An assembled water plant full life cycle management system and method

Abstract

The application relates to the technical field of water treatment, and specifically discloses a full-life-cycle management system and method for an assembled water plant, which comprises the following steps: identifying an efficiency-fragile area according to the correlation between water quality abnormality frequency and process adjustment delay, and constructing a dynamic efficiency evaluation matrix; configuring a core processing module and an auxiliary buffer module based on the matrix, embedding adaptive control logic to form a basic processing unit, and arranging a real-time monitoring network layer; cooperatively analyzing the dynamic efficiency evaluation matrix and monitoring data to deduce a unit full-life-cycle performance attenuation path and a risk evolution graph; introducing a function reinforcement module to construct a reinforcement processing unit according to the weak links revealed by the graph, introducing an energy efficiency optimization module and a cooperative optimizer to perform online reconstruction after the stable operation of the reinforcement processing unit, and generating an optimized processing unit; finally, calculating a full-life-cycle comprehensive health degree index of the optimized unit, realizing rapid replication and parallel deployment of the standardized processing unit, and realizing the transformation from passive response to active prediction intervention.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology, specifically to a prefabricated water plant full life cycle management system and method. Background Technology

[0002] Currently, the construction of prefabricated water plants commonly adopts factory prefabrication and on-site assembly of standard modules, significantly improving construction efficiency. In terms of operation and management, most systems rely on preset fixed process parameters and automated control based on simple thresholds (such as initiating chemical dosing based on exceeding the limit of a single water quality index). Their operational data is mainly used for monitoring and post-event alarms, and the linkage logic between modules is relatively rigid, lacking a collaborative mechanism for dynamic adjustment based on real-time operating conditions.

[0003] The limitations of existing technologies include: the aforementioned methods are insufficient to cope with the dynamic impacts caused by the coupling of source water quality, water load, and environmental factors; they cannot accurately locate and warn of weak links in the process that change over time and space, resulting in delayed control response and weak anti-disturbance capabilities. Furthermore, the system lacks the ability to simulate and predict equipment performance degradation over long periods, leading to reactive maintenance. In addition, successful operational optimization experience is difficult to quantify and replicate in new projects, hindering the overall efficiency and reliability of large-scale deployment of prefabricated water plants. Summary of the Invention

[0004] The purpose of this invention is to provide a prefabricated water plant full life cycle management system and method to solve the problems mentioned above.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for full life-cycle management of prefabricated water plants includes the following steps: S1: Obtain the time-series data of the source water quality of the target water plant, the regional water load curve and the external environmental disturbance parameters. Based on the correlation between the frequency of water quality anomalies and the process adjustment delay, identify the performance vulnerability areas in the spatiotemporal distribution of the water treatment process. S2: For each performance vulnerability zone, collect its core operating parameter set under different operating conditions, and construct a dynamic performance evaluation matrix for the performance vulnerability zone; S3: Based on the dynamic performance evaluation matrix, configure the core processing module group H1 and deploy the auxiliary buffer module group H2 coupled with its process flow. At the same time, embed an adaptive control logic set that can be triggered by the H2 running status data, together forming a basic processing unit with initial self-adjustment capability. S4: Deploy a sensor network around the boundaries and key nodes of the basic processing unit to form a real-time monitoring network layer that senses its internal state and input / output flow. S5: Based on the collaborative analysis results of the dynamic performance evaluation matrix and the real-time monitoring network layer, the performance degradation path and risk evolution map of the basic processing unit in the preset full life cycle are deduced. S6: Based on the weak links revealed by the performance degradation path and risk evolution map, functional enhancement module group H3 is introduced into the basic processing unit to build an enhanced processing unit with enhanced anti-disturbance capability. S7: When the enhanced processing unit operates stably within a continuously set evaluation period, and the overall performance fluctuation coefficient of the enhanced processing unit decreases to a preset threshold, the optimization process is triggered. Energy efficiency optimization module group H4 and module-strategy co-optimizer are introduced into the enhanced processing unit to perform online reconstruction and performance upgrade of the enhanced processing unit, generating an optimized processing unit. S8: Calculate and output the comprehensive health index of the entire life cycle of the optimized treatment unit, and manage the replication and parallel deployment of standardized treatment units based on the comprehensive health index and the needs of new or expanded water plants.

[0006] As a further aspect of the present invention: S1 specifically includes: By mapping the time-series abnormal peak values ​​of water quality with the time delay data of the corresponding process adjustment commands, a process response hysteresis spectrum is obtained. Based on the process response hysteresis map, frequency decomposition and vulnerability calculation are performed on each hysteresis unit to generate a spatiotemporal vulnerability surface. Based on the continuous high-value regions in the vulnerability surface, the effectiveness vulnerability zone is obtained by aggregation and division.

[0007] As a further aspect of the present invention: S2 specifically includes: Obtain the spatiotemporal coordinates of the performance vulnerability zone; Based on spatiotemporal coordinates, multiple sets of high-frequency sampling probes are simultaneously deployed on the corresponding water treatment process unit, and multi-condition switching trigger commands are preset to capture the ultra-short-cycle synchronous changes of each operating parameter before and after the instantaneous switching of operating conditions, forming a multi-condition parameter array. The data in the multi-condition parameter array are decomposed into a multi-dimensional orthogonal structure to form a dynamic performance tensor containing time, space and condition dimensions. The dynamic performance tensor is then integrated and normalized into a dynamic performance evaluation matrix.

[0008] As a further aspect of the present invention: S3 specifically includes: Analyze the dynamic performance evaluation matrix and extract the key performance feature spectrum that characterizes response hysteresis and load fluctuation from the dynamic performance evaluation matrix; Based on the key performance characteristic spectrum, core processing modules with targeted processing capabilities are matched from the pre-set module library, and auxiliary buffer modules that can dynamically complement the core processing modules in terms of flow and water quality fluctuations are selected. A set of asymmetric trigger thresholds is defined for the running data flow of the auxiliary buffer module, and an adaptive control logic that can drive the core processing module to perform parameter pre-adjustment is constructed based on the asymmetric trigger thresholds, thus completing the assembly and logic embedding of the basic processing unit.

[0009] As a further aspect of the present invention: S4 specifically includes: Based on the physical boundaries and internal process connection topology of the basic processing unit, multi-physics field coupling probe clusters are deployed at key nodes of material confluence, diversion and reaction interface. The multi-physics field coupling probe clusters synchronously collect hydraulic, chemical and biological multi-dimensional data in a preset spatial configuration. A miniature data collector with an adaptive sampling frequency is configured for a multi-physics coupled probe cluster. The collector dynamically adjusts its sampling period and wireless transmission power according to the gradient change rate of the received data to generate encrypted time-series data packets. Based on the wireless mesh network protocol, each micro data aggregator is connected to form a self-organizing network. Through layered data cleaning and spatiotemporal alignment fusion, a real-time monitoring network layer is constructed to continuously and undisturbedly perceive the internal state and input / output streams of the unit.

