Cross-factory collaborative emergency scheduling method and system for mine water recycling

Abstract

The present application belongs to the technical field of water treatment control, and particularly relates to a cross-factory collaborative emergency scheduling method and system for mine water recycling, which comprises the following steps: constructing a cross-factory entity correlation network, taking a water treatment unit as a graph entity node, taking a water transmission pipeline as a directed edge, and determining a physical weight according to a physical transmission delay time; monitoring water quality concentration, determining water quality concentration change speed and water quality concentration change acceleration; combining water quality concentration change speed, water quality concentration change acceleration and hydraulic retention time to calculate a water quality mutation index, so as to lock an abnormal starting mother node when a self-adaptive trigger boundary is met; traversing to determine a downstream disaster-affected node, determining a trigger time of an emergency scheduling instruction according to a physical transmission delay time and an inherent action lag time, and implementing asynchronous peak-shaving scheduling. The present application can utilize the flow time consumption of a physical space pipeline to offset the action lag of a hardware device, realize advanced peak-shaving blocking defense, and ensure safe operation of the system.

Description

Technical Field

[0001] This invention relates to the field of water treatment control technology. More specifically, this invention relates to a cross-plant collaborative emergency dispatching method and system for mine water recycling. Background Technology

[0002] In the mine water recycling project of coal enterprises, there are multiple geographically dispersed water treatment units with closely connected upstream and downstream processes. These units are responsible for centrally treating the water flowing from the underground goaf and purifying it for production replenishment or ecological irrigation. When a sudden deterioration of water quality occurs underground, water masses containing high concentrations of suspended solids or characteristic pollutants will flow along physical pipelines to the pretreatment, special defluorination and deep desalination workshops on the surface, posing a direct threat to the subsequent treatment system.

[0003] Chinese patent document CN110498525B discloses a method for combined mine water treatment in a coal preparation plant. This method buffers underground mine sludge water before subjecting it to sedimentation, primary concentration, and secondary concentration. Excess overflow and underflow generated during treatment are returned to the buffer tank or a shared filter press for disposal via pipelines. This method utilizes the physical volume of a regulating tank and multi-stage thickening tanks in series, employing the circulation and buffering of excess water and flexible switching of process pipelines to cope with fluctuations in underground water inflow, thus improving the water system's buffering capacity against water volume shocks to some extent. However, this method relies on the physical volume of the tanks for passive containment and water volume scheduling. Its control focus is on balancing water supply and demand and centralized sludge pressing, without establishing a dynamic tracking and early warning mechanism for real-time changes in pollutant concentration. In actual operation, this method can only provide post-treatment after the water concentration has increased and entered the current tank, relying on subsequent chemical precipitation; it cannot intervene before the water mass reaches downstream core equipment.

[0004] In existing technologies, although some water treatment systems address water volume fluctuations by adding buffer tanks or optimizing process pipelines, their control logic remains limited to the passive regulation of local water tanks, failing to incorporate the spatial flow direction and hydraulic transmission time of the pipeline network into dynamic calculations. When a sudden deterioration in water quality occurs due to well water inflow, high-concentration water masses migrate along the pipelines. Existing systems typically only trigger local chemical dosing or valve action when the concentration reaches a fixed threshold using inlet sensors. Due to the objective physical lag in the opening and closing of heavy valves and the mixing of chemicals, this reactive triggering mode causes the interception command to always lag behind the diffusion speed of the contaminated water mass. Excessive water flow that is not intercepted upstream will directly flow into downstream membrane treatment systems with stringent water quality requirements, causing irreversible fouling of membrane modules and equipment damage, forcing the entire water treatment chain to be interrupted. Summary of the Invention

