A smart water data processing method and system based on the Internet of Things

Abstract

The present invention relates to a smart water affairs data processing method and system based on the Internet of Things, and relates to the field of smart water affairs technology, including: receiving user-side demand water inflow, configuring a valve opening set and a duration set of multiple printing and dyeing wastewater source water valve node groups; traversing the water valve node group, and statistically analyzing the deviation modulus of the theoretical water supply and the actual water supply in the preset time zone, and the ratio of the deviation modulus to the theoretical water supply, and setting it as a water valve control fluctuation ratio set; adding the product of the water valve control fluctuation ratio set and the theoretical water supply identification set to obtain the fluctuating water supply; when the fluctuating water supply is less than the fluctuating water supply threshold, uploading the valve opening set, the duration set, the printing and dyeing wastewater treatment area and the demand water inflow to the water affairs server. The present invention solves the technical problem that the prior art lacks a dynamic adjustment and feedback mechanism, and cannot dynamically adjust the valve opening according to the actual water supply, resulting in insufficient water flow control accuracy, and improves the water supply stability and wastewater treatment stability.

Description

Technical Field

[0001] The present invention relates to the field of smart water technology, and in particular to a smart water data processing method and system based on the Internet of Things. Background Art

[0002] Printing and dyeing wastewater is wastewater generated during the dyeing and finishing process of the textile industry. It usually contains a large amount of harmful substances, such as dyes, chemical additives and suspended solids, and is highly polluting, requiring centralized treatment. In order to ensure the effect of wastewater treatment, the amount of wastewater entering the treatment pool must be accurately controlled to ensure that the sewage treatment equipment operates stably within the design parameter range.

[0003] At present, the common method of controlling the water inflow in the process of printing and dyeing wastewater treatment is to adjust the valve opening of multiple printing and dyeing wastewater source channels through a fixed water supply mode or a simple valve switch logic to control the wastewater flow. However, as the channel ages, scaling and corrosion may occur inside the pipeline, causing the mapping relationship between water flow and valve opening to gradually change. This change will cause a deviation between the actual flow and the expected flow, especially when the pipeline is blocked, which will seriously affect the accuracy of water inflow control, increase the instability in the wastewater treatment process, and thus have an adverse effect on the wastewater treatment effect. Summary of the invention

[0004] The present invention aims to solve the technical problem that in the prior art, due to the lack of dynamic adjustment and feedback mechanism, the valve opening cannot be dynamically adjusted according to the actual water supply, resulting in insufficient accuracy of water flow control, which in turn affects the wastewater treatment effect. An intelligent water data processing method and system based on the Internet of Things is provided to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In the first aspect, the present invention provides a smart water data processing method based on the Internet of Things, which is applied to the Internet of Things center, including: receiving the demand water inlet of the printing and dyeing wastewater treatment area uploaded by the user side, configuring the valve opening set and the duration set of the water valve node group of multiple printing and dyeing wastewater sources, wherein the valve opening set and the duration set have a theoretical water supply identification set; traversing the water valve node group, counting the deviation modulus of the theoretical water supply and the actual water supply in a preset time zone, and the ratio of the deviation modulus to the theoretical water supply is set as a water valve control fluctuation ratio set; summing the products of the one-to-one corresponding water valve control fluctuation ratio set and the theoretical water supply identification set to obtain the fluctuating water supply; when the fluctuating water supply is less than the fluctuating water supply threshold, uploading the valve opening set, the duration set, the printing and dyeing wastewater treatment area and the demand water inlet to the water server.

[0007] In a second aspect, the present invention provides a smart water data processing system based on the Internet of Things, the system comprising: a water valve node configuration module, used to receive the demand water inflow of the printing and dyeing wastewater treatment area uploaded by the user side, and configure the valve opening set and the duration set of the water valve node group of multiple printing and dyeing wastewater sources, wherein the valve opening set and the duration set have a theoretical water supply identification set; a control fluctuation calculation module, used to traverse the water valve node group, and count the deviation modulus of the theoretical water supply and the actual water supply in a preset time zone, and the ratio of the deviation modulus to the theoretical water supply, and set it as a water valve control fluctuation ratio set; a fluctuation water supply calculation module, used to sum the products of the one-to-one corresponding water valve control fluctuation ratio set and the theoretical water supply identification set to obtain the fluctuation water supply; a data upload module, used to upload the valve opening set, the duration set, the printing and dyeing wastewater treatment area and the demand water inflow to the water server when the fluctuation water supply is less than the fluctuation water supply threshold.