[0010] As a further aspect of the present invention: S5 specifically includes: The dynamic performance evaluation matrix is ​​time-stamped and causally correlated with the real-time data stream obtained from the real-time monitoring network layer to generate a full-dimensional state evolution sequence of the unit. From the state evolution sequence, the progressive performance loss components caused by equipment wear, packing performance degradation and microbial community succession are extracted and quantified. Based on the rate of change and interaction of performance loss components, an event chain deduction method is used to simulate the chain failure process inside the basic processing unit under different external disturbances, and to draw the performance degradation path and risk evolution map.

[0011] As a further aspect of the present invention: S6 specifically includes: Analyze the performance degradation path and risk evolution map to identify the core weak nodes formed by the intersection of multiple failure paths; Based on the failure mechanism and topological relationship of core weak nodes, specific functional enhancement module groups with multi-level buffering and functional redundancy characteristics are retrieved and combined from the pre-made module component library. The enhanced processing unit is seamlessly integrated into the basic processing unit using an in-situ embedded interface, and its anti-disturbance capability is verified through a preset load impact test, thereby completing the construction of the enhanced processing unit.

[0012] As a further aspect of the present invention: S7 specifically includes: The comprehensive performance fluctuation coefficient of the enhanced processing unit and the collaborative operation map of each module are continuously extracted from the real-time monitoring network layer, and the performance contribution entropy of the current state is calculated. Based on the distribution of efficiency contribution entropy, energy efficiency optimization module groups with process fine-tuning and energy recovery characteristics are allocated from the standard library as needed, and a causal knowledge stack driving the module groups is constructed based on the collaborative operation graph. By using non-intrusive flexible connections and online script injection, the energy efficiency optimization module group and causal knowledge stack are embedded into the enhanced processing unit, triggering a round of self-verification testing to complete the upgrade and reconstruction to the optimized processing unit.

[0013] As a further aspect of the present invention: S8 specifically includes: Extract the performance degradation trajectory of each module and the key event sequence of their coordinated operation from the real-time monitoring network layer and operation and maintenance history of the optimization processing unit; Based on the performance degradation trajectory and key event sequence, the independent state entropy of each module and the collaborative contribution between modules are calculated and dynamically weighted to generate a comprehensive health index for the entire life cycle. Based on the composition pattern of the comprehensive health index, the target operating conditions of newly built or expanded water plants are mapped to generate a modular configuration list and deployment topology map, which guides the rapid replication of standardized processing units and their adaptive parallel deployment within the target water plant.

[0014] A prefabricated water plant lifecycle management system includes a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, when the processor executes the computer program, it implements the steps of a prefabricated water plant lifecycle management system as described in any one of claims 1 to 9.

[0015] The beneficial effects of this invention are: (1) This invention, by constructing a "dynamic performance evaluation matrix" and a "real-time monitoring network layer," achieves for the first time in the field of water treatment the precise spatiotemporal location of "performance-vulnerable areas." Based on the deduction of "performance degradation paths and risk evolution maps," it can proactively identify weak links and failure risks in the process chain. This transforms the management strategy from post-treatment maintenance to pre-treatment reinforcement and in-process adaptive adjustment, improving the robustness and stability of the treatment process. This reduces the risk of effluent quality exceeding standards due to sudden disturbances and reduces energy and chemical consumption caused by frequent process adjustments.

[0016] (2) The evolution path of the "basic-enhancement-optimization" processing unit created by this invention, and the final output "comprehensive health index throughout the entire life cycle", establish a quantifiable "digital profile" for the performance of the processing unit. Based on this health index, "demand mapping" can be performed on new projects, and the optimal "modular configuration list" and "deployment topology map" can be directly generated. This allows the expansion and new construction of water plants to move away from traditional personalized design and transform into the rapid selection and splicing deployment of standardized "products", shortening the project cycle and ensuring that the new units have proven high performance and high reliability, thus achieving a simultaneous improvement in construction efficiency and operational quality. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] 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 embodiments of the present invention, and not all embodiments. 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.

[0020] Please see Figure 1 As shown, this invention is a method for full life-cycle management of prefabricated water plants, including the following steps: S1: Obtain the time-series data of the source water quality of the target water plant, the regional water load curve and the external environmental disturbance parameters. Based on the correlation between the frequency of water quality anomalies and the process adjustment delay, identify the performance vulnerability areas in the spatiotemporal distribution of the water treatment process. S2: For each performance vulnerability zone, collect its core operating parameter set under different operating conditions, and construct a dynamic performance evaluation matrix for the performance vulnerability zone; S3: Based on the dynamic performance evaluation matrix, configure the core processing module group H1 and deploy the auxiliary buffer module group H2 coupled with its process flow. At the same time, embed an adaptive control logic set that can be triggered by the H2 running status data, together forming a basic processing unit with initial self-adjustment capability. S4: Deploy a sensor network around the boundaries and key nodes of the basic processing unit to form a real-time monitoring network layer that senses its internal state and input / output flow. S5: Based on the collaborative analysis results of the dynamic performance evaluation matrix and the real-time monitoring network layer, the performance degradation path and risk evolution map of the basic processing unit in the preset full life cycle are deduced. S6: Based on the weak links revealed by the performance degradation path and risk evolution map, functional enhancement module group H3 is introduced into the basic processing unit to build an enhanced processing unit with enhanced anti-disturbance capability. S7: When the enhanced processing unit operates stably within a continuously set evaluation period, and the overall performance fluctuation coefficient of the enhanced processing unit decreases to a preset threshold, the optimization process is triggered. Energy efficiency optimization module group H4 and module-strategy co-optimizer are introduced into the enhanced processing unit to perform online reconstruction and performance upgrade of the enhanced processing unit, generating an optimized processing unit. S8: Calculate and output the comprehensive health index of the entire life cycle of the optimized treatment unit, and manage the replication and parallel deployment of standardized treatment units based on the comprehensive health index and the needs of new or expanded water plants.

[0021] In S1, time-series data of source water quality, regional water load curves, and external environmental disturbance parameters of the target water plant are acquired. Based on the correlation between the frequency of water quality anomalies and process adjustment delays, performance vulnerability zones in the spatiotemporal distribution of the water treatment process are identified, specifically including: First, data is collected from the target water plant. Time-series data of the source water quality is acquired through online water quality monitoring instruments. This time-series data includes measurements of ammonia nitrogen, turbidity, and chemical oxygen demand (COD) every five minutes. Simultaneously, regional water load curves for different time periods each day are extracted from the water plant's scheduling records, and temperature and rainfall intensity for the same period are obtained from meteorological monitoring stations as external environmental disturbance parameters. All of the above data is timestamped and stored in a database.

