Distributed multi-water-source cooperative control system based on integrated wall-mounted washing closestool

Abstract

The invention relates to the technical field of integrated wall-mounted laundry closestools, and discloses a distributed multi-water-source cooperative control system based on an integrated wall-mounted laundry closestool, which comprises a multi-source water quality monitoring module, a flow dynamic distribution module, a cleaning strategy generation module, a pipeline deposition correction module and a water source switching optimization module. The multi-source water quality monitoring module acquires water quality parameters in real time and constructs a multi-layer evaluation topology, the flow dynamic distribution module performs pipeline modeling and pressure optimization, and the cleaning strategy generation module combines water quality and pressure information to generate a flushing framework and sorts the flushing framework. The pipeline deposition correction module corrects the deposition rate and balances pressure parameters, and the water source switching optimization module detects path deviation and feeds back iteration. The system realizes multi-source water quality accurate monitoring, flow dynamic distribution and cleaning strategy intelligent generation, improves the utilization efficiency of water resources, ensures stable operation of the system, and solves the problems of extensive water quality monitoring, static flow distribution, lack of intelligent cleaning strategy and the like of a traditional system.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated wall-mounted laundry toilets, and in particular to a distributed multi-water source collaborative control system based on the integrated wall-mounted laundry toilet. Background Art

[0002] In the current field of bathroom equipment technology, with the increasingly severe problem of water shortage and the continuous improvement of people's requirements for water conservation and environmental protection, how to efficiently utilize multiple water sources for coordinated control has become an important issue that needs to be solved urgently. Traditional laundry toilet systems have many shortcomings in water source utilization. The lack of an accurate monitoring and evaluation mechanism for the quality of water from multiple sources makes it difficult to make reasonable use of the water quality characteristics of different water sources (such as hardness characteristic values, suspended solids concentration, etc.). As a result, water sources with poor water quality may be irrationally used in cleaning scenarios with high water quality requirements, affecting the cleaning effect, and may also cause waste of high-quality water sources. In terms of flow distribution, traditional systems usually adopt a fixed distribution mode and cannot be adjusted in real time according to dynamic factors such as pipeline pressure distribution and water quality change rate. This static flow distribution method can easily lead to uneven pressure in the pipeline, which in turn causes problems such as flow rate fluctuations and impurity accumulation. It not only affects the normal operating efficiency of the system, but also may increase the risk of pipeline blockage and reduce the service life of the equipment.

[0003] Traditional systems also lack flexibility and intelligence in generating cleaning strategies. They cannot effectively integrate water quality monitoring data and flow distribution to generate scientifically sound flushing strategies, making it difficult to maximize water conservation while ensuring cleaning effectiveness. Furthermore, when problems such as water quality anomalies and pipe sedimentation arise, traditional systems lack effective response mechanisms, lack effective correction and optimization measures, and are unable to implement closed-loop iterations, resulting in poor system stability and reliability.

[0004] The widespread adoption of integrated wall-mounted laundry toilets has led to higher demands on their integrated multi-water source collaborative control systems. The wall-mounted structure complicates piping layouts and places stricter demands on pressure and flow distribution. Traditional control systems struggle to meet the demands for efficient operation within this complex structure. Therefore, there is an urgent need for a distributed multi-water source collaborative control system that can accurately monitor water quality from multiple sources, dynamically allocate flow, and intelligently generate cleaning strategies. This system can address these issues with traditional systems, improve water resource utilization efficiency, and enhance equipment performance. Summary of the Invention

[0005] The object of the present invention is to provide a distributed multi-water source coordinated control system based on an integrated wall-mounted laundry toilet to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a distributed multi-water source coordinated control system based on an integrated wall-mounted laundry toilet, the system comprising:

[0007] Multi-source water quality monitoring module, flow dynamic allocation module and cleaning strategy generation module;

[0008] The multi-source water quality monitoring module includes a water quality parameter collection node and a turbidity fusion calculation network; the flow dynamic distribution module includes a pipeline topology modeling unit and a pressure gradient optimization unit; the cleaning strategy generation module includes a water-saving optimization node and a flushing logic engine; the water quality parameter collection node is used to obtain the hardness characteristic value and suspended matter concentration index of the incoming water quality in real time; according to the water quality change rate and the pipeline pressure distribution, a multi-layer water quality assessment topology is constructed through the turbidity fusion calculation network; the turbidity fusion calculation network is composed of N parallel computing subnets based on turbidity thresholds; based on the real-time turbidity response characteristics of the N computing subnets, a core flushing benchmark framework is generated through the water-saving optimization node, and combined with the pressure constraint signal output by the pressure gradient optimization unit, the water source distribution path is dynamically prioritized through the flushing logic engine.

[0009] Preferably, the system further comprises a pipeline deposition correction module and a water source switching optimization module; the pipeline deposition correction module comprises a flow rate fluctuation suppression unit and an impurity accumulation compensation unit;

[0010] According to the generated core flushing benchmark framework and dynamic priority sorting results, the water source distribution range is corrected for sedimentation rate through the flow rate fluctuation suppression unit, and the pressure parameters of the flow dynamic distribution module are dynamically balanced through the impurity accumulation compensation unit; at the same time, the pipeline health assessment network constructed by the water source switching optimization module is used to perform real-time detection of the distribution path deviation rate, and the detection results are fed back to the water-saving optimization node and the flushing logic engine to perform closed-loop iteration of the flushing framework.

[0011] Preferably, the flow rate fluctuation suppression unit is composed of pressure transmission channels corresponding to parallel computing subnets in the turbidity fusion computing network; the steps of constructing the multi-layer water quality assessment topology include:

[0012] Capture the hardness characteristic value and suspended solids concentration fluctuation signal, input it into the turbidity projection model configured in each level of computing subnet, generate the water quality correlation compensation coefficient and store it in the corresponding computing subnet;

[0013] Based on the correlation compensation coefficients stored in computing subnets at all levels and combined with the instantaneous change rate of pipeline pressure, the water source allocation weights are allocated to N computing subnets through the pressure importance evaluation algorithm to form a dynamic pressure matching topology.