[0008] The beneficial effects of the present invention are:

[0009] By receiving the demand water inflow data of the printing and dyeing wastewater treatment area uploaded by the user side, a real-time reference basis is provided for the subsequent dynamic adjustment, so that the subsequent steps can accurately regulate the water supply according to the actual demand, avoiding the waste of resources or low processing efficiency caused by the mismatch between supply and demand. By configuring the valve opening set and the duration set of the multi-printing and dyeing wastewater source water valve node group, the water supply of each water valve can be dynamically adjusted to meet the actual needs of the printing and dyeing wastewater treatment area. At the same time, the introduction of the theoretical water supply identification set provides a theoretical basis for the subsequent deviation analysis. Traversing the water valve node group, the deviation modulus of the theoretical water supply and the actual water supply in the preset time zone is counted, and the ratio of it to the theoretical water supply is set as the water valve control fluctuation ratio set, which realizes the quantitative evaluation of water supply fluctuations, identifies the water valves with large fluctuations in water supply, and provides data support for subsequent optimization and adjustment. By multiplying and summing the product of the water valve control fluctuation ratio set and the theoretical water supply identification set, the fluctuating water supply is obtained, and the fluctuation of the entire water supply system is comprehensively evaluated. By calculating the fluctuating water supply, it is possible to determine whether the current water supply status is stable, and decide whether further adjustments or data upload are needed. When the fluctuating water supply is less than the threshold, it means that the current water supply status is relatively stable. At this time, key data such as valve opening set, duration set, printing and dyeing wastewater treatment area and required water inflow are uploaded to the water server to provide support for subsequent analysis and decision-making, which not only realizes the dynamic update of data, but also provides real-time decision-making basis for water management.

[0010] To sum up, the present invention realizes precise regulation of the printing and dyeing wastewater supply system through a demand-driven dynamic water supply control method, combined with the intelligent configuration of valve opening set, duration set and theoretical water supply volume, and significantly improves the water supply stability and wastewater treatment effect in the printing and dyeing wastewater treatment process. It also realizes the intelligence and automation of water supply management by uploading data to the water server, providing reliable guarantee for the efficient operation of printing and dyeing wastewater treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic flow chart of a smart water data processing method based on the Internet of Things provided by the present invention.

[0012] Figure 2 A flow chart of valve opening sets and duration sets of multiple printing and dyeing wastewater source water valve node groups configured in a smart water data processing method based on the Internet of Things provided by the present invention.

[0013] Figure 3 A schematic diagram of the structure of a smart water data processing system based on the Internet of Things provided by the present invention.

[0014] The accompanying drawings are as follows:

[0015] Water valve node configuration module 10, control fluctuation calculation module 20, fluctuation water supply calculation module 30, data upload module 40. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiment b is a 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0019] Embodiment 1:

[0020] like Figure 1 As shown, an embodiment of the present invention provides a method for processing smart water affairs data based on the Internet of Things, the method comprising:

[0021] Step S1: receiving the required water inflow of the printing and dyeing wastewater treatment area uploaded by the user side, and configuring the valve opening set and duration set of multiple printing and dyeing wastewater source water valve node groups, wherein the valve opening set and the duration set have a theoretical water supply volume identification set.

[0022] Specifically, the printing and dyeing wastewater treatment area refers to a specific area in the printing and dyeing enterprise that is specifically used to treat printing and dyeing wastewater. This area includes various wastewater treatment equipment, pipelines and other facilities. Receive the required water inflow information of the printing and dyeing wastewater treatment area uploaded by the user side. The required water inflow is the water inflow that the printing and dyeing wastewater treatment area expects to receive according to its treatment capacity and the requirements of the treatment process. For example, a specific printing and dyeing wastewater treatment process needs to treat 10 cubic meters of printing and dyeing wastewater per hour according to the design to ensure the treatment effect. This 10 cubic meters is the required water inflow. According to the distribution of the water valve node group of multiple printing and dyeing wastewater sources and the characteristics of the pipeline, the valve opening and duration are configured for each water valve according to the required water inflow, forming a valve opening set and a duration set, and according to the valve opening and duration, the valve opening set and the duration set are marked with a theoretical water supply, forming a theoretical water supply mark set. Among them, the water valve node group is a set composed of multiple water valves used to control the flow of printing and dyeing wastewater. The valve opening set is a set of data that describes the degree of opening of each water valve in the water valve node group. For example, the water valve opening can be expressed in percentage. If there are three water valves with openings of 30%, 50% and 40% respectively, this set of data {30%, 50%, 40%} is the valve opening set. The duration set corresponds to each opening in the valve opening set, and is a set of the length of time that the water valve maintains the opening. The theoretical water supply quantity identifier is the identifier of the amount of water that should be supplied by each printing and dyeing wastewater source under ideal conditions, calculated based on the valve opening set and the duration set.

[0023] Step S1 provides an initial control strategy for the inflow of printing and dyeing wastewater. By reasonably configuring the valve opening and duration, a water supply plan is preliminarily planned according to the required water inflow, ensuring the planning and targeting of the water supply.

[0024] Step S2: traverse the water valve node group, count the deviation modulus of the theoretical water supply and the actual water supply in the preset time zone, and set the ratio of the deviation modulus to the theoretical water supply as the water valve control fluctuation ratio set.

[0025] Specifically, the preset time zone is a specific time range set in advance for statistical data related to water supply. For example, it can be set as a working day (8 hours) or a shift (12 hours) as the preset time zone. Traverse the water valve node group and check each water valve one by one. Within the preset time zone, collect the theoretical water supply (according to the calculation in step S1) and the actual water supply data (obtained through flow sensors and other devices) of each water valve. Then calculate the deviation modulus of the two, that is, the absolute value of the difference, and then divide the deviation modulus by the theoretical water supply to obtain the fluctuation ratio of each water valve. These fluctuation ratios constitute the water valve control fluctuation ratio set. By calculating the water valve control fluctuation ratio, the water supply fluctuation of each water valve can be quantified, so that the stability of the water valve control can be intuitively understood, and the water valves that may have water supply deviations can be discovered in time, providing basic data for the subsequent evaluation of the performance of the entire water supply system.