[0022] Secondly, a process response lag graph is constructed. Abnormal thresholds are set for various water quality parameters. Whenever the monitored data exceeds its corresponding abnormal threshold three times consecutively, it is recorded as a water quality time-series abnormal peak event, and the initial time of this peak event is marked. Subsequently, the time difference from this initial time to the moment when the water plant first issues and executes a process adjustment command (such as adjusting the dosage of chemicals or changing the filter backwash cycle) in response to this abnormality is retrieved and recorded in the operation log of the automatic control system. This time difference is the process adjustment delay. Each abnormal peak event and its corresponding process adjustment delay are plotted as a two-dimensional scatter plot with time as the x-axis and delay value as the y-axis. This plot is the process response lag graph.

[0023] Next, a spatiotemporal vulnerability surface is generated. The two-dimensional plane containing the process response hysteresis map is divided into several equally spaced grid cells along the time axis and the hysteresis time axis, with each grid cell defined as a hysteresis cell. The number of scatter points falling within each hysteresis cell is counted; this number represents the occurrence frequency of that cell. Then, the vulnerability value of each hysteresis cell is calculated by multiplying the hysteresis time value represented by the cell by the occurrence frequency value of that cell, and then multiplying by a preset time influence weighting factor. All hysteresis cells and their calculated vulnerability values ​​are spatially interpolated and smoothed on the two-dimensional plane to generate a continuous surface that characterizes the vulnerability level at each spatiotemporal point, i.e., the spatiotemporal vulnerability surface.

[0024] Finally, performance vulnerability zones are delineated. A vulnerability threshold is defined on the spatiotemporal vulnerability surface. A rectangular sliding window of a preset size is used to traverse the entire surface, calculating the average vulnerability value of all points within the window's coverage area. All adjacent window regions whose average vulnerability values ​​continuously exceed the vulnerability threshold are marked and aggregated. The high-vulnerability regions formed after aggregation, exhibiting continuity in time and process stages, are ultimately defined as performance vulnerability zones requiring targeted treatment.

[0025] In S2, for each performance-vulnerable region, a set of core operating parameters under different operating conditions is collected to construct a dynamic performance evaluation matrix for the performance-vulnerable region, specifically including: First, determine the spatiotemporal coordinates of the performance vulnerability zone. A performance vulnerability zone is defined by its start and end times on the time axis and its corresponding physical location within the water treatment process. From the performance vulnerability zone division results generated in the previous steps, directly extract the aforementioned time interval and physical location information; this information constitutes the spatiotemporal coordinates of the performance vulnerability zone. For example, a performance vulnerability zone might correspond to "every Monday morning from 8:00 to 10:00, located at the outlet section of flocculation tank number A."

[0026] Secondly, probes are deployed to form a multi-condition parameter array. Based on the physical location information in the aforementioned spatiotemporal coordinates, multiple sets of high-frequency sampling probes capable of simultaneously measuring hydraulic, water quality, and physical states are installed at designated points on the corresponding water treatment structures or pipelines. Each probe set includes at least sensors for measuring flow rate, pressure, turbidity, and pH, with a sampling frequency set to ten times per second. Simultaneously, a series of multi-condition switching trigger commands are preset in the programmable logic controller (PLC) in the water plant's central control room. These commands simulate possible load changes, reagent dosage variations, and other scenarios that may occur during actual operation. When a trigger command is executed, all the aforementioned probes are controlled to synchronously acquire data at the set high frequency for a period from five seconds before the command's execution to thirty seconds after. By repeatedly triggering different condition commands and acquiring data, the time-series data acquired from different probes are aligned and arranged according to time points to form a three-dimensional dataset, i.e., a multi-condition parameter array. The first dimension of this array represents different conditions, the second dimension represents different sampling time points, and the third dimension represents different measurement parameters.

[0027] Finally, data processing is performed to generate a dynamic performance evaluation matrix. A multi-dimensional orthogonal decomposition is performed on the data in the multi-condition parameter array. Specifically, a data processing method is used to independently extract the changing trends of the data in the array across three dimensions: time, space, and condition. The calculation process is as follows: first, the condition and parameter type are fixed, and the changing components of each probe's data over time are analyzed; then, the time and parameter type are fixed, and the differences in spatial distribution of data from different probes at the same time are analyzed; finally, the time and spatial location are fixed, and the changing components of the same parameter under different conditions are analyzed. These trend components decomposed from different dimensions are combined to form a comprehensive data structure that simultaneously reflects temporal evolution, spatial distribution, and condition response—the dynamic performance tensor. Next, the dynamic performance tensor is integrated and normalized: for each trend component value obtained through the above decomposition calculation, the minimum value of its corresponding data sequence is subtracted, and then divided by the difference between the maximum and minimum values ​​of the sequence, transforming each value to the range of 0 to 1. Finally, these normalized values ​​are reorganized into a two-dimensional table according to the original correspondence between time, space and operating conditions. The rows of the table represent different evaluation aspects (composed of combinations of time, space and operating condition trends), and the columns represent different performance vulnerability assessment moments or operating condition snapshots. This two-dimensional table is the required dynamic performance evaluation matrix.

[0028] In S3, based on the dynamic performance evaluation matrix, the core processing module group H1 is configured, and the auxiliary buffer module group H2, coupled with its process flow, is deployed. Simultaneously, an adaptive control logic set that can be triggered by the operating status data of H2 is embedded, together forming a basic processing unit with initial self-regulation capabilities, specifically including: First, the dynamic performance evaluation matrix is ​​analyzed and key performance feature spectra are extracted. The key performance feature spectra refer to the set of feature data quantified from the dynamic performance evaluation matrix, which centrally characterizes the performance of the processing unit in terms of response speed and resistance to fluctuations. The extraction process is as follows: for each row of data in the matrix (representing an evaluation aspect), its numerical changes at different continuous time points or operating conditions are calculated. Specifically, the feature row that reflects "response hysteresis" is selected. This row describes the time required for the unit to stabilize its output after receiving a disturbance signal. By calculating the difference between the values ​​at adjacent time points in this row, and accumulating the differences exceeding a preset time difference threshold, a hysteresis index is obtained. Simultaneously, the feature row that reflects "load fluctuation" is selected. This row describes the fluctuation of the unit's processed water volume or pollutant concentration. By calculating the difference between the maximum and minimum values ​​in this row and dividing it by the average value of the row, a fluctuation amplitude index is obtained. The calculated hysteresis index, fluctuation amplitude index, and other relevant feature indices are arranged in a fixed order into a one-dimensional data sequence; this sequence is the key performance feature spectrum.