[0014] Preferably, the calculation subnet construction conditions of the water quality parameter acquisition node configuration include: the hardness characteristic value is within the preset water quality range, the turbidity recognition rate corresponding to the calculation subnet is greater than the threshold, and the pressure distribution density is less than the allowable deviation range;

[0015] The pipeline topology modeling unit is also configured with a multi-water source switching link; the multi-water source switching link includes a steady-state distribution sub-link and an abnormal blocking sub-link; the steady-state distribution sub-link is generated based on the matching degree between the pressure distribution signal of the flow dynamic distribution module and the core flushing reference framework; the abnormal blocking sub-link is constructed based on the correlation between the instantaneous blockage risk of the water source distribution path and the abnormal water quality events.

[0016] Preferably, the water-saving optimization node is configured with a cross-water source fusion model; the cross-water source fusion model includes a hardness-turbidity mapping table corresponding to a multi-source water quality monitoring module and an abnormal blocking parameter library of a flow dynamic allocation module; the steps of generating the core flushing reference framework include:

[0017] Based on the real-time water quality data and priority ranking results of the multi-source water quality monitoring module, the theoretical allocation range upper limit of each level of computing subnet in the steady-state allocation sub-link is calculated;

[0018] The upper limit of the theoretical allocation range and transient water quality anomalies are input into the cross-water source fusion model to generate a flushing logic reference map, and the blockage risk of the reference map is corrected through the abnormal blocking sub-link to form a core flushing benchmark framework.

[0019] Preferably, the parameter updating step of the cross-water source fusion model includes:

[0020] When it is detected that the hardness characteristic value exceeds the preset water quality range or the distribution path deviation rate exceeds the threshold, the first abnormal blocking instruction is triggered, that is, the backup water source unit is started and the distribution granularity of the flow dynamic distribution module is adjusted;

[0021] If the allocation range has not returned to the allowed range after the execution of the first abnormal blocking instruction, the second abnormal blocking instruction will be triggered, that is, the multi-water source switching link will be switched to the abnormal blocking sub-link, and the topological weight of the water source allocation path will be redistributed based on the duration of the water quality abnormality event.

[0022] Preferably, the parameter updating step of the cross-water source fusion model further includes:

[0023] When the turbidity recognition rate of the computing subnet in the multi-source water quality monitoring module is lower than the threshold, the third abnormal blocking instruction is triggered, that is, the distribution output channel of the subnet is blocked, and the corresponding water flow is transferred to other computing subnets through the flow rate fluctuation suppression unit;

[0024] During the execution of the third abnormal blocking instruction, if other computing subnets are detected to be overloaded, the fourth abnormal blocking instruction is triggered, that is, the water source switching optimization module is called to downgrade the output strategy of the flushing logic engine and limit the allocation demand of the edge water source.

[0025] Preferably, the degradation processing logic of the water source switching optimization module includes:

[0026] Construct a water source allocation safety level table based on the deviation rate data and pressure gradient optimization indicators output by the pipeline health assessment network;

[0027] When the fourth abnormal blocking instruction is triggered, the allocation path priority is dynamically downgraded according to the security level table, and the allocation frequency of the traffic dynamic allocation module is synchronously adjusted to match the flushing strategy after the downgrade.

[0028] Preferably, the step of constructing the computing subnet further includes:

[0029] Based on the water quality clustering results of hardness characteristic values ​​and the historical suspended matter concentration fluctuation range, a water source gradient adaptation index table is generated;

[0030] According to the differences in correlation compensation coefficients of each computing subnet in the index table, the pressure transmission channels of N computing subnets are topologically weighted using the pressure weighted regression algorithm, and redundant distribution links are established between the computing subnets.

[0031] Preferably, the generation process of the flushing logic reference map includes:

[0032] After inputting the upper limit of the theoretical allocation range and transient water quality anomalies into the cross-water source fusion model, the preset water source allocation database and water quality anomaly interference layer are loaded simultaneously;

[0033] Based on the pipeline relationship strength data output by the pipeline topology modeling unit, the blockage threshold of the water source distribution path in the reference map is corrected, and the water quality sparsity parameter is superimposed on the abnormally blocked sub-link for secondary calibration.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] In terms of water quality monitoring and assessment, the water quality parameter acquisition nodes in the multi-source water quality monitoring module acquire the hardness characteristic values ​​and suspended solids concentration indicators of the incoming water in real time. A multi-layered water quality assessment topology is constructed using a turbidity fusion computing network, enabling accurate real-time monitoring and comprehensive assessment of multi-source water quality. This precise water quality monitoring mechanism enables the system to rationally arrange usage scenarios based on the water quality characteristics of different water sources, using high-quality water sources for cleaning tasks with high water quality requirements and using relatively poor-quality water sources for less demanding scenarios. This ensures the rational use of water resources while ensuring cleaning effectiveness and avoiding waste of high-quality water resources.

[0036] In terms of dynamic flow distribution, the pipeline topology modeling unit and the pressure gradient optimization unit of the dynamic flow distribution module work together to optimize the flow distribution in real time based on dynamic factors such as pipeline pressure distribution and water quality change rate. This dynamic distribution method effectively solves the problem of pipeline pressure imbalance caused by the fixed distribution mode in traditional systems, reduces flow rate fluctuations and impurity accumulation, reduces the risk of pipeline blockage, and improves the operating efficiency and stability of the system. At the same time, through the pressure constraint signal output by the pressure gradient optimization unit, combined with the flushing logic engine of the cleaning strategy generation module, the water source distribution path can be dynamically prioritized, further optimizing the flow distribution and ensuring that the system can operate efficiently under various working conditions.