[0026] Step S3: summing up the products of the one-to-one corresponding water valve control fluctuation ratio set and the theoretical water supply quantity identifier set to obtain the fluctuating water supply quantity.

[0027] Specifically, through the calculation process in step S2, it can be known that each water valve control fluctuation ratio corresponds to a theoretical water supply quantity mark, that is, the water valve control fluctuation ratio set corresponds to the elements in the theoretical water supply quantity mark set one by one. Each element in the water valve control fluctuation ratio set and the element in the corresponding theoretical water supply quantity mark set are extracted one by one, and the product operation is performed, and then all the product results are added to obtain the fluctuating water supply. For example, if the water valve control fluctuation ratio set is {0.1, 0.15, 0.05}, and the theoretical water supply quantity mark set is {3 cubic meters, 5 cubic meters, 2 cubic meters}, then the fluctuating water supply = (0.1×3) + (0.15×5) + (0.05×2) = 0.3 + 0.75 + 0.1 = 1.15 cubic meters.

[0028] Through step S3, a value (fluctuating water supply) is obtained that comprehensively reflects the fluctuation of water supply in the entire water supply system. This value comprehensively considers the fluctuation of each water valve and the theoretical water supply, and can more comprehensively evaluate the stability and accuracy of the water supply system.

[0029] Step S4: When the fluctuating water supply is less than the fluctuating water supply threshold, the valve opening set, the duration set, the printing and dyeing wastewater treatment area and the required water inflow are uploaded to the water server.

[0030] Specifically, the calculated fluctuating water supply is compared with the pre-set fluctuating water supply threshold. If the fluctuating water supply is less than the threshold, it means that the current water supply system is in a relatively stable state. At this time, the valve opening set, duration set, printing and dyeing wastewater treatment area, and required water inflow information are uploaded to the water server through network communication (such as Ethernet, Wi-Fi, etc.). The water server can store and analyze these data for further management decisions.

[0031] Step S4 ensures that relevant important data will be uploaded to the water server only when the water supply system is operating stably, providing the water server with accurate and reliable data, which is conducive to the water management department to effectively monitor, manage and make decisions on the supply of printing and dyeing wastewater.

[0032] Furthermore, the embodiment of the present invention also includes:

[0033] Step S5: When the fluctuating water supply is greater than or equal to the fluctuating water supply threshold, the valve opening set and the duration set are updated to obtain an updated valve opening set and an updated duration set and execute a cycle.

[0034] Specifically, when it is judged that the fluctuating water supply is greater than or equal to the fluctuating water supply threshold, it means that the current water supply state is not stable enough or the water supply does not meet the requirements. At this time, it is necessary to adjust the valve opening set and the duration set. For example, the valve opening can be adjusted according to a certain proportion based on the deviation between the theoretical water supply and the actual water supply previously counted. For example, when the deviation is large, the opening can be appropriately increased or decreased, and the duration can be adjusted accordingly. The adjusted results are then used as the updated valve opening set and the updated duration set, and the previous cycle (steps S2, S3, S4) is executed again to re-evaluate the water supply situation. This adjustment process will be repeated multiple times until the requirements for stable water supply are met.

[0035] By updating the valve opening and duration, the water supply system can be dynamically adjusted to cope with unstable water supply or water supply that does not meet requirements. Re-executing the cycle can continuously optimize the water supply strategy so that the fluctuating water supply is eventually reduced below the threshold, thereby improving the stability and accuracy of the water supply, ensuring that the printing and dyeing wastewater treatment area can obtain the appropriate water intake and guaranteeing the wastewater treatment effect.

[0036] Further, such as Figure 2 As shown, step S1 includes:

[0037] Step S11: Matching a water supply calibration table according to the printing and dyeing wastewater treatment area, wherein any column of the water supply calibration table uniquely corresponds to one of multiple printing and dyeing wastewater sources, and any column of the water supply calibration table is uniquely associated with a water supply mapping model, and the water supply mapping model is used to process the valve opening and duration of the corresponding column of printing and dyeing wastewater sources to output the theoretical water supply.

[0038] Step S12: randomly configuring the initial valve opening degree set and initial duration set of the multiple printing and dyeing wastewater source water valve node groups.

[0039] Step S13: input the initial valve opening set and the initial duration set into the water supply calibration table, and output an initial theoretical water supply quantity identification set.

[0040] Step S14: When the sum of the initial theoretical water supply identification set is greater than the required water inlet, the initial valve opening set and the initial duration set are set as the valve opening set and the duration set, and the initial theoretical water supply identification set is set as the theoretical water supply identification set.