[0005] To address the technical problems of existing systems that rely on isolated thresholds for post-event control, lack cross-plant hydraulic delay tracking, resulting in delayed response and inability to intercept contaminated water masses in advance to protect downstream equipment, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a cross-plant collaborative emergency dispatching method for mine water recycling, comprising: A cross-plant entity association network is constructed, where graph entity nodes represent water treatment units, directed edges represent water transmission pipelines, and the physical weights of the directed edges are determined based on the physical transmission delay time of water flow within the pipelines. The water quality concentration corresponding to each graph entity node is monitored in real time, and the rate of change and acceleration of water quality concentration change for each graph entity node are determined based on the changes in water quality concentration within adjacent sampling periods. A water quality mutation index is calculated based on the rate of change, acceleration of change, and hydraulic residence time of the graph entity nodes, and the anomaly initiation point is locked when the water quality mutation index meets the adaptive trigger boundary. Path traversal is performed starting from the anomaly initiation point to determine the affected downstream disaster-bearing nodes. The trigger time for emergency dispatch instructions is determined based on the physical transmission delay time in the cross-plant entity association network and the inherent action lag time of the downstream disaster-bearing nodes, enabling asynchronous peak-shifting dispatching of the downstream disaster-bearing nodes.

[0007] This invention constructs a cross-plant entity association network and determines the physical weights of directed edges using physical transmission delay time, thus connecting the spatial flow path of polluted water between plants. It extracts the rate and acceleration of water concentration change in real time and, combined with hydraulic residence time, calculates the water quality deterioration trend to generate a water quality mutation index. When the water quality mutation index meets the adaptive trigger boundary, the origin of the anomaly is locked in advance, breaking away from the traditional passive alarm mode with fixed thresholds. Furthermore, it locates the affected downstream disaster-bearing nodes along the network path, deducts the physical transmission delay time of the polluted water mass from the inherent action lag time of the downstream disaster-bearing nodes to plan the triggering time of emergency dispatch commands, and uses the time spent by water flow in the pipeline network to offset the objective response lag of on-site equipment, realizing asynchronous peak-shifting dispatching across plants. This effectively overcomes the defect that local control commands lag behind the diffusion speed of polluted water masses, and completes advanced collaborative interception before the arrival of excessive water flow, avoiding irreversible damage to downstream deep treatment equipment and ensuring the continuous and safe operation of the entire water treatment chain.

[0008] Preferably, the construction of the cross-plant entity association network includes: obtaining the physical equipment ledger and process drawings of the mine water circulation system; abstracting the physical water pools inside the pretreatment plant, the special defluorination plant, and the membrane concentration treatment plant into a set of graph entity nodes; obtaining the water transmission pipeline between any two nodes in the set of graph entity nodes, and abstracting the water transmission pipeline into the directed edge to connect the corresponding graph entity nodes; obtaining the pipeline length and real-time pipeline flow velocity of the water transmission pipeline, and determining the physical transmission delay time based on the ratio of the pipeline length to the real-time pipeline flow velocity.

[0009] This invention maps physical water tanks and water pipelines into graph entity nodes and directed edges, and determines the physical weight of the directed edges based on the ratio of pipeline length to real-time pipeline flow velocity. This constructs a spatial topological benchmark that reflects the actual water flow path and dynamic time consumption. It can update the travel time between nodes in real time based on the start and stop of pumps and valves and flow fluctuations. This provides reliable underlying data support for tracking the spatial flow and arrival time of polluted water masses between plant areas, effectively solving the problem that traditional isolated monitoring cannot grasp the cross-regional hydraulic connectivity status.

[0010] Preferably, it further includes: in response to the real-time pipeline flow rate being 0, marking the physical weight of the corresponding directed edge in the cross-plant entity association network as infinite, indicating that the physical transmission link is temporarily disconnected.

[0011] Preferably, determining the rate of change of water quality concentration and the acceleration of change of water quality concentration corresponding to each map entity node includes: filtering the real-time water quality concentration corresponding to each map entity node acquired in real time to obtain a smoothed water quality concentration; determining the rate of change of water quality concentration based on the ratio of the difference in smoothed water quality concentration within adjacent sampling periods to the sampling time interval; and determining the acceleration of change of water quality concentration based on the ratio of the difference in the rate of change of water quality concentration within adjacent sampling periods to the sampling time interval.

[0012] Preferably, the method for obtaining the hydraulic residence time is as follows: obtain the effective physical volume and rated processing flow rate of the physical pool corresponding to the map entity node, and determine the ratio of the effective physical volume to the rated processing flow rate as the hydraulic residence time.