[0037] In terms of cleaning strategy generation and optimization, the water-saving optimization node and flushing logic engine of the cleaning strategy generation module can intelligently generate a core flushing benchmark framework based on water quality monitoring data and flow distribution, and dynamically adjust the flushing strategy. This intelligently generated cleaning strategy can not only ensure good cleaning results, but also save water resources to the greatest extent. In addition, the system is also equipped with a pipeline deposition correction module and a water source switching optimization module, which can correct pipeline deposition problems in real time and optimize water source switching. The water source distribution range and pressure parameters are corrected and balanced through the flow rate fluctuation suppression unit and the impurity accumulation compensation unit, and the distribution path deviation rate is detected and fed back in real time through the pipeline health assessment network constructed by the water source switching optimization module, thus realizing closed-loop iterative optimization of the system, improving the reliability and stability of the system, and extending the service life of the equipment.

[0038] The cross-source fusion model enables the system to comprehensively consider multi-source water quality data and flow distribution anomalies, generating a more scientific and reasonable flushing logic reference map. Blockage risk correction is performed through the abnormal blocking sub-link, further improving the system's ability to cope with water quality anomalies and pipe blockages. When the system encounters abnormalities such as hardness characteristic values ​​exceeding a preset range, distribution path deviation rates exceeding a threshold, or turbidity recognition rates falling below a threshold, the corresponding abnormal blocking instructions are triggered promptly. By activating backup water source units, switching water distribution paths, and blocking the output channels of the faulty subnet, the system can quickly resume normal operation, improving its fault tolerance and reliability.

[0039] The degradation processing logic of the water source switching optimization module dynamically downgrades and adjusts the priority of distribution paths based on data from the pipeline health assessment network and pressure gradient optimization indicators. This ensures basic system functionality in the event of overloads, while also preventing system failures caused by overloads, further improving system stability and reliability. The water source gradient adaptation index table generated during the computational subnet construction step and the establishment of redundant distribution links enhance the system's adaptability to sources of varying water quality and enhance system redundancy, ensuring continued operation even when partial subnet failures occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a working principle diagram of the distributed multi-water source coordinated control system based on the integrated wall-mounted laundry toilet according to the present invention;

[0041] Figure 2 Flowchart for pipeline sedimentation correction and water source switching optimization;

[0042] Figure 3 Logic diagram for pipeline deposition correction module control;

[0043] Figure 4 Schematic diagram of the water source switching optimization mechanism. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] See also Figures 1-4 The present invention relates to a distributed multi-water source collaborative control system based on an integrated wall-mounted laundry toilet, which specifically includes the following steps:

[0046] The system includes a multi-source water quality monitoring module, a flow dynamic allocation module, and a cleaning strategy generation module. The multi-source water quality monitoring module consists of water quality parameter acquisition nodes and a turbidity fusion calculation network; the flow dynamic allocation module includes a pipeline topology modeling unit and a pressure gradient optimization unit; and the cleaning strategy generation module includes a water-saving optimization node and a flushing logic engine.

[0047] The water quality parameter collection node acquires the influent water hardness characteristic values ​​and suspended solids concentration indicators in real time. Based on the water quality change rate and pipeline pressure distribution, a multi-layer water quality assessment topology is constructed using a turbidity fusion computing network. This turbidity fusion computing network consists of N parallel computing subnetworks based on turbidity thresholds. Based on the real-time turbidity response characteristics of these N computing subnetworks, a core flushing benchmark framework is generated through the water-saving optimization node. Combined with the pressure constraint signal output by the pressure gradient optimization unit, the flushing logic engine dynamically prioritizes water source allocation paths.

[0048] Example 1: The system also includes a pipeline deposition correction module and a water source switching optimization module. The pipeline deposition correction module includes a flow rate fluctuation suppression unit and an impurity accumulation compensation unit. During system operation, after the core flushing benchmark framework is generated based on the multi-source water quality monitoring module, the dynamic flow distribution module, and the cleaning strategy generation module, and the flushing logic engine completes the dynamic prioritization of water source allocation paths, the pipeline deposition correction module is required to address pipeline deposition issues during the allocation process.

[0049] The flow rate fluctuation suppression unit is composed of the pressure transmission channels corresponding to each of the N parallel computing subnets in the turbidity fusion computing network. These pressure transmission channels correspond one-to-one to the computing subnets, forming a specific physical connection and data transmission path. Once the core flushing reference framework and dynamic priority sorting results are determined, the flow rate fluctuation suppression unit will use this information to correct the sedimentation rate of the water source distribution range in the pipeline. The principle of its correction is to monitor the rate of change of water quality and the pressure distribution in the pipeline, and utilize the pressure transmission channel characteristics of the computing subnet in the turbidity fusion computing network to suppress fluctuations in water flow velocity, thereby reducing changes in the impurity deposition rate in the pipeline caused by unstable flow rate, and avoiding excessive deposition that affects the normal operation of the system.

[0050] At the same time, the impurity accumulation compensation unit dynamically balances the pressure parameters of the dynamic flow distribution module. During the water distribution process, pressure differences at different locations within the pipeline, coupled with changes in water quality parameters, can easily lead to impurity accumulation in certain areas. The impurity accumulation compensation unit collects pressure parameters in real time and adjusts them based on the core flushing benchmark framework and priority sorting results to achieve a more uniform pressure distribution within the pipeline, thereby compensating for impurity accumulation caused by uneven pressure and maintaining smooth water flow within the pipeline.

[0051] In addition, the water source switching optimization module builds a pipeline health assessment network, which is used to perform real-time detection of the deviation rate of the distribution path. The pipeline health assessment network collects water flow data, pressure data, and water quality data in the distribution path through monitoring points set up at different locations in the pipeline, and then calculates the deviation rate of the distribution path through a specific algorithm. When it is detected that the deviation rate exceeds the preset threshold, the pipeline health assessment network will feed back the detection results to the water-saving optimization node and the flushing logic engine. After receiving the feedback information, the water-saving optimization node and the flushing logic engine will perform a closed-loop iteration on the generated flushing framework, that is, readjust the core flushing benchmark framework and the dynamic priority sorting of the water source distribution path to adapt to the actual conditions of the pipeline and ensure the stable operation of the system.