[0041] Specifically, the water supply calibration table is a table predefined for the printing and dyeing wastewater supply system, which records the water supply information of different printing and dyeing wastewater sources, as well as the mapping relationship between the valve opening and the duration corresponding to the wastewater source. In this table, each column corresponds to a printing and dyeing wastewater source and is associated with a specific water supply mapping model, which is used to calculate the theoretical water supply based on the valve opening and duration. According to the printing and dyeing wastewater treatment area, a matching search is performed in the existing water supply calibration table to select a suitable water supply calibration table. Each column of the water supply calibration table corresponds to a wastewater source, and the column contains the mapping relationship between the valve opening and the duration corresponding to the wastewater source, as well as the water supply calculation model. By matching the water supply calibration table, a foundation is established for the subsequent calculation of the theoretical water supply, and the appropriate calculation model and the corresponding relationship between the printing and dyeing wastewater source can be obtained according to the specific conditions of the printing and dyeing wastewater treatment area, ensuring the accuracy and pertinence of the theoretical water supply calculation.

[0042] After determining the water supply calibration table and mapping model related to the printing and dyeing wastewater source, the random number generation algorithm is used to randomly set the initial valve opening set and initial duration set for the multi-printing and dyeing wastewater source water valve node group. The random configuration here needs to be carried out within the reasonable range allowed by the process and equipment performance. For example, the valve opening is randomly selected between 0% and 100%, and the duration is randomly selected within a reasonable interval (such as 0.5 hours to 5 hours) according to the normal cycle of printing and dyeing wastewater treatment. The initial valve opening set and the initial duration set provide an initial trial value for the subsequent theoretical water supply calculation.

[0043] Input the initial valve opening set and the initial duration set into the water supply mapping model corresponding to the water supply calibration table. According to the calculation rules of the water supply mapping model, the theoretical water supply corresponding to each printing and dyeing wastewater source is calculated, and these theoretical water supplies constitute the initial theoretical water supply identification set. For example, if there are three printing and dyeing wastewater sources, after inputting the initial valve opening and duration, the theoretical water supply is calculated by their respective water supply mapping models to be 3 cubic meters, 4 cubic meters, and 2 cubic meters respectively, then the initial theoretical water supply identification set is {3 cubic meters, 4 cubic meters, 2 cubic meters}.

[0044] Calculate the sum of the initial theoretical water supply quantity identification set. Then compare this sum with the required water inflow. If the sum is greater than the required water inflow, it means that the current initial valve opening set, initial duration set, and initial theoretical water supply quantity identification set meet the current water supply requirements and can be directly set as the final valve opening set, duration set, and theoretical water supply quantity identification set.

[0045] The above steps, through the coordination of the water supply calibration table and the water supply mapping model, can accurately map the valve opening and duration with the actual water supply, ensuring the rationality of the initial parameters and laying the foundation for subsequent dynamic adjustment and optimization.

[0046] Furthermore, step S11 includes:

[0047] Step S111: obtaining a first water supply mapping model from a first printing and dyeing wastewater source to the printing and dyeing wastewater treatment area.

[0048] Step S112: until the Mth water supply mapping model from the Mth printing and dyeing wastewater source to the printing and dyeing wastewater treatment area is obtained.

[0049] Step S113: constructing the water supply calibration table according to the first water supply mapping model to the Mth water supply mapping model.

[0050] Specifically, multiple printing and dyeing wastewater sources are numbered and distinguished. The first printing and dyeing wastewater source is the first source mentioned or processed among the numerous printing and dyeing wastewater sources, and the Mth printing and dyeing wastewater source represents the Mth in this series of printing and dyeing wastewater sources. Here, M is a positive integer, indicating the total number of printing and dyeing wastewater sources. For example, in a system with 5 printing and dyeing wastewater sources, M=5.

[0051] By analyzing the demand for the printing and dyeing wastewater treatment area, the characteristics of the first printing and dyeing wastewater source (such as the flow characteristics, water quality, source pressure, etc. of the wastewater), and the pipeline system connecting the first printing and dyeing wastewater source to the printing and dyeing wastewater treatment area, a water supply mapping model is established for the first printing and dyeing wastewater source, namely the first water supply mapping model.

[0052] Similarly, repeat the above analysis operation for the remaining printing and dyeing wastewater sources (the 2nd wastewater source to the Mth wastewater source) among the multiple printing and dyeing wastewater sources, establish corresponding water supply mapping models for them, and obtain the second water supply mapping model until the Mth water supply mapping model.

[0053] Finally, these models (all water supply mapping models from the first wastewater source to the Mth wastewater source) are integrated into a water supply calibration table. This water supply calibration table is a multi-dimensional data table that contains the water supply mapping model of each printing and dyeing wastewater source and the correlation between related parameters, providing a unified and comprehensive reference standard for the subsequent calculation of theoretical water supply based on the printing and dyeing wastewater source. Through this table, the theoretical water supply can be calculated based on the valve opening and duration of each wastewater source.

[0054] Furthermore, the step of constructing any one of the water supply mapping models from the first water supply mapping model to the Mth water supply mapping model includes:

[0055] Step S11 - 1 : configuring a water supply topology from a preset printing and dyeing wastewater source to a preset area, wherein the water supply topology includes a water supply valve distribution node, and the preset printing and dyeing wastewater source has a fixed water inlet flow identifier.

[0056] Step S11-2: With the water supply valve distribution nodes, the fixed water inlet flow identifier and the water supply topology as constraints, collect the water supply valve opening record data, duration record data and preset area water supply record data within the preset service life.