[0013] Preferably, the water quality mutation index satisfies the expression: In the formula, Represents the entity nodes in the graph At any moment Water quality mutation index; Represents the entity nodes in the graph At any moment The rate of change of water quality concentration; Indicates the hydraulic residence time; Represents the entity nodes in the graph At any moment The acceleration of water quality concentration change; Represents the square of the hydraulic residence time; This represents the standard deviation of steady-state concentration fluctuations; This represents the minimum positive real number concentration compensation value; represents the constant coefficient of the second-order term in a Taylor series.

[0014] This invention extends the instantaneous changes and accelerating trends of water quality deterioration to the actual residence time in the water tank, and deduces the theoretical cumulative increase of pollution load during the natural dilution process. At the same time, by eliminating background fluctuation interference under normal process conditions in the denominator, the early warning indicator can keenly capture the characteristics of the accelerated burst of shock load, and achieve early identification with a high signal-to-noise ratio before the absolute value of the concentration reaches the warning line.

[0015] Preferably, locking the originating point of an anomaly when the water quality mutation index meets the adaptive trigger boundary includes: calculating the average value of the water quality mutation index of the map entity node within the historical operating cycle; determining the adaptive trigger boundary based on the product of the average value and the preset trigger multiplier; and determining the corresponding map entity node as the originating point of an anomaly when the water quality mutation index is greater than the adaptive trigger boundary for several consecutive sampling cycles.

[0016] This invention transforms the fixed static alarm threshold into a dynamic boundary that fits the actual tolerance level of each plant area. By using continuous comparison in the time dimension, it filters out false signals caused by instantaneous changes in sensors or occasional water flow disturbances, ensuring that only the real impact trend that continues to deteriorate can initiate the subsequent scheduling process, which greatly reduces the frequency of false alarms and the operational losses caused by invalid actions.

[0017] Preferably, determining the trigger time of the emergency dispatch command includes: extracting the physical transmission delay time on all directed edges between the anomaly originating point and the downstream disaster-bearing node, and linearly accumulating it to obtain the absolute arrival time of the contaminated water mass; adding the anomaly discovery time of the anomaly originating point to the absolute arrival time, and subtracting the inherent action delay time of the on-site mechanical actuators of the downstream disaster-bearing node to obtain the trigger time of the emergency dispatch command.

[0018] This invention utilizes the natural transmission time of water flow in long-distance pipelines as a buffer window, delaying the mechanical response of heavy valve opening and closing and chemical dosing until the water mass approaches, so that the completion point of emergency protection actions coincides perfectly with the physical moment when the polluted water mass arrives at the downstream core equipment. This reverses the passive situation where traditional post-event response always lags behind the spread of pollution, and achieves advanced interception.

[0019] Preferably, it also includes: when the system clock reaches the trigger time, issuing a dosing frequency increase command or a bypass diversion valve opening command to the control unit of the corresponding downstream disaster-bearing node.

[0020] Secondly, the present invention provides a cross-plant collaborative emergency dispatch system for mine water recycling, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned cross-plant collaborative emergency dispatch method for mine water recycling is implemented.

[0021] By adopting the above technical solution, the cross-plant collaborative emergency dispatch method for the recycling of mine water is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. Terminal equipment can then be made based on the memory and processor for convenient use.