[0052] When building a multi-layer water quality assessment topology, the first step is to capture the fluctuation signals of the hardness characteristic values ​​and suspended solids concentration of the incoming water. These fluctuation signals reflect the real-time changes in water quality and are an important basis for water quality assessment. These captured fluctuation signals are then input into the turbidity projection model configured in each computing subnet. The turbidity projection model is a mathematically based calculation tool that analyzes and processes the input water quality signals to generate water quality correlation compensation coefficients. These compensation coefficients are used to correct for deviations in the water quality data processed by different computing subnets, ensuring the accuracy of water quality assessments. The generated water quality correlation compensation coefficients are stored in the corresponding computing subnet for subsequent use.

[0053] Based on the correlation compensation coefficients stored in each computational subnetwork and the instantaneous rate of change of pipeline pressure, a pressure importance assessment algorithm assigns water source allocation weights to N computational subnetworks. This algorithm comprehensively considers the impact of instantaneous changes in pipeline pressure on water quality assessment and water source allocation. Based on the correlation compensation coefficients and the pressure change rate, it calculates the importance weight of each computational subnetwork in water source allocation. These weights are then assigned to each computational subnetwork, forming a dynamic pressure matching topology. This dynamic pressure matching topology automatically adjusts the water source allocation weights of each computational subnetwork based on real-time changes in pipeline pressure. This allows the water quality assessment topology to better adapt to pressure fluctuations within the pipeline, improving the system's ability to monitor and assess water quality and providing more accurate data support for subsequent water source allocation and cleansing strategy generation.

[0054] Example 2: The computing subnet configured by the water quality parameter acquisition node must meet specific construction conditions, that is, the hardness characteristic value is within the preset water quality range, the turbidity recognition rate corresponding to the computing subnet is greater than the threshold, and the pressure distribution density is less than the allowable deviation range. The preset water quality range is pre-set according to the system design requirements and actual application scenarios, and is used to define the water quality hardness range suitable for the normal operation of the computing subnet. The turbidity recognition rate threshold is to ensure that the computing subnet can accurately identify changes in water quality turbidity. Only when the recognition rate is greater than the threshold can the water quality data output by the computing subnet be guaranteed to be reliable. The allowable deviation range of the pressure distribution density is to avoid the pressure distribution in the pipeline being too complex or abnormal, which affects the normal operation of the computing subnet. Only when these three conditions are met at the same time can the computing subnet be correctly constructed and put into use, thereby providing accurate data support for subsequent water quality monitoring and evaluation.

[0055] The pipeline topology modeling unit is also equipped with a multi-water source switching link, which includes a steady-state allocation sub-link and an abnormal blocking sub-link. The generation of the steady-state allocation sub-link is based on the matching degree between the pressure distribution signal of the flow dynamic allocation module and the core flushing reference framework. When the system is operating normally, the flow dynamic allocation module will monitor the pressure distribution in the pipeline in real time and generate a corresponding pressure distribution signal. At the same time, the core flushing reference framework, as the basis of the system's flushing strategy, stipulates the basic framework and requirements for water source allocation. When the matching degree between the pressure distribution signal and the core flushing reference framework reaches a certain standard, the steady-state allocation sub-link will be generated based on this information to achieve stable water source distribution under normal circumstances.

[0056] The construction of the abnormal blocking sub-link is based on the correlation between the instantaneous blockage risk of the water distribution path and abnormal water quality events. During system operation, the system analyzes and determines the presence of instantaneous blockage risks and abnormal water quality events by real-time monitoring of water flow, pressure, and water quality parameters in the water distribution path. If a correlation between instantaneous blockage risk and abnormal water quality events is found, an abnormal blocking sub-link is constructed based on this correlation. This sub-link is used to promptly block or adjust the water distribution path when an abnormal situation occurs, preventing system failures or disruptions to normal operation.

[0057] The water-saving optimization node is configured with a cross-source fusion model, which includes a hardness-turbidity mapping table corresponding to the multi-source water quality monitoring module and an abnormal blocking parameter library of the flow dynamic allocation module. The hardness-turbidity mapping table is established through analysis and processing of a large amount of historical water quality data, which records the corresponding relationship between different hardness characteristic values and turbidity, and is used to quickly query the corresponding turbidity according to the real-time monitored hardness characteristic value during system operation, providing reference for the calculation of the cross-source fusion model. The abnormal blocking parameter library stores the blocking parameters and strategies of the flow dynamic allocation module under various abnormal conditions, providing data support for the cross-source fusion model when handling abnormal conditions.

[0058] The generation of the core flushing reference framework needs to go through the following steps: first, according to the real-time water quality data obtained by the multi-source water quality monitoring module, including hardness characteristic value, suspended matter concentration index, etc., and the dynamic priority sorting result of the water source distribution path by the flushing logic engine, the theoretical distribution range upper limit of each level of calculation subnetwork in the steady-state distribution sublink is calculated. The calculation of the theoretical distribution range upper limit needs to consider the changes of water quality data, priority sorting results and the characteristics of the steady-state distribution sublink, to ensure that the calculated range can meet the flushing needs of the system, while avoiding over-distribution or under-distribution of water sources.

[0059] Then, the calculated theoretical distribution range upper limit and transient water quality abnormality are input into the cross-source fusion model. Transient water quality abnormalities may be caused by sudden changes in water quality, accumulation of impurities in the pipeline, etc., and need to be handled in time. After receiving these input information, the cross-source fusion model will generate a flushing logic reference map in combination with the hardness-turbidity mapping table and the abnormal blocking parameter library. The flushing logic reference map is a direct display of water source distribution and flushing strategy, which contains the distribution range, flushing priority, etc. of each calculation subnetwork.

[0060] Finally, the flushing logic reference map is modified for the risk of blockage by the abnormal blocking sublink. The abnormal blocking sublink will analyze and judge the water source distribution path that may have a risk of blockage in the reference map according to the previously constructed association between transient blockage risk and water quality abnormality events, and make appropriate modifications, such as adjusting the distribution range, changing the flushing priority, etc., to form the final core flushing reference framework. This framework can ensure the flushing effect while minimizing the risk of pipeline blockage, ensuring the stable operation of the system.