[0057] Step S11-3: Construct a graph neural network topology based on the water supply topology, wherein the graph neural network topology is the same as the water supply topology, the input nodes of the graph neural network topology are the neural network nodes corresponding to the water supply valve distribution nodes, and the output nodes are the neural network nodes corresponding to the preset area water supply nodes.

[0058] Step S11-4: Using the preset area water supply record data as supervision, and the water supply valve opening record data and the duration record data as input, train the graph neural network topology to obtain any one of the first water supply mapping model to the Mth water supply mapping model.

[0059] Specifically, according to the actual layout of the preset printing and dyeing wastewater source and the preset area and the design requirements of the water supply system, the distribution nodes of the water supply valve in the entire water supply system are determined to construct the water supply topology. This water supply topology is a model that describes the structure of the water supply system between the preset printing and dyeing wastewater source and the preset area, including information such as the distribution nodes of the water supply valve in this system, which helps to understand the structural relationship of the entire water supply system. For example, in a simple printing and dyeing wastewater supply system, there are three pipelines from the wastewater source to the treatment area, and each pipeline has a valve. Then the water supply topology will show the distribution nodes of the three valves and their connection relationship with the wastewater source and the treatment area. In the water supply topology, the preset printing and dyeing wastewater source has a specific and relatively fixed inflow flow mark, that is, a fixed inflow flow mark. This mark can be a specific flow value (such as 5 cubic meters per hour) or a flow range, which is used to indicate the inflow flow of the wastewater source under normal conditions.

[0060] Under the constraints of the determined water supply topology and the fixed inflow flow mark of the known preset printing and dyeing wastewater source, specific data is collected from the relevant data recording system. Here we mainly focus on the water supply valve opening record data, duration record data and preset area water supply record data within the preset service life. For example, if the preset service life is 1 year, the valve opening record data and duration record data of the water supply valve with a service life of less than 1 year are screened out from the database. These data are continuously recorded by sensors (such as valve opening sensors, flow sensors, etc.) during actual operation. The preset service life is usually set to a shorter time to screen out relatively new pipelines. The flow-affecting factors in such pipelines usually only include the valve opening, which ensures the validity of the subsequent mapping relationship.

[0061] Create a graph neural network topology based on the constructed water supply topology. Convert each water supply valve distribution node in the water supply topology to a corresponding neural network node in the graph neural network, and convert the preset area water supply node to a corresponding neural network node. For example, if there are 5 valve distribution nodes and 1 preset area water supply node in the water supply topology, then the graph neural network topology will have 5 input nodes and 1 output node, and the connection relationship between these nodes is the same as the actual structure in the water supply topology.

[0062] The collected water supply record data of the preset area is used as the supervision data (i.e., the target output), and the water supply valve opening record data and duration record data are used as the input data to train the constructed graph neural network topology. During the training process, the graph neural network will continuously adjust its weights and parameters according to the input data and supervision data through optimization algorithms such as stochastic gradient descent to minimize the difference between the predicted output and the supervision data. After multiple iterations of training, a water supply mapping model is obtained that can accurately predict the water supply based on the valve opening and duration. This model can be any one of the first water supply mapping model to the Mth water supply mapping model. These water supply mapping models can all be constructed using the above-mentioned steps of configuring the water supply topology, training and constructing the graph neural network topology, etc.

[0063] The above steps make full use of the topological structure and actual operation data of the water supply system, and combine with the graph neural network technology to train the water supply mapping model. Graph neural networks can handle complex water supply topological structures, consider the mutual influence between various wastewater sources, and automatically learn and adjust the water supply mapping relationship, thereby improving the accuracy and adaptability of water supply prediction, making water supply control more flexible and accurate.

[0064] Further, step S5 includes:

[0065] Step S51: When the number of updates satisfies the K value, and the K-times updated fluctuating water supply is greater than or equal to the fluctuating water supply threshold, K fluctuating water supplies are obtained.

[0066] Step S52: respectively combining the normalized parameters of the previous K valve opening sets and the previous K duration sets corresponding to each other to construct K multi-element arrays.

[0067] Step S53: Calculate the pairwise Euclidean distances of the K multivariate arrays to obtain a plurality of Euclidean distances, and calculate the variances of the plurality of Euclidean distances to set as the updated discrete coefficient.

[0068] Step S54: When the updated discrete coefficient is greater than or equal to the updated discrete coefficient threshold, the minimum value of the K fluctuating water supplies is extracted as a selected valve opening set and a selected duration set.

[0069] Step S55: uploading the selected valve opening set, the selected duration set, the printing and dyeing wastewater treatment area, the required water inflow and the minimum fluctuating water supply to the water service server.

[0070] Specifically, when the valve opening set and the duration set are updated, when the number of updates reaches K value, and the fluctuating water supply after each update is greater than or equal to the fluctuating water supply threshold, the K times of fluctuating water supply are recorded. Among them, K value is a preset threshold, K is a positive integer, and specifies the number of iterations of the update process.

[0071] For the previous K valve opening sets and corresponding duration sets, the parameters are normalized, that is, the parameter values ​​in different ranges are converted into a unified and comparable range (for example, between 0 and 1). The normalization process can be completed by the minimum-maximum normalization algorithm. Then, the normalized parameters of each valve opening and duration are combined to construct K multivariate arrays.

