Water quality data processing method, system, and computer-readable storage medium

Abstract

Provided are an intelligent water quality data processing method, system, and computer readable storage medium. The method can include: entering water quality data to form an index table and a water quality detail table; preprocessing the entered water quality data; performing visual analysis on the preprocessed water quality data; and establishing a water quality data trend analysis model and performing prediction and scheme recommendation based on the model.

Description

Technical Field

[0001] This disclosure relates to water quality data processing technology in the field of water treatment. Specifically, this disclosure relates to an intelligent water quality data processing method, system, and computer-readable storage medium. Background Technology

[0002] In the water treatment industry, as companies continue to operate and expand their businesses, the number of projects they undertake and complete increases daily, and the corresponding project data grows exponentially. However, this valuable measured data is often archived in various electronic or paper folders after the project is completed, failing to be effectively utilized and becoming "data dormant." When historical experimental data needs to be referenced, technicians must sift through numerous folders, searching for the required information item by item, a tedious and inefficient process. Furthermore, due to the lack of systematic summarization and integration of data from similar projects, important information such as key process parameters and core empirical values ​​is difficult to extract and summarize quickly. This not only increases the possibility of duplication of effort but also affects the accuracy and responsiveness of technical decisions during the project design and bidding stages.

[0003] In current practice, project data is mostly managed using spreadsheet tools such as Excel. While this method achieves basic data archiving, it lacks flexible visualization and analysis capabilities, making it difficult to achieve multi-dimensional and multi-angle data comparison and in-depth analysis. Although some visualization database systems for water quality data exist, they are typically designed for simple data scenarios with limited data volume, and cannot support the comprehensive analysis needs of large-scale, multi-source, and complex water treatment project data. Therefore, existing technologies have significant shortcomings in visualization, interactive analysis, and experience extraction when dealing with massive, heterogeneous, and diverse project data.

[0004] To improve this situation and achieve efficient reuse and intelligent support of historical project data, this invention proposes an intelligent water quality data processing method and system. This method, based on AI and Python tools and Power BI visualization software, integrates, cleans, and structures scattered and dormant project data, presents it visually, and uses trend analysis models to perform water quality analysis and prediction for similar and new projects. This revitalizes data assets and provides real-time, intuitive, and multi-dimensional data support for project design and bidding. Summary of the Invention

[0005] This disclosure provides an intelligent water quality data processing method, system, and computer-readable storage medium.

[0006] According to a method disclosed herein, an intelligent water quality data processing method is provided. The method may include: inputting water quality data to form an index table and a water quality detail table; preprocessing the input water quality data; performing visual analysis on the preprocessed water quality data; and establishing a water quality data trend analysis model and making predictions and recommending solutions based on the model.

[0007] According to the method disclosed herein, the index table is associated with the water quality details table via the “TAG” field in the index table.

[0008] According to the method of this disclosure, the process of entering water quality data includes pre-determining or designing a structured data entry template before entering the water quality data.

[0009] According to the method disclosed herein, the water quality data entry includes: automatically downloading and uploading the original test report to the cloud storage system; extracting key content from the original test report using text parsing technology; and automatically identifying and extracting key data from the key content using natural language processing technology based on preset keyword recognition rules, and filling them into the index table and water quality detail table respectively.

[0010] According to the method disclosed herein, the preprocessing of water quality data includes data aggregation and data cleaning.

[0011] According to the method of this disclosure, data cleaning may include the following steps:

[0012] Standardize the names and / or units of the detection categories (S310, S320).

[0013] Determine whether all detection category names and / or units have been standardized (S311, S321).

[0014] If all is completed, output the dataset (S314, S324) in which the detection category names and / or units are unified.

[0015] If not all steps are completed, determine whether the detection category name and / or unit are identifiable (S313, S323). If identifiable, return to continue the unification process; if not identifiable, proceed to the manual intervention step (S314, S324).

[0016] According to the method disclosed herein, the method further includes: embedding the original test report as a link into the report obtained by visualization analysis, so that users can access and view the corresponding report through set permissions.

[0017] The method disclosed herein further includes: when new data is detected being uploaded, training and optimizing the model based on the newly uploaded data, and automatically storing the updated results.