[0061] Example 3: The parameter update mechanism of the cross-water source fusion model is a key link to ensure the adaptability of the system, which includes the triggering logic and processing flow of multi-level abnormal blocking instructions. When the system detects that the hardness characteristic value exceeds the preset water quality range, it means that the current inlet water quality may have deviated from the baseline range of the system's optimized operation. For example, a sudden increase in hardness may increase the risk of scaling in the pipe, or a decrease in hardness may affect the flushing effect. At the same time, if the distribution path deviation rate exceeds the threshold, it indicates that the actual water source distribution path deviates significantly from the preset framework, which may be caused by factors such as pipeline deposition and pressure fluctuations. At this time, the system will trigger the first abnormal blocking instruction, that is, start the backup water source unit and adjust the allocation granularity of the flow dynamic allocation module. The startup of the backup water source unit can switch to a water source with more stable water quality, and the adjustment of the allocation granularity is to refine or coarsen the level of flow distribution so that the system can adapt to water quality mutations more quickly and rebalance the water source input of each computing subnet.

[0062] If the allocation range has not returned to the allowed range after the execution of the first abnormal blocking instruction, it means that the impact of water quality anomalies or path deviations is relatively stubborn, and conventional adjustment measures are not enough to solve the problem. At this time, the system will trigger the second abnormal blocking instruction and switch to the abnormal blocking sub-link through the multi-water source switching link. The abnormal blocking sub-link is constructed based on the correlation between the instantaneous blockage risk of the water source distribution path and the water quality anomaly events in historical data. Its path design focuses more on avoiding high-risk areas. After switching to this sub-link, the system will reallocate the topological weight of the water source distribution path based on the duration of the water quality anomaly event. For example, if the water quality anomaly lasts for a long time, the weight of the affected path will be reduced, and the water source allocation ratio of the backup path will be increased to dynamically reconstruct the topological structure of the water source distribution to ensure that the water supply to the key flushing area is not seriously affected.

[0063] In addition to abnormalities in hardness and distribution paths, the turbidity recognition rate of the computing subnet is also an important triggering condition for parameter updates. When the turbidity recognition rate of a computing subnet in the multi-source water quality monitoring module falls below the threshold, this indicates that the subnet may be unable to accurately feedback water quality turbidity data due to sensor contamination, hardware failure, or sudden changes in water quality. Continuing to use this data may lead to deviations in the flushing strategy. At this point, the system triggers the third abnormal blocking instruction, blocking the distribution output channel of this subnet to prevent erroneous data from participating in water source distribution calculations. At the same time, the flow rate fluctuation suppression unit transfers the corresponding water flow to other computing subnets. The flow rate fluctuation suppression unit is composed of the pressure transmission channels corresponding to the parallel computing subnets in the turbidity fusion computing network. By adjusting the pressure transmission path, it directs the water flow of the blocked subnet to other normally functioning subnets, maintaining the balance of the overall water flow and avoiding sudden increases or decreases in local water flow caused by the blocking of a single subnet.

[0064] During the execution of the third abnormal blocking instruction, if the system detects that other computing subnets are overloaded, for example, the pressure of a subnet exceeds the safety threshold due to the additional water flow, or the amount of data processed exceeds its computing capacity, the fourth abnormal blocking instruction will be triggered. At this time, the system will call the water source switching optimization module to downgrade the output strategy of the flushing logic engine. Degradation does not mean completely stopping the function, but reducing the complexity and resource consumption of the strategy, such as reducing the flushing frequency of non-critical areas, merging the priority levels of some allocation paths, etc. At the same time, the system will limit the allocation demand of marginal water sources. Marginal water sources usually refer to water sources with poor water quality stability or low frequency of use. Limiting their allocation can give priority to the supply of main water sources to the core flushing areas, avoiding the decline in the flushing efficiency of the overall system due to overload.

[0065] During the entire parameter update process, the system forms a multi-level abnormal response mechanism by real-time monitoring of water quality parameters, distribution path status and the operation of each subnet. The triggering of the first to fourth abnormal blocking instructions reflects the system's processing logic from initial adjustment to deep reconstruction. For example, the first and second instructions target macro-anomalies of water quality and distribution paths, and solve the problem through water source switching and topology reconstruction; the third and fourth instructions focus on subnet-level failures and overloads, and ensure the robustness of the system through channel blocking, traffic transfer and policy degradation. This hierarchical processing method enables the system to take targeted measures when faced with different types and degrees of abnormalities, which not only avoids the waste of resources caused by excessive response, but also ensures the continuous operation of key functions.

[0066] In addition, the parameter update of the cross-water source fusion model is not an isolated operation, but forms a closed-loop feedback with other modules of the system. For example, after the abnormal blocking instruction is triggered, the water source switching optimization module will feed back the processing results to the water-saving optimization node and the flushing logic engine, prompting the recalculation of the core flushing benchmark framework and the water source allocation priority; the pipeline deposition correction module will also adjust the flow rate fluctuation suppression and impurity accumulation compensation parameters according to the pressure distribution after the water flow is transferred, ensuring that the deposition risk in the pipeline is always within a controllable range. This multi-module collaborative parameter update mechanism enables the system to continuously optimize the water source allocation strategy and maintain an efficient and stable working state under complex water quality environments and dynamic operating conditions.

[0067] Example 4: The degradation processing logic of the water source switching optimization module plays a key role when the system responds to overload or abnormalities. Its core lies in the dynamic adjustment of the allocation strategy by constructing a safety level table. Specifically, the system will quantitatively evaluate the safety of the water source distribution path based on the deviation rate data and pressure gradient optimization index output by the pipeline health assessment network. For example, when the deviation rate of a certain section of pipeline continues to be higher than the preset threshold and the pressure gradient shows abnormal fluctuations, the ranking of the path in the safety level table will be reduced accordingly. The construction of the safety level table is not fixed, but is continuously updated based on real-time monitoring data, covering the combination of different pipeline sections, water source types and distribution paths. Each entry corresponds to a specific safety parameter range and processing priority.