[0072] For the constructed K multivariate arrays, the Euclidean distance between any two multivariate arrays is calculated to obtain several Euclidean distance values. Then the variance of these Euclidean distance values ​​is calculated and used as the update dispersion coefficient to measure the degree of dispersion of these combined parameters in the process of updating the valve opening set and the duration set for multiple times. If the update dispersion coefficient is large, it means that the changes in the valve opening and duration are relatively scattered and lack regularity during each update.

[0073] The calculated update discrete coefficient is compared with the preset update discrete coefficient threshold. This update discrete coefficient threshold is a preset value used to determine whether the update discrete coefficient is within an acceptable range. If the update discrete coefficient is greater than or equal to the update discrete coefficient threshold, it means that during multiple updates, the changes in valve opening and duration are relatively discrete, that is, during the update process, the selected water supply parameters (valve opening and duration) are widely distributed and have covered most of the water supply schemes. At this time, find the minimum value from the K fluctuating water supplies, and then extract the valve opening set and duration set corresponding to this minimum value as the selected valve opening set and selected duration set, respectively.

[0074] The selected valve opening set, duration set, printing and dyeing wastewater treatment area, required water inflow, and corresponding minimum fluctuation water supply are uploaded to the water server through network communication (such as Ethernet, Wi-Fi, etc.). The water server can store and analyze these data for further management decisions.

[0075] Furthermore, when the update discrete coefficient is less than the update discrete coefficient threshold, the update number is reduced by 0.5 times and then an update cycle is executed.

[0076] Specifically, when the calculated update discrete coefficient is less than the update discrete coefficient threshold, it means that the valve opening and duration selected in the previous update process are relatively concentrated and only updated in a local range. Therefore, it is necessary to continue updating to traverse more possible parameter selection schemes. At this time, the number of updates is reduced to 0.5 times the original number. For example, if the previous number of updates was 10, the number of updates now becomes 5. Then, the entire update cycle is executed again, including updating the valve opening set and the duration set, recalculating the fluctuating water supply, constructing a multivariate array, calculating the update discrete coefficient, and a series of other operations until the conditions for the end of the cycle are met (such as the fluctuating water supply is less than the fluctuating water supply threshold or the maximum number of updates is reached). Due to the reduction in the number of updates, each iteration of the optimization process will be more refined, ensuring that the water supply control strategy can achieve the best effect in fewer iterations while reducing the amount of calculation.

[0077] In this way, when the update discrete coefficient is small, we try to further optimize the combination of valve opening and duration with fewer updates, improve the efficiency and stability of the entire printing and dyeing wastewater supply system, and converge more quickly to a combination of valve opening and duration that meets the requirements.

[0078] The embodiment of the present invention provides a smart water data processing method based on the Internet of Things, which has at least the following technical effects:

[0079] By receiving the demand water inflow uploaded by users and configuring the valve opening and duration sets of multiple wastewater sources, the water supply system is provided with a theoretical water supply identification. On this basis, the water valve node group is traversed to calculate the deviation between the theoretical water supply and the actual water supply in each period, and the water valve control fluctuation ratio set is adjusted to obtain the fluctuating water supply. When the fluctuating water supply is less than the preset threshold, the current valve opening and duration are uploaded to the water service server as the optimal control strategy; when the fluctuating water supply is large, the control strategy of the water supply is continuously optimized by updating the valve opening and duration sets. In order to adjust the water supply more finely, a graph neural network is used to model the relationship between the water supply and the valve opening and duration, and a stable and accurate water supply control scheme is generated by gradually optimizing the model. During the update process, the parameters of the valve opening and duration are normalized, the Euclidean distance calculation method is used to evaluate the difference of each update, and the statistical discrete coefficient is used to determine whether the optimization has stabilized. Ultimately, it is ensured that after multiple iterations of optimization, the selected valve opening and duration combination can provide the most stable and accurate water supply control strategy while meeting the required water inlet.

[0080] Overall, the embodiment of the present invention realizes precise regulation of the printing and dyeing wastewater supply system through a demand-driven dynamic water supply control method, combined with the intelligent configuration of valve opening set, duration set and theoretical water supply volume, significantly improves the water supply stability and wastewater treatment effect in the printing and dyeing wastewater treatment process, and realizes the intelligence and automation of water supply management by uploading data to the water server, providing reliable guarantee for the efficient operation of printing and dyeing wastewater treatment.

[0081] Embodiment 2:

[0082] like Figure 3 As shown, based on the same inventive concept as the method for processing smart water affairs data based on the Internet of Things provided in Embodiment 1, an embodiment of the present invention further provides a smart water affairs data processing system based on the Internet of Things, the system comprising:

[0083] The water valve node configuration module 10 is used to receive the required water inlet of the printing and dyeing wastewater treatment area uploaded by the user side, and configure the valve opening set and duration set of the multi-printing and dyeing wastewater source water valve node group, wherein the valve opening set and the duration set have a theoretical water supply volume identification set.

[0084] The control fluctuation calculation module 20 is used to traverse the water valve node group, calculate the deviation modulus of the theoretical water supply and the actual water supply in the preset time zone, and set the ratio of the deviation modulus to the theoretical water supply as the water valve control fluctuation ratio set.