[0029] Secondly, processing modules are matched and selected based on key performance characteristic spectra. A pre-built module library is a data list containing various water treatment functional units (such as specific types of filters, reactors, dosing devices, etc.) and their performance parameters. The key performance characteristic spectra obtained in the previous step are compared one by one with the nominal performance parameter range of each functional unit in the module library. The matching process is as follows: the hysteresis index in the characteristic spectrum is compared with the nominal "response time" parameter of the unit in the library, and the unit with a response time shorter than the requirement corresponding to the hysteresis index is selected as a candidate core processing module; simultaneously, the fluctuation amplitude index in the characteristic spectrum is compared with the nominal "treatment load elasticity range" parameter of the unit in the library. Finally, the unit that best matches the characteristic spectrum requirements in terms of treatment performance is selected as the core processing module. Subsequently, based on the influent water quality requirements and effluent flow characteristics of this core processing module, one or more functional units that can buffer water quality fluctuations at the upstream end or regulate flow mutations at the downstream end are selected from the module library and identified as auxiliary buffer modules. For example, if the core module is sensitive to fluctuations in influent turbidity, a pre-positioned micro-flocculation buffer tank can be matched as an auxiliary buffer module.

[0030] Finally, the trigger thresholds are defined, and the assembly and logic embedding are completed. A specific definition of the asymmetric trigger threshold is given: it is two different sets of numerical limits set for one or more operating data points of the auxiliary buffer module (such as effluent turbidity or tank level). One set is an upward threshold used to trigger an alarm or adjustment, and the other is a downward threshold used to clear the alarm or restore the system. The upward threshold is numerically higher than the downward threshold. It is constructed by using the median of the historical operating data of the auxiliary buffer module, increasing it by 20% and decreasing it by 15%. Adaptive control logic refers to a series of "if-then" format conditional execution rules. Its construction process is as follows: a rule is written stipulating that when the real-time operating data of the auxiliary buffer module exceeds its upward threshold for three consecutive seconds, a preset instruction is automatically sent to the core processing module. This instruction is used to adjust a key operating parameter of the core processing module (such as backwash frequency) to a higher preset value in advance; when the data decreases and remains below its downward threshold for five consecutive seconds, an instruction is sent to restore the parameter. The matched core processing module and auxiliary buffer module are connected and assembled with pipes according to the process flow, and the written adaptive control logic rule set is written into the programmable controller attached to the assembly, thus completing the construction and logic embedding of the basic processing unit.

[0031] In S4, a sensor network is deployed around the boundaries and key nodes of the basic processing unit to form a real-time monitoring network layer that senses its internal state and input / output flow. Specifically, this includes: First, a multi-physics coupled probe cluster is deployed. Based on the process flow diagram and physical dimensions of the basic processing unit, the three-dimensional coordinates of its physical boundaries and key nodes such as internal material confluence points, divergence points, and interfaces with the biochemical reaction tank are determined. At each key node, four sensors are installed in a group according to a tetrahedral spatial configuration, forming a probe cluster. A sensor is deployed at each of the four vertices of this tetrahedron: a vortex flow meter is installed at the first vertex to collect hydraulic data (instantaneous flow); a composite electrode is installed at the second vertex to collect chemical data (pH value and redox potential); a fluorescence-based dissolved oxygen and microbial sensor is installed at the third vertex to collect bio-data (dissolved oxygen concentration and specific bacterial community activity); and a temperature and pressure sensor is installed at the fourth vertex. These four sensors, triggered by a unified synchronization signal, achieve synchronous acquisition and timestamping of all physical field data every second.

[0032] Secondly, a miniature data aggregator with an adaptive sampling frequency is configured. One miniature data aggregator is connected to each of the aforementioned probe clusters. This aggregator has a built-in computing unit, and its sampling period is adjusted according to the following rule: it calculates in real time the absolute value of the change in a key data point (e.g., traffic flow) received from the probe cluster relative to the data from the previous second; this absolute value is the gradient rate of change. A baseline sampling period of one second is set. When the calculated gradient rate of change is lower than a preset first threshold three times consecutively, the operating condition is considered stable, and the sampling period is automatically extended to five seconds; when the gradient rate of change exceeds a preset second threshold, the operating condition is considered abruptly changed, and the sampling period is immediately shortened to 0.2 seconds. Simultaneously, its wireless transmission power is adjusted according to the following rule: the transmission power is directly proportional to the average value of the gradient rate of change of all data within the current sampling period; the larger the average value, the higher the transmission power is to ensure real-time data transmission. The aggregator transmits the packaged time-series data, with an added encryption checksum, through its wireless communication unit.

[0033] Finally, a wireless mesh network is constructed and data fusion processing is completed. All micro data aggregators are configured to operate under the same wireless communication protocol that supports multi-hop routing. After power-on, each aggregator automatically searches for other aggregators within its communication range and establishes connections, forming a mesh network that can dynamically adjust its path. After the data is transmitted to the designated central receiving point, a layered data cleaning process is performed: the first layer of cleaning removes obviously erroneous data based on the sensor's measurement range; the second layer of cleaning utilizes the physical correlation of data from different sensors within the same probe cluster for cross-verification and repair. Spatiotemporal alignment fusion refers to fine-tuning and aligning the timestamps of data collected by all other aggregators using the clock of a central aggregator as a reference; simultaneously, a spatial sequence relationship of data is established based on the order of each probe cluster in the process flow. After the above networking, cleaning, and alignment fusion steps, a real-time monitoring network layer is finally formed that can continuously and non-disruptibly perceive the internal state and input / output streams of the basic processing unit.

[0034] In S5, based on the collaborative analysis results of the dynamic performance evaluation matrix and the real-time monitoring network layer, the performance degradation path and risk evolution map of the basic processing unit within a preset lifecycle are deduced, specifically including: First, time-stamp alignment and causal correlation fusion are performed to generate a full-dimensional state evolution sequence for the unit. A full-dimensional state evolution sequence refers to a data set arranged chronologically and completely recording the states of various aspects of the processing unit. The implementation process involves continuously acquiring real-time data streams containing timestamps from the real-time monitoring network layer. This data stream includes at least flow rate, key water quality parameters (such as turbidity and ammonia nitrogen), equipment operating current, and signals from key biosensors. Simultaneously, a previously constructed dynamic performance evaluation matrix is ​​invoked, where each row represents a snapshot of a performance characteristic at different historical moments. Alignment refers to finding the corresponding historical performance characteristic data row within ten minutes of the real-time data stream's timestamp in the dynamic performance evaluation matrix and concatenating it with the real-time data. Causal correlation fusion involves establishing causal relationship rules between data based on process knowledge; for example, "increased turbidity in sedimentation tank effluent" is the cause of "increased backwashing frequency in filter beds." By retrieving aligned data, when a "cause" data (such as turbidity) is identified as exceeding its normal threshold, the state of the associated "result" data (such as backwashing frequency) is marked in subsequent time windows, thus adding a causal label to the simple data sequence. All historical performance data and real-time data that have been aligned and causally labeled are arranged in chronological order to form a sequence describing the complete state change of a unit from a certain point in the past to the present, i.e., the unit's full-dimensional state evolution sequence.

