wherein each state combination includes one or more interception wells of modified state; Based on the environmental parameters of each interception well and the multiple state combinations, the discharge volume and stability corresponding to each state combination are calculated; Determine the optimal state combination based on the displacement and stability corresponding to each state combination; Based on the optimal state combination, the composite control state of each intercepting well is determined.

5. The control method of the anti-backflow interception system according to claim 1, characterized in that: The generating of control instructions for each interception well based on the composite regulation state of each interception well and the current state includes: Based on the composite control state of each interception well and the current state, determining the interception well whose state has changed; Based on the composite control state of the interception well whose state has changed, a control instruction is generated, wherein the control instruction includes identification information of the interception well whose state has changed and the composite control state.

6. A control device for a backflow prevention and interception system, characterized in that: include: A communication module, used to obtain the environmental parameters and current status of each interception well in the interception system; The environmental parameters include water level, flow rate and rainfall; A processing module is used to perform single interception well strategy control based on the environmental parameters of each interception well to determine the single strategy state of each interception well; based on the environmental parameters and current state of each interception well and the single strategy state of each interception well, perform state compounding on each interception well to determine the compound control state of each interception well; based on the compound control state of each interception well and the current state, generate control instructions for each interception well; Based on the control instructions of each interception well, the opening and closing operations of each interception well are controlled.

7. The control device of the anti-backflow interception system according to claim 6, characterized in that: The processing module is specifically used for determining, for any interception well, that the single strategy state of the interception well is a closed state if the water level of the interception well is less than the minimum water level; determining, based on the flow rate and rainfall of the interception well, that the single strategy state of the interception well is a closed state if the water level of the interception well is greater than or equal to the minimum water level and less than the maximum water level; and determining, if the water level of the interception well is greater than or equal to the maximum water level, that the single strategy state of the interception well is an open state.

8. The control device of the backflow prevention and interception system according to claim 6, characterized in that: The processing module is specifically used to calculate the flow velocity change rate and the rainfall change rate based on the flow velocity and rainfall of the intercepting well; if the flow velocity change rate is greater than the preset flow velocity, or the rainfall change rate is greater than the preset rainfall, the single strategy state of the intercepting well is determined to be the open state; if the flow velocity change rate is less than or equal to the preset flow velocity, and the rainfall change rate is less than or equal to the preset rainfall, the single strategy state of the intercepting well is determined to be the closed state.

9. The control device of the anti-backflow interception system according to claim 6, characterized in that: The processing module is specifically used to determine multiple state combinations of each interception well based on the current state and single strategy state of each interception well, wherein each state combination includes one or more interception wells in a modified state; based on the environmental parameters of each interception well and the multiple state combinations, calculate the drainage and stability corresponding to each state combination; based on the drainage and stability corresponding to each state combination, determine the optimal state combination; based on the optimal state combination, determine the composite control state of each interception well.

10. A backflow prevention and interception system, characterized in that: The backflow prevention and interception system includes an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the steps of the method as described in any one of claims 1 to 5.

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