[0022] The beneficial effects of this invention are as follows: By constructing a cross-plant entity association network that maps real water pipelines to physical transmission delay times, this invention connects the spatial flow paths of polluted water masses between dispersed plants, tracks the evolution trend of water quality concentration change rate and acceleration in real time, and introduces hydraulic retention time to calculate the theoretical cumulative increase of pollution load in the pool, generating a water quality mutation index that can filter process background fluctuations. When the water quality mutation index exceeds the adaptive trigger boundary, the abnormal origin point is locked in advance, thus overcoming the limitations of passive alarms that rely on fixed concentration values; furthermore, the affected downstream areas are extracted along the network topology. At the disaster-bearing node, the time taken for the contaminated water mass to travel through the pipeline network is offset by the inherent lag time of the on-site mechanical actuators to calculate the trigger time of the emergency dispatch command. The natural transmission time of water flow in the pipeline is used to offset the objective delay of valve opening and closing and agent mixing, so as to realize asynchronous peak-shifting dispatch across plant areas. This ensures that the protective actions triggered by the upstream early warning are completed on time before the contaminated water mass reaches the downstream core equipment. This effectively overcomes the defect that the local control command lags behind the speed of pollution diffusion, avoids the intrusion of excessive water flow into the deep treatment system and causes irreversible damage, and ensures the continuous and safe operation of the entire mine water recycling chain. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the cross-plant collaborative emergency dispatch method for mine water recycling in this invention; Figure 2 This is a graph showing the changes in water quality concentration at the entity nodes in the spectral map. Figure 3 This is a schematic diagram of the curves showing the rate of change of water concentration versus the acceleration of the change in water concentration. Figure 4 This is a schematic diagram illustrating the changes in the water quality mutation index. Figure 5 Operation timing diagram to utilize pipeline flow time to offset equipment lag. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] This invention discloses a cross-plant collaborative emergency dispatch method for mine water recycling, referring to... Figure 1 This includes steps S1-S4: S1: Establish a cross-plant entity association network including physical transmission delay time.

[0027] Specifically, the physical equipment ledger and process drawings of the mine water circulation system are obtained, and key water treatment units such as primary sedimentation tanks, equalization tanks, sedimentation tanks, clear water tanks, and pump rooms within the pretreatment plant, specialized defluorination plant, and membrane concentration plant are abstracted into a set of graph entity nodes. The actual physical water transmission pipelines between any two nodes in the graph entity node set are obtained, and these actual physical water transmission pipelines are abstracted as directed edges, connecting the corresponding graph entity nodes to construct a cross-plant entity association network.

[0028] The diameter and length of each real physical water pipeline are obtained, and the real-time pipeline flow velocity is acquired using electromagnetic flowmeters deployed on the corresponding pipelines. When the real-time pipeline flow velocity is not zero, the real physical transmission delay time of water flow between adjacent graph entity nodes is calculated based on the pipeline length and the real-time pipeline flow velocity. This real physical transmission delay time is then used as the physical weight attribute of the directed edges and written into the cross-plant entity association network. The real physical transmission delay time satisfies the expression:

[0029] In the formula, This represents the actual physical transmission delay time of water flow between adjacent map entity nodes; This represents the actual length of the physical water supply pipeline. This represents the real-time flow velocity in a physical water pipeline. Pipeline length. The longer the pipe and the higher the real-time flow rate The slower the speed, the longer the physical transmission delay. The larger the value, the more it reflects the physical time required for water to travel through long-distance pipe networks.

[0030] If the real-time pipeline flow velocity is 0, it means that the current water pipeline is in a stopped and static state. At this time, there is no need to calculate the corresponding real physical transmission delay time. Instead, the physical weight of the directed edge in the cross-plant entity association network is marked as infinite, indicating that the physical transmission link is temporarily disconnected.

[0031] S2: Construct a smooth water quality evolution trajectory.

[0032] Specifically, within a preset sampling time window, the real-time water quality concentration of the physical water tanks corresponding to each graph entity node in the cross-plant entity association network is continuously acquired. ,in Indicates the index identifier of the graph entity node. This represents the current time. A moving average filtering algorithm is used to reduce noise in the real-time water quality concentration, resulting in a smoothed water quality concentration:

[0033] In the formula, Represents the entity nodes in the graph At any moment The smooth water quality concentration; Indicates the size of the sliding filter window; Indicates the offset index within the sliding filter window; Represents the entity nodes in the graph At any moment Real-time water quality concentration. For the sliding filter window size... If the value is too small, the high-frequency background noise will not be completely filtered out. If the value is too large, the true short-term impact load characteristics will be over-smoothed and lost. In this embodiment, 5 is selected as the optimal balance value. In other embodiments, the implementer can set the sliding filter window size according to the signal-to-noise ratio of the online water quality analyzer. Through this sliding average calculation, numerical spikes caused by local disturbances in the water body can be effectively eliminated, and the true physical evolution trend of water quality changes can be exposed.