[0035] Secondly, the progressive performance loss component caused by equipment wear, packing material performance degradation, and microbial community succession is extracted and quantified from the state evolution sequence. The performance loss component refers to the irreversible decrease in performance caused by a specific physical or biological process, expressed as a percentage. The extraction and quantification process consists of three steps. Step 1: Data extraction and baseline setting. Three sets of long-term trend data are extracted from the state evolution sequence: 1) parameters characterizing the mechanical state of the equipment, such as pump efficiency, calculated through the relationship between input current and output head and flow rate; 2) parameters characterizing packing material performance, such as the initial head loss of the filter unit under constant influent conditions; 3) parameters characterizing microbial activity, such as the degradation rate of a specific pollutant per unit time in the biochemical reaction tank. An average value of the initial stable operating period (e.g., the first 30 days) is set as the performance baseline for each set of data, denoted as 100%. Step 2: Calculation of each independent loss component. For each set of data, the percentage decrease in value relative to its baseline at each subsequent time point (e.g., every ten days). For example, the... The pump efficiency at each time point is If its baseline efficiency is E_0, then the efficiency loss component caused by equipment wear is... It can be calculated using the following formula: ; Similarly, the component of packing performance degradation loss L_media(t) and the component of microbial effectiveness loss can be calculated. The third step is to separate the mutually coupled effects. Since the actual losses are coupled, further separation is needed. A simultaneous calculation relationship involving three equations is established. The core idea is that, over a short period, the changes in the three types of losses have a cumulative effect on the overall performance observation, and each has its own influence coefficient. By analyzing data from multiple time points in the sequence, these three coefficients can be solved inversely, thus accurately decomposing the observed overall performance decline into three independent loss components. , , The curves showing how these independent components change over time are the quantified asymptotic performance loss components.

[0036] Finally, based on the rate of change and interaction of the performance loss components, an event chain deduction method is used to simulate the cascading failure process and generate a graph. Event chain deduction refers to simulating how an initial abnormal event triggers a series of subsequent events, ultimately leading to functional failure. The implementation steps are as follows: First, determine the initial disturbance and failure rules. Set a series of typical external disturbances, such as water inflow load impact, sudden failure of critical equipment, or sudden change in ambient temperature. At the same time, based on engineering experience and historical data, set failure thresholds for each component within the processing unit and the above three loss components. For example, specify when... When the cumulative loss reaches 25%, the corresponding equipment enters a high-risk failure state. Second, construct an interaction network. Nodes represent the various sub-components within the unit (such as pumps, filter beds, and biological treatment tanks) and their current loss component states. Directed lines represent the influence relationships between them (e.g., "increased head loss in the filter bed" leads to "increased pump load, thus accelerating wear"). Each line is assigned a probability weight, representing the likelihood that a precursor event will trigger a subsequent event. This weight is estimated by analyzing the frequency of event co-occurrence in the historical state evolution sequence. Third, perform iterative deduction and graph plotting. The deduction begins with applying an external perturbation. For example, simulating the event of "instantaneous doubling of influent ammonia nitrogen concentration." First, this event directly impacts the microbial community. Based on the quantified relationships in the second step, a temporary... Incremental. Then, check if there are any node states (such as updated ones). If the threshold is exceeded or a drastic change occurs, one or more subsequent events are randomly triggered according to probability weights based on the interaction network (e.g., "decreased microbial treatment capacity leads to automatic increase in front-end chemical dosage"). Newly triggered events then act as causes, continuing to check and potentially triggering new events. This process iterates until no new events are triggered or the ultimate failure state, such as "effluent water quality exceeding standards," is deduced. This deduction process is repeated hundreds or thousands of times, statistically analyzing the typical event sequence (i.e., performance degradation path) experienced from the beginning to the final failure under each external disturbance, as well as the risk exposure frequency of each sub-component during the failure process. Finally, the frequently occurring degradation paths are plotted as flowcharts, and the risk frequencies of each component at different deduction stages are marked as heatmaps on the unit process flow diagram, collectively forming the performance degradation path and risk evolution map.

[0037] In S6, based on the weaknesses revealed by the performance degradation path and risk evolution map, a functional enhancement module group H3 is introduced into the basic processing unit to construct an enhanced processing unit with improved disturbance resistance, specifically including: First, the performance degradation path and risk evolution graph are analyzed to identify core weak points. Core weak points refer to water treatment sub-components or process stages that are targeted by more than three independent failure paths in the performance degradation path and risk evolution graph, and whose risk exposure frequency ranks in the top 10%. The process involves: examining each performance degradation path recorded in the graph one by one, and recording all nodes (i.e., sub-components or stages) that the path passes through. A node list is created, and the number of times each node is hit by different paths in the entire graph is counted. Simultaneously, the risk exposure frequency data for each node is extracted from the risk evolution graph; this frequency refers to the proportion of times the node enters a high-risk state in all simulations out of the total number of simulations. The number of path hits for each node is multiplied by its risk exposure frequency to obtain a comprehensive weakness index. All nodes are sorted from high to low by their comprehensive weakness indices, and the top 10% of nodes are identified as core weak points. For example, a certain type of booster pump may be identified as a failure starting point in multiple paths, and its risk frequency is extremely high, thus being identified as a core weak point of that unit.

[0038] Secondly, functional enhancement module groups are combined based on failure mechanisms. For each identified core weak node, its main failure modes shown in the graph are analyzed. For example, if the failure of a node is mainly manifested as its inability to cope with instantaneous flow surges, its failure mechanism is "insufficient resistance to hydraulic load surges". The pre-fabricated module component library is a database containing various water treatment standard components with enhanced functions (such as buffer tanks of different volumes, security filters of different precision, and different types of backup flow path switching valves) and their performance specifications. According to the failure mechanism of the node and its upstream and downstream topological position in the process flow (i.e., the links directly connected to it before and after), it is retrieved and matched from this library. The principle of combination is to form multi-level buffers and functional redundancy: for the node with "insufficient resistance to hydraulic load surges", a buffer tank with a larger regulating volume (first-level buffer) and a parallel bypass pipeline with a smaller capacity backup pump are deployed upstream (functional redundancy). The set of standard components matched and combined for all core weak nodes is defined as a specific functional enhancement module group.