[0068] When the fourth abnormal blocking instruction is triggered, the system will dynamically downgrade the priority of the allocation path according to the safety level table. For example, suppose a certain allocation path is responsible for the main flushing water source of the laundry toilet, but the deviation rate of the pipeline in which it is located exceeds the threshold and is accompanied by a sudden increase in pressure. The safety level table will mark it as "high risk". At this time, the system will lower the priority of the path and temporarily reduce its water source allocation. Instead, it will give priority to paths with higher safety levels, such as the basic water supply for toilet flushing. At the same time, the system will synchronously adjust the allocation frequency of the flow dynamic allocation module to match the downgraded flushing strategy. For example, the original allocation frequency is 5 flow adjustments per minute, which may be adjusted to 3 times per minute after downgrading. By reducing the adjustment frequency, the system resource consumption is reduced to avoid the increase in pressure fluctuations caused by high-frequency adjustments.

[0069] The construction steps of the computing subnet also include generating a water source gradient adaptation index table based on the water quality clustering results of the hardness characteristic value and the historical suspended solids concentration fluctuation range. Water quality clustering is to divide the incoming water quality into different categories, such as low hardness, medium hardness, and high hardness intervals, by analyzing historical hardness data. Each interval corresponds to a different water source treatment strategy. The historical suspended solids concentration fluctuation range records the change in suspended solids concentration in different time periods, providing data support for the index table. The water source gradient adaptation index table is essentially a mapping database that maps different water quality clustering results and suspended solids concentration ranges to specific computing subnets, and marks the adapted water source type and processing capacity of each subnet. For example, when the water quality clustering result is high hardness and the suspended solids concentration is high, the index table will point to a specific computing subnet that is pre-configured with processing parameters for high hardness and high turbidity water quality.

[0070] Based on the differences in the correlation compensation coefficients of each computing subnet in the index table, the system will use a pressure-weighted regression algorithm to assign topological weights to the pressure transmission channels of N computing subnets. The correlation compensation coefficient reflects the accuracy and reliability of each subnet in treating a specific water quality. The higher the coefficient, the stronger the subnet's ability to treat the corresponding water quality. The pressure-weighted regression algorithm uses these coefficients as weighting factors and, combined with real-time pipeline pressure data, calculates the topological weight that should be assigned to the pressure transmission channels of each subnet. For example, a computing subnet with a higher correlation compensation coefficient will receive a higher weight in the pressure-weighted regression, meaning that the pressure transmission channels of this subnet have a higher priority in water source allocation and can obtain water input first to ensure treatment results.

[0071] In addition, the system will also establish redundant distribution links between computing subnets to enhance system reliability. The establishment of redundant distribution links is based on the topological weight distribution results. In addition to the main distribution link, 1-2 backup links are set for each computing subnet. For example, when the main link causes water distribution to be interrupted due to abnormal pressure or blockage, the redundant link will be automatically activated and the water source will be switched to the backup path to ensure the continuous operation of the computing subnet. The parameter settings of these redundant links (such as pipe diameter and pressure threshold) will be adjusted according to the topological weight of the corresponding subnet. Subnets with high topological weights are usually equipped with thicker pipe diameters or lower switching thresholds to ensure rapid response and efficient transmission of the backup links.

[0072] In the actual operation scenario, if the hardness of the water source connected to a certain integrated wall-mounted laundry toilet suddenly increases and exceeds the preset range, the system's water quality parameter collection node will capture this change in real time and input the hardness characteristic value into the water quality clustering module. The clustering result is displayed as "high hardness water quality", which triggers a query of the water source gradient adaptation index table and points to the corresponding high hardness processing subnet. At this time, the correlation compensation coefficient of the subnet is high, and it obtains a higher topological weight in the pressure-weighted regression algorithm, and its pressure transmission channel is given priority allocation. At the same time, the system will monitor the operating status of the subnet. If the processing load increases due to high hardness water quality and other subnets show signs of overload, the water source switching optimization module will start the degradation processing logic, reduce the priority of non-critical paths according to the safety level table, and adjust the allocation frequency to ensure the stable operation of the high hardness processing subnet.

[0073] If, during operation, the turbidity recognition rate of a computing subnet falls below the threshold, the third abnormal blocking instruction is triggered, the distribution output channel of this subnet is blocked, and the water flow is transferred to other subnets via redundant distribution links. At this time, the system recalculates the topological weights of the remaining subnets and adjusts the distribution ratio of the pressure transmission channel. If the transferred water flow causes other subnets to overload, the fourth abnormal blocking instruction is triggered. The water source switching optimization module will downgrade the distribution path according to the safety level table. For example, it will temporarily reduce the water source allocation for the laundry function to prioritize the basic function of the toilet flushing. At the same time, it will reduce the adjustment frequency of the flow dynamic distribution module to prevent the system from paralyzing due to overload.

[0074] Example 5: The generation process of the flushing logic reference map is the core link for the system to achieve accurate water source allocation, which requires a deep integration of theoretical calculations and real-time data. Take the scenario where a certain integrated wall-mounted laundry toilet is connected to different water sources (such as municipal tap water and recycled water) as an example. When the system obtains the real-time data of the multi-source water quality monitoring module (such as the hardness characteristic value is 200mg / L and the suspended matter concentration is 50mg / L) and completes the dynamic priority sorting, it first calculates the upper limit of the theoretical allocation range of each level of the computing subnet in the steady-state allocation sub-link. For example, for the computing subnet corresponding to the laundry function, the theoretical allocation upper limit may be set to 40% of the total flow to balance the washing effect and water consumption.