[0034] Calculate the rate of change of water quality concentration based on the smoothed water quality concentration of adjacent sampling periods:

[0035] In the formula, Represents the entity nodes in the graph At any moment The rate of change of water quality concentration; Represents the entity nodes in the graph At any moment The smooth water quality concentration; Represents the entity nodes in the graph At any moment The smooth water quality concentration; This indicates the sampling time interval. As the difference in smoothed water quality concentration increases, the rate of change in water quality concentration also increases, reflecting the severity of the instantaneous deterioration caused by the pollution load entering the current physical water tank.

[0036] Calculate the acceleration of water concentration change based on the rate of change of water concentration in adjacent sampling periods:

[0037] In the formula, Represents the entity nodes in the graph At any moment The acceleration of water quality concentration change; Represents the entity nodes in the graph At any moment The rate of change of water quality concentration; Represents the entity nodes in the graph At any moment The rate of change of water quality concentration; This indicates the sampling time interval. The acceleration of water concentration change reflects the rate of increase in water quality deterioration, revealing the cumulative potential for pollution load to erupt.

[0038] The smooth water quality concentration, the rate of change of water quality concentration, and the acceleration of change of water quality concentration are constructed as coordinate points in a three-dimensional phase space coordinate system. As the time axis advances, the continuous coordinate points are connected in the three-dimensional phase space coordinate system to form the smooth water quality evolution trajectory of the entity node of the map.

[0039] S3: Calculate the node water quality abrupt change index by combining hydraulic residence time.

[0040] Specifically, the effective physical volume and non-zero rated treatment flow rate of the physical pool corresponding to the entity node in the graph are obtained, the ratio of effective physical volume to rated treatment flow rate is calculated, and this ratio is used as the hydraulic retention time. Obtain the set of real-time water quality concentrations of the entity nodes in the map during a historical period of stable operation without abnormalities, calculate the standard deviation of the real-time water quality concentration set, and use the standard deviation as the steady-state concentration fluctuation standard deviation.

[0041] Based on the values ​​of each coordinate point in the smooth water quality evolution trajectory, the water quality mutation index of the entity node in the map is calculated. Specifically, the existing Taylor series expansion theorem is used to deduce the function value after the prediction time step using the derivative values ​​of the fundamental function at a certain point. Its second-order truncation polynomial satisfies the expression: Due to hydraulic residence time Physically, it characterizes the theoretical buffering time of water flow in the current physical pool, and therefore uses it as the prediction time step. Substituting these values ​​into the Taylor series expansion theorem above, we can deduce the future state of the water mass as it flows out of the physical pool. Simultaneously, we will consider the time interval... Smoothing water concentration Substitute the corresponding base function values , will be the moment rate of change in water concentration Substituting the corresponding first derivative , will be the moment Acceleration of water concentration change Substituting the corresponding second derivative The predicted future water quality concentration values ​​after buffering and retention were derived. Subtract the smoothed water quality concentration at the current moment from the predicted future water quality concentration value. To extract the predicted increase in water quality concentration due to water quality deterioration, and to obtain the smoothed water quality concentration at the current time during the subtraction process. The offsetting effect results in the final predicted increase in water quality concentration as follows: To assess the degree of anomalousness of the predicted water quality concentration increment relative to normal fluctuations, the predicted water quality concentration increment is divided by the sum of the steady-state concentration fluctuation standard deviation and the minimum positive real concentration compensation value to obtain the water quality mutation index, which satisfies the expression:

[0042] In the formula, Represents the entity nodes in the graph At any moment Water quality mutation index; Represents the entity nodes in the graph At any moment The rate of change of water quality concentration; Indicates the hydraulic residence time; Represents the entity nodes in the graph At any moment The acceleration of water quality concentration change; Represents the square of the hydraulic residence time; This represents the standard deviation of steady-state concentration fluctuations; This represents the minimum positive real number concentration compensation value; The constant coefficients of the second-order Taylor series are represented. The minimum positive real-number concentration compensation value is... Its dimensions are consistent with the concentration units, and its introduction aims to eliminate the standard deviation of steady-state concentration fluctuations. The division-by-zero exception triggered when the value is 0 or close to 0 ensures the numerical stability of the algorithm. For example, A possible value is 0.001. This value is typically set to a small amount below the minimum detection resolution of the water quality sensor or the lower limit of normal process fluctuations. In other embodiments, those skilled in the art can make adaptive adjustments based on the floating-point arithmetic accuracy of the actual control system, the detection limit of the online analyzer, and the background characteristics of the on-site water quality, as long as it ensures... much smaller The typical working value is sufficient.

[0043] Hydraulic residence time Physically, this characterizes the natural residence time of the pool to the impact of pollution, as the rate of change in water concentration... With the acceleration of changes in water concentration When both are greater than 0, the hydraulic residence time The mathematical amplification effect in the numerator exhibits a linear and quadratic order, causing a sharp increase in the derived predicted water quality concentration increment. This is because the denominator introduces the standard deviation of steady-state concentration fluctuations. The predicted increase in water quality concentration essentially represents the theoretical cumulative increase in pollution load over the current hydraulic retention time in the pool, calculated by dividing by the standard deviation of steady-state concentration fluctuation. Compensation value for minimum positive real concentration The sum is converted into a deviation factor relative to steady-state process fluctuations, thus achieving dimensionless cross-scale early warning. When the predicted increase in water quality concentration is much greater than the natural process background fluctuation, it indicates a sharp deterioration in the signal-to-noise ratio and a significant increase in the water quality mutation index. It will show a significant leap.

[0044] Furthermore, the historical average of the water quality mutation index of the map entity nodes over consecutive operating cycles is multiplied by a preset trigger multiplier to obtain the adaptive trigger boundary. For the preset trigger multiplier, a value that is too small will cause the system to become extremely sensitive and generate frequent malfunctions, while a value that is too large will cause the system to respond slowly to real pollution shocks. In this embodiment, 10 is selected as the optimal balance value. In other embodiments, the implementer can set the preset trigger multiplier according to the sensitivity tolerance of the downstream membrane module to water quality deterioration.

[0045] When the water quality mutation index exceeds the adaptive trigger boundary for three consecutive sampling periods, it is determined that the current map entity node has encountered a sudden impact load, and the node is locked as the abnormal originating point.

[0046] S4: Implement asynchronous peak-shifting scheduling to offset inherent motion delay time.

[0047] Specifically, starting from the locked originating point of the anomaly, a breadth-first traversal is performed along the directed edges in the cross-plant entity association network to extract all downstream disaster-bearing nodes affected by physical impact.

[0048] Extract the actual physical transmission delay time on all directed edges between the anomaly originating point and each downstream disaster-bearing node, and linearly accumulate all the actual physical transmission delay times on the path to obtain the absolute arrival time of the polluted water mass from the anomaly originating point to the corresponding downstream disaster-bearing node.

[0049] To obtain the inherent motion lag time of the on-site mechanical actuators corresponding to each downstream disaster-affected node, specifically: under no-load or low-load steady-state operation conditions, the programmable logic controller issues a standard step command and simultaneously starts high-precision timestamp recording. From the moment the command is issued until the moment the displacement sensor or flow feedback signal of the on-site mechanical actuator reaches 95% of the command target value and remains stable, the time difference between the two is taken as the single measured lag time. The above test is repeated several times. After removing abnormal outliers caused by power fluctuations or mechanical jamming, the arithmetic mean of the remaining measured lag times is taken as the inherent motion lag time of the on-site mechanical actuators corresponding to the downstream disaster-affected node, and written into the attribute field of the corresponding node in the cross-plant entity association network for use in emergency dispatch.