[0085] The fluctuating water supply calculation module 30 is used to add the products of the one-to-one corresponding water valve control fluctuation ratio set and the theoretical water supply identification set to obtain the fluctuating water supply.

[0086] The data uploading module 40 is used to upload the valve opening set, the duration set, the printing and dyeing wastewater treatment area and the required water inflow to the water server when the fluctuating water supply is less than the fluctuating water supply threshold.

[0087] Furthermore, the data upload module 40 of the embodiment of the present invention is also used to perform the following steps:

[0088] When the fluctuating water supply is greater than or equal to the fluctuating water supply threshold, the valve opening set and the duration set are updated to obtain an updated valve opening set and an updated duration set and execute a cycle.

[0089] Furthermore, the water valve node configuration module 10 of the embodiment of the present invention is also used to perform the following steps:

[0090] A water supply calibration table is matched according to the printing and dyeing wastewater treatment area, wherein any column of the water supply calibration table uniquely corresponds to one of the multiple printing and dyeing wastewater sources, and any column of the water supply calibration table is uniquely associated with a water supply mapping model, and the water supply mapping model is used to process the valve opening and duration of the corresponding column of printing and dyeing wastewater sources to output a theoretical water supply; an initial valve opening set and an initial duration set of the water valve node group of the multiple printing and dyeing wastewater sources are randomly configured; the initial valve opening set and the initial duration set are input into the water supply calibration table, and an initial theoretical water supply identification set is output; when the sum of the initial theoretical water supply identification set is greater than the required water inlet, the initial valve opening set and the initial duration set are set as the valve opening set and the duration set, and the initial theoretical water supply identification set is set as the theoretical water supply identification set.

[0091] Furthermore, the water valve node configuration module 10 of the embodiment of the present invention is also used to perform the following steps:

[0092] Obtain a first water supply mapping model from a first printing and dyeing wastewater source to the printing and dyeing wastewater treatment area; until obtaining an Mth water supply mapping model from an Mth printing and dyeing wastewater source to the printing and dyeing wastewater treatment area; construct the water supply calibration table according to the first water supply mapping model until the Mth water supply mapping model.

[0093] Furthermore, the water valve node configuration module 10 of the embodiment of the present invention is also used to perform the following steps:

[0094] Configure a water supply topology from a preset printing and dyeing wastewater source to a preset area, wherein the water supply topology includes a water supply valve distribution node, and the preset printing and dyeing wastewater source has a fixed water inlet flow identifier; with the water supply valve distribution node, the fixed water inlet flow identifier and the water supply topology as constraints, collect water supply valve opening record data, duration record data and preset area water supply record data with a service life within a preset period; construct a graph neural network topology based on the water supply topology, wherein the graph neural network topology is the same as the water supply topology, the input nodes of the graph neural network topology are the neural network nodes corresponding to the water supply valve distribution nodes, and the output nodes are the neural network nodes corresponding to the preset area water supply nodes; with the preset area water supply record data as supervision, and the water supply valve opening record data and the duration record data as input, train the graph neural network topology to obtain any one of the first water supply mapping model to the Mth water supply mapping model.

[0095] Furthermore, the data upload module 40 of the embodiment of the present invention is also used to perform the following steps:

[0096] When the number of updates meets the K value, and the K updated fluctuating water supplies are greater than or equal to the fluctuating water supply threshold, K fluctuating water supplies are obtained; the normalized parameters of the previous K valve opening sets and the previous K duration sets corresponding to each other are respectively combined to construct K multivariate arrays; the pairwise Euclidean distances of the K multivariate arrays are calculated to obtain a number of Euclidean distances, and the variances of the several Euclidean distances are counted and set as the updated discrete coefficient; when the updated discrete coefficient is greater than or equal to the updated discrete coefficient threshold, the minimum value of the K fluctuating water supplies is extracted as the selected valve opening set and the selected duration set; the selected valve opening set, the selected duration set, the printing and dyeing wastewater treatment area, the required water inlet and the minimum fluctuating water supply are uploaded to the water server.

[0097] Furthermore, the data upload module 40 of the embodiment of the present invention is also used to perform the following steps:

[0098] When the update discrete coefficient is less than the update discrete coefficient threshold, the update cycle is executed after the update times are reduced by 0.5 times.

[0099] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0101] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A smart water data processing method based on the Internet of Things, characterized in that: Applied to IoT centers, including: Receive the required water inflow of the printing and dyeing wastewater treatment area uploaded by the user side, and configure the valve opening set and the duration set of multiple printing and dyeing wastewater source water valve node groups, wherein the valve opening set and the duration set have a theoretical water supply volume identifier set; Traverse the water valve node group, count the deviation modulus of the theoretical water supply and the actual water supply in the preset time zone, and the ratio of the deviation modulus to the theoretical water supply, and set it as the water valve control fluctuation ratio set; Add the products of the one-to-one corresponding water valve control fluctuation ratio set and the theoretical water supply quantity identifier set to obtain the fluctuation water supply quantity; When the fluctuating water supply is less than the fluctuating water supply threshold, the valve opening set, the duration set, the printing and dyeing wastewater treatment area and the required water inflow are uploaded to the water service server; Among them, the required water inflow of the printing and dyeing wastewater treatment area uploaded by the user side is received, and the valve opening set and duration set of the multi-printing and dyeing wastewater source water valve node group are configured, including: A water supply calibration table is matched according to the printing and dyeing wastewater treatment area, wherein any column of the water supply calibration table uniquely corresponds to one of the multiple printing and dyeing wastewater sources, and any column of the water supply calibration table is uniquely associated with a water supply mapping model, and the water supply mapping model is used to process the valve opening and duration of the corresponding column of the printing and dyeing wastewater source to output the theoretical water supply; Randomly configuring the initial valve opening set and initial duration set of the multiple printing and dyeing wastewater source water valve node groups; Input the initial valve opening set and the initial duration set into the water supply calibration table, and output an initial theoretical water supply quantity identification set; When the sum of the initial theoretical water supply identification set is greater than the required water inlet, the initial valve opening set and the initial duration set are set as the valve opening set and the duration set, and the initial theoretical water supply identification set is set as the theoretical water supply identification set.