[0039] Finally, in-situ embedded interface integration and disturbance rejection capability were verified. The in-situ embedded interface refers to a prefabricated integrated connection device containing standard flanges, quick-plug circuits, and signal connectors, whose dimensions and interface specifications perfectly match the modification ports reserved on the basic processing unit. During implementation, firstly, in the process flow of the basic processing unit, the pipes or circuits are disconnected at the location where the reinforcement module is planned to be inserted, and the female end of the interface is installed. Subsequently, the piping and cables of the functional reinforcement module group itself are terminated to the male end interface matching the female end. Through simple physical docking and locking, seamless integration of the module group in fluid channels, power supply, and data communication can be achieved. After integration, a preset load impact test is initiated for verification: a typical external disturbance that previously caused the original unit to fail (such as a 50% increase in influent flow rate within 30 seconds) is simulated and continuously run for 30 minutes, with data collected through real-time monitoring of the network layer. The verification standard is that, during the testing period, the fluctuation range of key water quality parameters in the enhanced unit's effluent should be reduced to within 60% of the fluctuation range of the original unit under the same test conditions, and no new fault alarms should be triggered. Passing this test is considered a successful construction of the enhanced treatment unit.

[0040] In S7, when the enhanced processing unit operates stably within a continuously set evaluation period, and the overall performance fluctuation coefficient of the enhanced processing unit decreases to a preset threshold, an optimization process is triggered. This introduces the energy efficiency optimization module group H4 and the module-strategy co-optimizer into the enhanced processing unit, performing online reconstruction and performance upgrades to generate an optimized processing unit. Specifically, this includes: First, key data is extracted and the efficiency contribution entropy is calculated. A continuous evaluation period of 72 hours is set. Within this period, the comprehensive efficiency fluctuation coefficient of the enhanced treatment unit is collected every minute from the real-time monitoring network layer. This coefficient is obtained by calculating the ratio of the standard deviation to the average value of key effluent water quality parameters (such as turbidity and ammonia nitrogen) per unit time. Simultaneously, the operating status parameters (such as frequency, pressure, and current) of each treatment module (such as pumps, filters, and reactors) are recorded every ten minutes, forming a collaborative operation map describing their combined operating points at the same time. The efficiency contribution entropy of the current state is calculated as follows: from the collaborative operation map of the most recent evaluation period, the proportion of time each module spends in its optimal efficiency range (defined by the equipment manual) is statistically analyzed, and this proportion is taken as the stable contribution of that module. The specific calculation method for the efficiency contribution entropy is to first normalize the stable contribution values ​​of all modules so that their sum is 100%, thus obtaining the contribution ratio of each module. Next, using the concept of information entropy, the contribution ratio of each module is multiplied by the base-2 logarithm of that ratio. The sum of these products for all modules is then taken as a negative value. This result is the efficiency contribution entropy; a larger value indicates a more balanced contribution from each module and better system synergy.

[0041] Secondly, the optimization module group is allocated and a causal knowledge stack is constructed based on the entropy distribution. The distribution of the calculated efficiency contribution entropy is analyzed, specifically checking whether the contribution ratio of each module is severely unbalanced (e.g., one module's ratio exceeds 50% while others are all below 10%). The standard library is a database containing various energy-saving and optimization components, such as micro-metering pumps for precise control of chemical dosage, small turbine generators that can recover water flow energy, or fine-tuning valves for optimizing aeration. Based on the bottleneck modules (i.e., modules with excessively high contribution ratios) revealed by the entropy distribution and their functions, components that can assist or replace them are allocated from the standard library as needed. These components must have process fine-tuning or energy recovery characteristics to collectively form an energy efficiency optimization module group. For example, if a dosing pump is found to have an excessively high contribution ratio and frequent start-stop cycles, a precision metering pump with a small-capacity buffer tank is allocated as an optimization module. The process of constructing the causal knowledge stack that drives this module group is as follows: Based on the collaborative operation graph, analyze which upstream or parallel modules in the process chain (such as increased water flow) will regularly appear when the bottleneck module has a specific working state (such as high current). Describe and record these "cause-effect" state pairs using the rule form of "if [cause state], then adjust [optimization module] to [target value]", forming a series of rule sets arranged in priority, i.e., the causal knowledge stack.

[0042] Finally, online reconstruction and verification are completed through flexible connections and script injection. Non-invasive flexible connections refer to using self-sealing flexible hoses and prefabricated cable connectors to connect the energy efficiency optimization module group to a designated point on the enhanced processing unit without cutting the main pipeline or permanently altering the original circuitry. Online script injection refers to remotely uploading and writing the causal knowledge stack constructed in the previous step into its control memory in the form of a script program through the communication interface reserved by the original programmable controller of the enhanced processing unit. After completing the physical connection and logical injection, a self-verification test is triggered: the system automatically executes a two-hour simulation, during which it actively triggers a preset "cause state" in the causal knowledge stack, observes whether the optimization module group responds correctly according to the rules, and monitors the overall comprehensive performance fluctuation coefficient. If, after the test, the new comprehensive performance fluctuation coefficient decreases by more than 15% compared to before reconstruction, and no new alarms are generated, the online reconstruction and performance upgrade are considered successful, and the enhanced processing unit is then defined as the optimization processing unit.

[0043] In S8, the overall health index of the optimized treatment unit throughout its entire lifecycle is calculated and output. Based on the overall health index and the needs of new or expanded water plants, the replication and parallel deployment management of standardized treatment units is carried out, specifically including: First, the performance degradation trajectory of each module and the key event sequence of their coordinated operation are extracted from the real-time monitoring network layer and operation and maintenance history of the optimization processing unit. The performance degradation trajectory refers to the curve showing the percentage decrease in the core performance parameters (such as efficiency and differential pressure) of each independent functional module (e.g., pump, filter) relative to its initial value over time throughout its entire operating history. During extraction, the periodic maintenance and testing data and performance test reports of each module are retrieved from the operation and maintenance history database in chronological order. The performance percentage of each test is plotted on a graph with the test date as the horizontal axis, and the points are connected to form the trajectory. The key event sequence refers to a list of events recording the mutual influence of different modules' operating states, sorted by timestamp. During extraction, the high-frequency data stored in the real-time monitoring network layer is analyzed, and events are identified through preset rules. For example, when "the outflow rate of module A decreases by more than 20% within 3 seconds," it is checked whether "the inlet pressure of module B increases by more than 15%" within the following 2 minutes. If the conditions are met, "the decrease in flow rate of A causes the increase in pressure of B" is recorded as an event pair along with the occurrence time. By traversing all historical data, we can identify all such event pairs with strong temporal correlations and sort them by time to obtain the key event sequence.