[0075] When the upper limit of the theoretical allocation range and transient water quality anomalies (such as sudden increases in turbidity) are input into the cross-water source fusion model, the system will simultaneously load the preset water source allocation database and the water quality anomaly interference layer. The water source allocation database stores the historical allocation ratios of each water source under different water quality conditions. For example, when the hardness is higher than 150mg / L, the recycled water allocation ratio does not exceed 30%. The water quality anomaly interference layer marks the areas where pipes are prone to blockage and sensitive points of water quality fluctuations. For example, the bends in toilet flush pipes are high-risk areas for blockage. At this time, the cross-water source fusion model will calculate the allocation correction coefficients for each subnet under the current water quality based on the hardness-turbidity mapping table (for example, 200mg / L hardness corresponds to a turbidity warning threshold of 80NTU), and generate a preliminary flushing logic reference map, showing that the laundry subnet is allocated 35% of the flow, the punching subnet is allocated 50% of the flow, and the backup subnet is allocated 15% of the flow.

[0076] Based on the pipeline relationship strength data output by the pipeline topology modeling unit, the system will correct the blockage threshold of the water source distribution path in the reference map. For example, the relationship strength of the connection section between the laundry pipeline and the punching sub-pipeline is detected to be 0.8 (the full score is 1), which is a high correlation. At this time, the blockage threshold of the connection section will be lowered, that is, when the pressure fluctuation exceeds 0.2MPa, it is judged as a potential blockage risk, rather than the default 0.3MPa. Subsequently, the water quality sparsity parameter is superimposed on the abnormal blocking sub-link for secondary calibration. The water quality sparsity parameter reflects the uniformity of the impurity distribution in the water. If it is detected that the suspended matter concentration does not exceed the standard but the distribution is extremely uneven (such as a sudden increase in local concentration), the distribution path will be further adjusted, such as reducing the flow through the pipe bend and increasing the distribution ratio of the straight pipe to reduce the probability of blockage.

[0077] Assume that in a certain scenario, the hardness of municipal tap water suddenly rises to 250mg / L, exceeding the preset range (100-200mg / L). The theoretical allocation upper limit generated by the system shows that the laundry subnet needs to reduce the flow by 5% to avoid the risk of scaling. At the same time, the transient water quality anomaly is manifested as the turbidity rising from 30NTU to 70NTU within 10 minutes, approaching the warning threshold. After inputting the cross-water source fusion model, the model calls the historical allocation plan for the "high hardness + high turbidity" scenario in the water source allocation database, adjusts the theoretical allocation upper limit of the laundry subnet from 40% to 30%, and marks in the reference map that the allocation path of the subnet must avoid sections with pipe inner diameters less than 15mm.

[0078] The pipeline topology modeling unit outputs pipeline relationship strength data showing that the relationship strength between the laundry subnet and the punching subnet shared pipeline segment is 0.6, which is a medium correlation. Therefore, the system revised the blockage threshold for this pipeline segment from 0.3MPa to 0.25MPa. At this time, the abnormal blocking sub-link detected that the water quality rarefaction parameter showed that suspended matter was locally concentrated at the inlet of the laundry pipeline, with a concentration of 120mg / L, far exceeding the average level. Therefore, during the second calibration, the system switched the laundry subnet's distribution path to the backup pipeline and further reduced the flow distribution ratio to 25%. At the same time, the distribution ratio of the punching subnet was increased to 55%, allowing a higher flow rate to flush the shared pipeline segment and reduce the risk of blockage.

[0079] In another scenario, when recycled water is connected to the system and the suspended solids concentration is 80 mg / L (near the threshold), the theoretical allocation limit is calculated to be no more than 40% of recycled water used in the perforated subnet. However, transient water quality anomalies indicate that the turbidity of the reclaimed water pipeline continues to increase after a certain use, indicating possible deposits on the pipe walls. After inputting the cross-source fusion model, the model loads the water quality anomaly interference layer and identifies a section of the riser in the perforated subnet as belonging to an area with historically high congestion. Therefore, a warning indicator is generated in the reference map, indicating that the flushing frequency of this route should be increased. Pipeline relationship strength data indicates that this riser is close to the laundry pipeline, with a relationship strength of 0.4. Based on this, the system adjusts the blockage threshold for this riser from 0.3 MPa to 0.28 MPa. When the water quality sparsity parameter is superimposed on the abnormal blocking sublink, it is found that the probability of suspended solids depositing at the bottom of the riser is high. Therefore, after secondary calibration, the allocation path of the perforated subnet is adjusted so that it flows through this riser section first during each flush, utilizing the impact of the water flow to reduce deposits.

[0080] Throughout the generation of the flushing logic reference map, the system develops a multi-level water allocation strategy optimization mechanism through theoretical allocation range calculation, cross-source fusion model processing, pipeline relationship strength correction, and secondary calibration of abnormally blocked sub-links. As demonstrated in specific examples, regardless of sudden changes in water quality or differences in pipeline characteristics, the system dynamically adjusts allocation paths and flow ratios through the synergistic effects of various modules, ensuring flushing effectiveness while minimizing blockage risks.

[0081] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0082] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A distributed multi-water source collaborative control system based on an integrated wall-mounted laundry toilet, characterized in that: include: Multi-source water quality monitoring module, flow dynamic allocation module and cleaning strategy generation module; The multi-source water quality monitoring module includes a water quality parameter collection node and a turbidity fusion calculation network; the flow dynamic distribution module includes a pipeline topology modeling unit and a pressure gradient optimization unit; the cleaning strategy generation module includes a water-saving optimization node and a flushing logic engine; the water quality parameter collection node is used to obtain the hardness characteristic value and suspended matter concentration index of the incoming water quality in real time; according to the water quality change rate and the pipeline pressure distribution, a multi-layer water quality assessment topology is constructed through the turbidity fusion calculation network; the turbidity fusion calculation network is composed of N parallel computing subnets based on turbidity thresholds; based on the real-time turbidity response characteristics of the N computing subnets, a core flushing benchmark framework is generated through the water-saving optimization node, and combined with the pressure constraint signal output by the pressure gradient optimization unit, the water source distribution path is dynamically prioritized through the flushing logic engine.