2. The method for processing smart water affairs data based on the Internet of Things according to claim 1, characterized in that: Also includes: When the fluctuating water supply is greater than or equal to the fluctuating water supply threshold, the valve opening set and the duration set are updated to obtain an updated valve opening set and an updated duration set and execute a cycle.

3. The method for processing smart water affairs data based on the Internet of Things according to claim 1, characterized in that: According to the printing and dyeing wastewater treatment area, the water supply calibration table is matched, including: Obtaining a first water supply mapping model from a first printing and dyeing wastewater source to the printing and dyeing wastewater treatment area; Until the Mth water supply mapping model from the Mth printing and dyeing wastewater source to the printing and dyeing wastewater treatment area is obtained; The water supply calibration table is constructed according to the first water supply mapping model to the Mth water supply mapping model.

4. The method for processing smart water affairs data based on the Internet of Things as claimed in claim 3, characterized in that: The step of constructing any one of the water supply mapping models from the first water supply mapping model to the Mth water supply mapping model comprises: Configuring a water supply topology from a preset printing and dyeing wastewater source to a preset area, wherein the water supply topology includes a water supply valve distribution node, and the preset printing and dyeing wastewater source has a fixed water inlet flow identifier; Taking the water supply valve distribution nodes, the fixed water inlet flow identifier and the water supply topology as constraints, collecting the water supply valve opening record data, the duration record data and the preset area water supply volume record data within the preset service life; According to the water supply topology, a graph neural network topology is constructed, wherein the graph neural network topology is the same as the water supply topology, the input nodes of the graph neural network topology are the neural network nodes corresponding to the water supply valve distribution nodes, and the output nodes are the neural network nodes corresponding to the preset area water supply nodes; Taking the preset area water supply record data as supervision and the water supply valve opening record data and the duration record data as input, the graph neural network topology is trained to obtain any water supply mapping model from the first water supply mapping model to the Mth water supply mapping model.

5. The method for processing smart water affairs data based on the Internet of Things as claimed in claim 2, characterized in that: When the fluctuating water supply is greater than or equal to the fluctuating water supply threshold, the valve opening set and the duration set are updated to obtain an updated valve opening set and an updated duration set and execute a cycle, including: When the number of updates meets the K value, and the K-times updated fluctuating water supply is greater than or equal to the fluctuating water supply threshold, K fluctuating water supplies are obtained; The normalized parameters of the previous K valve opening sets and the previous K duration sets that correspond to each other are combined to construct K multivariate arrays; Calculate the pairwise Euclidean distances of the K multivariate arrays to obtain a plurality of Euclidean distances, and calculate the variances of the plurality of Euclidean distances to update the discrete coefficient; When the updated discrete coefficient is greater than or equal to the updated discrete coefficient threshold, extracting the selected valve opening set and the selected duration set of the minimum values ​​of the K fluctuating water supplies; The valve opening set, the duration set, the printing and dyeing wastewater treatment area, the required water inflow and the minimum fluctuation water supply are uploaded to the water server.

6. A method for processing smart water affairs data based on the Internet of Things as claimed in claim 5, characterized in that: Also includes: When the update discrete coefficient is less than the update discrete coefficient threshold, the update cycle is executed after the update times are reduced by 0.5 times.

7. A smart water data processing system based on the Internet of Things, characterized in that: The system is applied to an IoT center and is used to execute a smart water affairs data processing method based on the IoT as described in any one of claims 1 to 6, including: The water valve node configuration module is used to receive the required water inflow of the printing and dyeing wastewater treatment area uploaded by the user side, and configure the valve opening set and the duration set of the multi-printing and dyeing wastewater source water valve node group, wherein the valve opening set and the duration set have a theoretical water supply volume identification set; A control fluctuation calculation module is used to traverse the water valve node group, calculate the deviation modulus of the theoretical water supply and the actual water supply in the preset time zone, and set the ratio of the deviation modulus to the theoretical water supply as the water valve control fluctuation ratio set; A fluctuating water supply calculation module, used for summing the products of the one-to-one corresponding water valve control fluctuation ratio set and the theoretical water supply identification set to obtain the fluctuating water supply; The data uploading module is used to upload the valve opening set, the duration set, the printing and dyeing wastewater treatment area and the required water inflow to the water server when the fluctuating water supply is less than the fluctuating water supply threshold.

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