[0044] Secondly, based on the performance degradation trajectory and key event sequence, the independent state entropy of each module and the collaborative contribution between modules are calculated and dynamically weighted to generate a comprehensive health index for the entire lifecycle. The independent state entropy of a module is used to quantify the uncertainty or disorder of the operating state of a single module. Its calculation process is as follows: for a module, a key operating parameter (such as current) is selected from its real-time monitoring data, and its possible value range is divided into 10 equally wide intervals; the frequency of the parameter value falling into each interval in the past month is counted as the probability of that interval; the state entropy is calculated by multiplying all these probability values ​​by their base-2 logarithms, summing the resulting products, and finally taking the negative number. The collaborative contribution between modules is used to quantify the efficiency of multiple modules working collaboratively as a whole. Its calculation process is as follows: based on the key event sequence, the percentage of events belonging to "benign collaboration" (such as the optimization of one module's state leading to the optimization of another module's state) out of the total number of events is counted. Finally, dynamic weighted fusion is performed: a weight is assigned to the independent state entropy of each module, equal to the proportion of that module's criticality score (determined by the design manual, e.g., a booster pump scores 5, a regular valve scores 1) in the total score of all modules. The weighted state entropies of all modules are summed and multiplied by the inter-module collaborative contribution. The resulting value is linearly transformed to the range of 0 to 100, which is the overall life-cycle health index. The higher the index, the better the overall health and collaborative status of the unit.

[0045] Finally, based on the composition pattern of the comprehensive health index, demand mapping is performed on the target operating conditions of newly built or expanded water plants to generate a modular configuration list and deployment topology map. This guides the rapid replication of standardized treatment units and their adaptive parallel deployment within the target water plant. Demand mapping involves matching the target water plant's design influent water quality, planned treatment volume, and land constraints with the historical operating condition data of optimized treatment units with high health indices. The matching method calculates the Euclidean distance between the target operating condition parameters and historical operating condition parameters in various dimensions, selecting the three historical operating conditions with the smallest distances as references. The modular configuration list directly generates a procurement and assembly list based on the specific module models, quantities, and parameter lists of the verified healthy optimized treatment units corresponding to these reference operating conditions. The deployment topology map combines the target water plant's site plan and overall process flow diagram, scaling or adaptively adjusting the modules in the list according to their relative positions and connection logic in the original optimized units, to draw the specific installation locations and pipeline / line connection diagrams within the new plant space. Based on this list and diagram, construction workers can quickly replicate and deploy standardized processing units in parallel. The newly deployed units, being based on high-health prototypes and mapped to operating conditions, have the potential to adapt to new environments.

[0046] The present invention also includes a prefabricated water plant life cycle management system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, when the processor executes the computer program, it implements the steps of the prefabricated water plant life cycle management as described in any one of claims 1 to 9.

[0047] The working principle of this invention is as follows: First, based on the time-series data of the target water plant's source water quality, water load curve, and environmental disturbance parameters, and according to the lag relationship between water quality anomalies and process adjustments, a process response lag map is constructed, and a spatiotemporal vulnerability surface is generated to identify performance vulnerability zones in the spatiotemporal distribution. Second, for each performance vulnerability zone, high-frequency sampling probes are simultaneously deployed at its corresponding process location. Core operating parameter sets are collected by triggering multi-condition switching commands, forming a multi-condition parameter array. This array is then processed through multi-dimensional orthogonal decomposition and integration normalization to construct a dynamic performance evaluation matrix for that region. Then, based on this matrix, key performance feature spectra characterizing response lag and load fluctuations are extracted. Accordingly, a core processing module and its process-coupled auxiliary buffer module are matched from a pre-set module library. An asymmetric trigger threshold is defined for the operating data of the auxiliary buffer module, thereby constructing adaptive control logic that can drive the core processing module to pre-adjust. All these components are then assembled into a basic processing unit with initial self-regulation capabilities. Next, around the unit's boundaries and key nodes, a multiphysics-coupled probe cluster in a tetrahedral configuration was deployed to synchronously collect hydraulic, chemical, and biological data. A miniature data collector was configured for each probe cluster, capable of dynamically adjusting the sampling period and transmission power based on the data gradient change rate. These probes were connected via a self-organizing wireless mesh network protocol. After layered data cleaning and spatiotemporal alignment and fusion, a real-time monitoring network layer was formed. Subsequently, the dynamic performance evaluation matrix and the data from the real-time monitoring network layer were time-scaled and causally correlated to generate a full-dimensional state evolution sequence for the unit. From this sequence, the progressive performance loss components caused by equipment wear, packing material deterioration, and microbial succession were extracted and quantified. Then, an event chain extrapolation method was used to simulate the cascading failure process under different external disturbances, mapping the performance degradation path and risk evolution spectrum throughout the entire lifecycle. Subsequently, based on the core weak nodes revealed by the graph, which are formed by the convergence of multiple failure paths, functional reinforcement modules with multi-level buffering and redundancy characteristics are retrieved and combined from the prefabricated module library. These modules are then integrated into the basic processing unit via an in-situ embedded interface. After load impact testing and verification, the reinforcement processing unit is constructed. When the reinforcement processing unit operates stably within a continuous evaluation cycle and its overall performance fluctuation coefficient decreases to a preset threshold, its performance contribution entropy is calculated. Based on the entropy distribution, energy efficiency optimization modules with process fine-tuning and energy recovery characteristics are allocated from the standard library. A causal knowledge stack is constructed based on the collaborative operation graph. The two are then embedded through non-intrusive flexible connections and online script injection. After self-verification testing, online reconstruction and performance upgrades are completed, generating the optimized processing unit.Finally, the performance degradation trajectory of each module and the key event sequence of collaborative operation are extracted from the monitoring network and operation and maintenance records of the optimized processing unit. The independent state entropy of each module and the collaborative contribution between modules are calculated and dynamically weighted to generate a comprehensive health index for the entire life cycle. Based on the composition pattern of this index, the target operating conditions of the newly built or expanded water plant are mapped to generate a modular configuration list and deployment topology map, thereby guiding the rapid replication and adaptive parallel deployment of standardized processing units.