2. The distributed multi-water source coordinated control system based on the integrated wall-mounted laundry toilet according to claim 1, characterized in that: The system also includes a pipeline deposition correction module and a water source switching optimization module; the pipeline deposition correction module includes a flow rate fluctuation suppression unit and an impurity accumulation compensation unit; According to the generated core flushing benchmark framework and dynamic priority sorting results, the water source distribution range is corrected for sedimentation rate through the flow rate fluctuation suppression unit, and the pressure parameters of the flow dynamic distribution module are dynamically balanced through the impurity accumulation compensation unit; at the same time, the pipeline health assessment network constructed by the water source switching optimization module is used to perform real-time detection of the distribution path deviation rate, and the detection results are fed back to the water-saving optimization node and the flushing logic engine to perform closed-loop iteration of the flushing framework.

3. The distributed multi-water source coordinated control system based on the integrated wall-mounted laundry toilet according to claim 2, characterized in that: The flow rate fluctuation suppression unit is composed of pressure transmission channels corresponding to parallel computing subnets in the turbidity fusion computing network; the steps of constructing the multi-layer water quality assessment topology include: Capture the hardness characteristic value and suspended solids concentration fluctuation signal, input it into the turbidity projection model configured in each level of computing subnet, generate the water quality correlation compensation coefficient and store it in the corresponding computing subnet; Based on the correlation compensation coefficients stored in computing subnets at all levels and combined with the instantaneous change rate of pipeline pressure, the water source allocation weights are allocated to N computing subnets through the pressure importance evaluation algorithm to form a dynamic pressure matching topology.

4. The distributed multi-water source coordinated control system based on the integrated wall-mounted laundry toilet according to claim 3, characterized in that: The calculation subnet construction conditions of the water quality parameter acquisition node configuration include: the hardness characteristic value is within the preset water quality range, the turbidity recognition rate corresponding to the calculation subnet is greater than the threshold, and the pressure distribution density is less than the allowable deviation range; The pipeline topology modeling unit is also configured with a multi-water source switching link; the multi-water source switching link includes a steady-state distribution sub-link and an abnormal blocking sub-link; the steady-state distribution sub-link is generated based on the matching degree between the pressure distribution signal of the flow dynamic distribution module and the core flushing reference framework; the abnormal blocking sub-link is constructed based on the correlation between the instantaneous blockage risk of the water source distribution path and the abnormal water quality events.

5. The distributed multi-water source coordinated control system based on the integrated wall-mounted laundry toilet according to claim 4, characterized in that: The water-saving optimization node is configured with a cross-water source fusion model; the cross-water source fusion model includes a hardness-turbidity mapping table corresponding to the multi-source water quality monitoring module and an abnormal blocking parameter library of the flow dynamic allocation module; The steps of generating the core flushing reference frame include: Based on the real-time water quality data and priority ranking results of the multi-source water quality monitoring module, the theoretical allocation range upper limit of each level of computing subnet in the steady-state allocation sub-link is calculated; The upper limit of the theoretical allocation range and transient water quality anomalies are input into the cross-water source fusion model to generate a flushing logic reference map, and the blockage risk of the reference map is corrected through the abnormal blocking sub-link to form a core flushing benchmark framework.

6. The distributed multi-water source coordinated control system based on the integrated wall-mounted laundry toilet according to claim 5, characterized in that: The parameter updating step of the cross-water source fusion model includes: When it is detected that the hardness characteristic value exceeds the preset water quality range or the distribution path deviation rate exceeds the threshold, the first abnormal blocking instruction is triggered, that is, the backup water source unit is started and the distribution granularity of the flow dynamic distribution module is adjusted; If the allocation range has not returned to the allowed range after the execution of the first abnormal blocking instruction, the second abnormal blocking instruction will be triggered, that is, the multi-water source switching link will be switched to the abnormal blocking sub-link, and the topological weight of the water source allocation path will be redistributed based on the duration of the water quality abnormality event.

7. The distributed multi-water source coordinated control system based on an integrated wall-mounted laundry toilet according to claim 6, characterized in that: The parameter updating step of the cross-water source fusion model further includes: When the turbidity recognition rate of the computing subnet in the multi-source water quality monitoring module is lower than the threshold, the third abnormal blocking instruction is triggered, that is, the distribution output channel of the subnet is blocked, and the corresponding water flow is transferred to other computing subnets through the flow rate fluctuation suppression unit; During the execution of the third abnormal blocking instruction, if other computing subnets are detected to be overloaded, the fourth abnormal blocking instruction is triggered, that is, the water source switching optimization module is called to downgrade the output strategy of the flushing logic engine and limit the allocation demand of the edge water source.

8. The distributed multi-water source coordinated control system based on the integrated wall-mounted laundry toilet according to claim 7, characterized in that: The degradation processing logic of the water source switching optimization module includes: Construct a water source allocation safety level table based on the deviation rate data and pressure gradient optimization indicators output by the pipeline health assessment network; When the fourth abnormal blocking instruction is triggered, the priority of the allocation path is dynamically downgraded according to the security level table, and the allocation frequency of the traffic dynamic allocation module is synchronously adjusted to match the flushing strategy after the downgrade.

9. The distributed multi-water source coordinated control system based on an integrated wall-mounted laundry toilet according to claim 3, characterized in that: The step of constructing the computing subnet further includes: Based on the water quality clustering results of hardness characteristic values ​​and the historical suspended matter concentration fluctuation range, a water source gradient adaptation index table is generated; According to the differences in correlation compensation coefficients of each computing subnet in the index table, the pressure transmission channels of N computing subnets are topologically weighted using the pressure weighted regression algorithm, and redundant distribution links are established between the computing subnets.

10. The distributed multi-water source coordinated control system based on an integrated wall-mounted laundry toilet according to claim 5, characterized in that: The generation process of the flushing logic reference map includes: After inputting the upper limit of the theoretical allocation range and transient water quality anomalies into the cross-water source fusion model, the preset water source allocation database and water quality anomaly interference layer are loaded simultaneously; Based on the pipeline relationship strength data output by the pipeline topology modeling unit, the blockage threshold of the water source distribution path in the reference map is corrected, and the water quality sparsity parameter is superimposed on the abnormally blocked sub-link for secondary calibration.

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