[0050] Based on the anomaly detection time, absolute arrival time, and inherent action lag time, calculate the trigger time for issuing emergency dispatch instructions to the downstream disaster-affected node:

[0051] In the formula, This indicates the trigger time for issuing emergency dispatch instructions to downstream disaster-affected nodes; This indicates the moment when the anomaly was detected due to a sudden impact load, indicating the origin of the anomaly. This indicates the absolute arrival time of the polluted water mass from the origin of the anomaly to the corresponding downstream disaster-bearing node; This indicates the inherent lag time of the mechanical actuators at the downstream disaster-affected node. (Time of anomaly detection) Plus absolute arrival time The natural time when the contaminated water mass actually comes into contact with downstream disaster-bearing equipment was determined, and the inherent lag time was then subtracted from this time. This makes the triggering time of issuing commands... It was exactly one mechanical cycle ahead of schedule.

[0052] It should be noted that if the absolute time taken is reached... Less than the inherent motion lag time This indicates that the arrival time of the polluted water mass is shorter than the complete mechanical response time, and the system directly sets the trigger time. The value is the time when the anomaly is detected. This means that the action is triggered immediately. As the absolute time elapses... Increased or inherent motion lag time The decrease, trigger time This will be delayed accordingly, ensuring that the valve opening or dosing pump boosting action is completed precisely at the moment the contaminated water mass arrives. When the system clock reaches the trigger time... At that time, the system sends a command to increase the dosing frequency or open the bypass diversion valve to the programmable logic controller of the corresponding downstream disaster-bearing node to achieve cross-plant collaborative defense response.

[0053] For example, Figure 2 This is a graph showing the changes in water quality concentration at the entity nodes in the spectrum. Figure 2 The paper demonstrates the filtering effect of moving average filtering on high-frequency random fluctuations in real-time collected concentrations. While maintaining the inertia of concentration evolution, it smooths out water quality concentrations and captures the initial trend of water quality deterioration at the moment of anomaly detection.

[0054] Figure 3 The diagram shows the curves of the rate of change of water concentration versus the acceleration of the change of water concentration. The acceleration curve of the change of water concentration shows a significant upward pulse in the early stage of the abnormal outbreak, which provides a stable dynamic component for deriving the prediction of the increase in water concentration using Taylor series.

[0055] Figure 4 This is a schematic diagram illustrating the changes in the water quality mutation index. Figure 4 It can be seen that the water quality mutation index is at a low level during the stable operation phase, and quickly crosses the adaptive trigger boundary during the water quality abrupt change phase, verifying the high sensitivity of the water quality mutation index to the water quality abrupt change characteristics.

[0056] Figure 5 To utilize pipeline flow time to offset equipment lag, the operation timing diagram uses timing anchor lines and filled areas to visually present the timing offset relationship between the anomaly detection time, the command trigger time, and the absolute arrival time of the contamination plume. By calculating the absolute arrival time of the pipeline flow to compensate for the inherent lag time, the engineering effectiveness of the asynchronous peak-shifting scheduling strategy is confirmed.

[0057] This invention also discloses a cross-plant collaborative emergency dispatch system for mine water recycling, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the cross-plant collaborative emergency dispatch method for mine water recycling according to this invention is implemented.

[0058] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A cross-plant collaborative emergency dispatch method for mine water recycling, characterized in that, include: Construct a cross-plant entity association network, in which the graph entity nodes in the cross-plant entity association network represent water treatment units, the directed edges represent water transmission pipelines, and the physical weight of the directed edges is determined based on the physical transmission delay time of the water flow in the water transmission pipeline. Real-time monitoring of water quality concentration corresponding to each map entity node, and determination of the rate of change and acceleration of water quality concentration change corresponding to each map entity node based on the changes in water quality concentration in adjacent sampling periods; The water quality mutation index is calculated based on the rate of change of water quality concentration, the acceleration of change of water quality concentration, and the hydraulic residence time of the entity nodes in the map. The anomaly originating point is locked when the water quality mutation index meets the adaptive trigger boundary. Starting from the originating point of the anomaly, a path traversal is performed to determine the affected downstream disaster-bearing nodes. Based on the physical transmission delay time in the cross-plant entity association network and the inherent action lag time of the downstream disaster-bearing nodes, the triggering time of the emergency dispatch command is determined so as to implement asynchronous peak-shifting dispatch for the downstream disaster-bearing nodes.