[0048] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for full life-cycle management of prefabricated water plants, characterized in that, Includes the following steps: S1: Obtain the time-series data of the source water quality of the target water plant, the regional water load curve and the external environmental disturbance parameters. Based on the correlation between the frequency of water quality anomalies and the process adjustment delay, identify the performance vulnerability areas in the spatiotemporal distribution of the water treatment process. S2: For each performance vulnerability zone, collect its core operating parameter set under different operating conditions, and construct a dynamic performance evaluation matrix for the performance vulnerability zone; S3: Based on the dynamic performance evaluation matrix, configure the core processing module group H1 and deploy the auxiliary buffer module group H2 coupled with its process flow. At the same time, embed an adaptive control logic set that can be triggered by the H2 running status data, together forming a basic processing unit with initial self-adjustment capability. S4: Deploy a sensor network around the boundaries and key nodes of the basic processing unit to form a real-time monitoring network layer that senses its internal state and input / output flow. S5: Based on the collaborative analysis results of the dynamic performance evaluation matrix and the real-time monitoring network layer, the performance degradation path and risk evolution map of the basic processing unit in the preset full life cycle are deduced. S6: Based on the weak links revealed by the performance degradation path and risk evolution map, functional enhancement module group H3 is introduced into the basic processing unit to build an enhanced processing unit with enhanced anti-disturbance capability. S7: When the enhanced processing unit operates stably within a continuously set evaluation period, and the overall performance fluctuation coefficient of the enhanced processing unit decreases to a preset threshold, the optimization process is triggered. Energy efficiency optimization module group H4 and module-strategy co-optimizer are introduced into the enhanced processing unit to perform online reconstruction and performance upgrade of the enhanced processing unit, generating an optimized processing unit. S8: Calculate and output the comprehensive health index of the entire life cycle of the optimized treatment unit, and manage the replication and parallel deployment of standardized treatment units based on the comprehensive health index and the needs of new or expanded water plants.

2. The method for full life-cycle management of prefabricated water plants according to claim 1, characterized in that, S1 specifically includes: By mapping the time-series abnormal peak values ​​of water quality with the time delay data of the corresponding process adjustment commands, a process response hysteresis spectrum is obtained. Based on the process response hysteresis map, frequency decomposition and vulnerability calculation are performed on each hysteresis unit to generate a spatiotemporal vulnerability surface. Based on the continuous high-value regions in the vulnerability surface, the effectiveness vulnerability zone is obtained by aggregation and division.

3. The method for full life-cycle management of prefabricated water plants according to claim 1, characterized in that, S2 specifically includes: Obtain the spatiotemporal coordinates of the performance vulnerability zone; Based on spatiotemporal coordinates, multiple sets of high-frequency sampling probes are simultaneously deployed on the corresponding water treatment process unit, and multi-condition switching trigger commands are preset to capture the ultra-short-cycle synchronous changes of each operating parameter before and after the instantaneous switching of operating conditions, forming a multi-condition parameter array. The data in the multi-condition parameter array are decomposed into a multi-dimensional orthogonal structure to form a dynamic performance tensor containing time, space and condition dimensions. The dynamic performance tensor is then integrated and normalized into a dynamic performance evaluation matrix.

4. The method for full life-cycle management of prefabricated water plants according to claim 1, characterized in that, S3 specifically includes: Analyze the dynamic performance evaluation matrix and extract the key performance feature spectrum that characterizes response hysteresis and load fluctuation from the dynamic performance evaluation matrix; Based on the key performance characteristic spectrum, core processing modules with targeted processing capabilities are matched from the pre-set module library, and auxiliary buffer modules that can dynamically complement the core processing modules in terms of flow and water quality fluctuations are selected. A set of asymmetric trigger thresholds is defined for the running data flow of the auxiliary buffer module, and an adaptive control logic that can drive the core processing module to perform parameter pre-adjustment is constructed based on the asymmetric trigger thresholds, thus completing the assembly and logic embedding of the basic processing unit.

5. The method for full life-cycle management of prefabricated water plants according to claim 1, characterized in that, S4 specifically includes: Based on the physical boundaries and internal process connection topology of the basic processing unit, multi-physics field coupling probe clusters are deployed at key nodes of material confluence, diversion and reaction interface. The multi-physics field coupling probe clusters synchronously collect hydraulic, chemical and biological multi-dimensional data in a preset spatial configuration. A miniature data collector with an adaptive sampling frequency is configured for a multi-physics coupled probe cluster. The collector dynamically adjusts its sampling period and wireless transmission power according to the gradient change rate of the received data to generate encrypted time-series data packets. Based on the wireless mesh network protocol, each micro data aggregator is connected to form a self-organizing network. Through layered data cleaning and spatiotemporal alignment fusion, a real-time monitoring network layer is constructed to continuously and undisturbedly perceive the internal state and input / output streams of the unit.

6. The method for full life-cycle management of prefabricated water plants according to claim 1, characterized in that, S5 specifically includes: The dynamic performance evaluation matrix is ​​time-stamped and causally correlated with the real-time data stream obtained from the real-time monitoring network layer to generate a full-dimensional state evolution sequence of the unit. From the state evolution sequence, the progressive performance loss components caused by equipment wear, packing performance degradation and microbial community succession are extracted and quantified. Based on the rate of change and interaction of performance loss components, an event chain deduction method is used to simulate the chain failure process inside the basic processing unit under different external disturbances, and to draw the performance degradation path and risk evolution map.

7. The method for full life-cycle management of prefabricated water plants according to claim 1, characterized in that, S6 specifically includes: Analyze the performance degradation path and risk evolution map to identify the core weak nodes formed by the intersection of multiple failure paths; Based on the failure mechanism and topological relationship of core weak nodes, specific functional enhancement module groups with multi-level buffering and functional redundancy characteristics are retrieved and combined from the pre-made module component library. The enhanced processing unit is seamlessly integrated into the basic processing unit using an in-situ embedded interface, and its anti-disturbance capability is verified through a preset load impact test, thereby completing the construction of the enhanced processing unit.

8. The method for full life-cycle management of prefabricated water plants according to claim 1, characterized in that, S7 specifically includes: The comprehensive performance fluctuation coefficient of the enhanced processing unit and the collaborative operation map of each module are continuously extracted from the real-time monitoring network layer, and the performance contribution entropy of the current state is calculated. Based on the distribution of efficiency contribution entropy, energy efficiency optimization module groups with process fine-tuning and energy recovery characteristics are allocated from the standard library as needed, and a causal knowledge stack driving the module groups is constructed based on the collaborative operation graph. By using non-intrusive flexible connections and online script injection, the energy efficiency optimization module group and causal knowledge stack are embedded into the enhanced processing unit, triggering a round of self-verification testing to complete the upgrade and reconstruction to the optimized processing unit.

9. The method for full life-cycle management of prefabricated water plants according to claim 1, characterized in that, S8 specifically includes: Extract the performance degradation trajectory of each module and the key event sequence of their coordinated operation from the real-time monitoring network layer and operation and maintenance history of the optimization processing unit; Based on the performance degradation trajectory and key event sequence, the independent state entropy of each module and the collaborative contribution between modules are calculated and dynamically weighted to generate a comprehensive health index for the entire life cycle. Based on the composition pattern of the comprehensive health index, the target operating conditions of newly built or expanded water plants are mapped to generate a modular configuration list and deployment topology map, which guides the rapid replication of standardized processing units and their adaptive parallel deployment within the target water plant.

10. A prefabricated water plant lifecycle management system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a prefabricated water plant full life cycle management as described in any one of claims 1 to 9.

Images