2. The cross-plant collaborative emergency dispatch method for mine water recycling according to claim 1, characterized in that, The construction of the cross-plant entity association network includes: Obtain the physical equipment ledger and process drawings in the mine water circulation system, and abstract the physical water pools inside the pretreatment plant, special defluorination plant and membrane concentration treatment plant into a set of graph entity nodes; Obtain the water supply pipeline between any two nodes in the graph entity node set, and abstract the water supply pipeline as the directed edge to connect the corresponding graph entity nodes; The pipe length and real-time pipe flow velocity of the water supply pipeline are obtained, and the physical transmission delay time is determined based on the ratio of pipe length to real-time pipe flow velocity.

3. The cross-plant collaborative emergency dispatch method for mine water recycling according to claim 2, characterized in that, Also includes: In response to a real-time pipeline flow rate of 0, the physical weight of the corresponding directed edge in the cross-plant entity association network is marked as infinite, indicating that the physical transmission link is temporarily disconnected.

4. The cross-plant collaborative emergency dispatch method for mine water recycling according to claim 1, characterized in that, The determination of the rate of change of water quality concentration and the acceleration of the rate of change of water quality concentration corresponding to each map entity node includes: The real-time water quality concentrations corresponding to each map entity node acquired in real time are filtered to obtain smooth water quality concentrations. The rate of change of water quality concentration is determined by the ratio of the difference in smooth water quality concentration within adjacent sampling periods to the sampling time interval. The acceleration of water concentration change is determined by the ratio of the difference in the rate of change of water concentration within adjacent sampling periods to the sampling time interval.

5. The cross-plant collaborative emergency dispatch method for mine water recycling according to claim 1, characterized in that, The method for obtaining the hydraulic residence time is as follows: Obtain the effective physical volume and rated processing flow rate of the physical pool corresponding to the entity node in the graph, and determine the hydraulic retention time as the ratio of the effective physical volume to the rated processing flow rate.

6. The cross-plant collaborative emergency dispatch method for mine water recycling according to claim 1, characterized in that, The water quality mutation index satisfies the expression: ； In the formula, Represents the entity nodes in the graph At any moment Water quality mutation index; Represents the entity nodes in the graph At any moment The rate of change of water quality concentration; Indicates the hydraulic residence time; Represents the entity nodes in the graph At any moment The acceleration of water quality concentration change; Represents the square of the hydraulic residence time; This represents the standard deviation of steady-state concentration fluctuations; This represents the minimum positive real number concentration compensation value; represents the constant coefficient of the second-order term in a Taylor series.

7. The cross-plant collaborative emergency dispatch method for mine water recycling according to claim 1, characterized in that, The step of locking the anomaly originating point when the water quality mutation index meets the adaptive trigger boundary includes: Calculate the average water quality mutation index of the map entity node within the historical operating cycle; determine the adaptive trigger boundary based on the product of the average value and the preset trigger multiplier; when the water quality mutation index is greater than the adaptive trigger boundary for several consecutive sampling cycles, the corresponding map entity node is determined as the abnormal initiation point.

8. The cross-plant collaborative emergency dispatch method for mine water recycling according to claim 1, characterized in that, Determining the trigger time of the emergency dispatch instruction includes: Extract the physical transmission delay time on all directed edges between the anomaly originating point and the downstream disaster-bearing node, and linearly accumulate it to obtain the absolute arrival time of the contaminated water mass; add the anomaly discovery time of the anomaly originating point to the absolute arrival time, and subtract the inherent action delay time of the on-site mechanical actuators of the downstream disaster-bearing node to obtain the trigger time of the emergency dispatch command.

9. The cross-plant collaborative emergency dispatch method for mine water recycling according to claim 1, characterized in that, Also includes: When the system clock reaches the trigger time, it sends a command to increase the dosing frequency or open the bypass diversion valve to the control unit of the corresponding downstream disaster-bearing node.

10. A cross-plant collaborative emergency dispatch system for mine water recycling, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the cross-plant collaborative emergency dispatch method for mine water recycling according to any one of claims 1-9.

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