[0018] The method disclosed herein also includes: automatically filtering or generating matching recommended solutions based on background information and requirements of the water treatment project input by the user.

[0019] According to another aspect of this disclosure, an intelligent water quality data processing system is provided. The system may include: a data entry module for entering water quality data to form an index table and a water quality detail table; a data preprocessing module for summarizing and cleaning the entered water quality data; a data analysis module for visual analysis of the preprocessed water quality data; and a modeling and prediction module for establishing a water quality data trend analysis model and making predictions and recommending solutions based on the model.

[0020] The system disclosed herein also includes: a data storage / update module for training and optimizing the model based on newly uploaded data and storing the updated results.

[0021] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory for storing one or more programs; and one or more processors communicatively coupled to the memory; wherein, when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement an intelligent water quality data processing method as described herein.

[0022] According to another aspect of this disclosure, a computer-readable storage medium is provided having executable instructions stored thereon that, when executed by a processor, enable the processor to implement the intelligent water quality data processing method as described herein. Attached Figure Description

[0023] The following description, along with the appendix Figure 1 When considered, the above and other aspects, features, and advantages of certain embodiments of this disclosure will become more apparent, as illustrated in the accompanying drawings:

[0024] Figure 1 A flowchart of an intelligent water quality data processing method according to an embodiment of the present disclosure is shown.

[0025] Figure 2 Exemplary index tables and water quality detail tables according to embodiments of this disclosure are shown.

[0026] Figure 3 A flowchart of a water quality data cleaning process according to an embodiment of the present disclosure is shown.

[0027] Figure 4 An exemplary water quality data visualization analysis report according to an embodiment of this disclosure is shown.

[0028] Figure 5A and Figure 5BA schematic diagram of a water quality data trend analysis model according to an embodiment of the present disclosure is shown.

[0029] Figure 6 A block diagram of an intelligent water quality data processing system according to an embodiment of the present disclosure is shown.

[0030] In the various figures, the same reference numerals will be understood to refer to the same parts, components and structures. Detailed Implementation

[0031] The following description, with reference to the accompanying drawings, is provided to assist in a thorough understanding of the various embodiments of the present disclosure as defined by the claims and their equivalents. This description includes various specific details to aid this understanding, but these details are to be considered exemplary only. Therefore, those skilled in the art will recognize that all other embodiments, with various changes and modifications to the various embodiments described herein, are within the scope of this invention without departing from the scope and spirit of the present disclosure.

[0032] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, method embodiments may include other steps and / or omit certain steps. In this disclosure, details well-known in the art have been omitted to avoid unnecessarily obscuring the scope of this disclosure.

[0033] It should be noted that the term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The modifications of "a" and "a plurality" mentioned in this disclosure are illustrative and not restrictive, and those skilled in the art should understand that they should be understood as "one or more" unless explicitly indicated in the context.

[0034] Figure 1 This invention presents an intelligent water quality data processing and analysis method, which mainly includes the following steps:

[0035] 1. Step S1: Enter water quality data to form an index table and a water quality detail table.

[0036] To achieve standardized and efficient data entry, a structured data entry template can be predetermined or designed before entry begins. For example, the entry template can be designed to include, for instance, […]. Figure 2The table shows index table 201 and water quality detail tables 202-1 and 202-2. The number of index tables and water quality detail tables can be determined based on the amount of data to be processed, data categories, etc. Index table 201 can be linked to water quality detail tables 202-1 and 202-2 via the "TAG" field, allowing water quality data to be directly pasted from reports or data tables without needing to fill in each item individually, thus improving data entry efficiency. Table 1 shows a specific example of using Python to link the index table and water quality detail tables via the TAG field, but this invention is not limited to this and other programming languages ​​can also be used.

[0037] Table 1

[0038]

[0039] Data entry can be performed manually or automatically. In automatic capture mode, the system can automatically download the original test report uploaded to the cloud storage system, extract key content from the original test report using text parsing technology, and then automatically identify and capture data from the key content based on preset keyword recognition rules, filling them into the index table and water quality details table respectively.

[0040] 2. Step S2: Preprocess the entered water quality data.

[0041] Preprocessing of water quality data can include data aggregation and data cleaning.

[0042] Data aggregation can automatically identify and aggregate index tables and water quality detail tables submitted by multiple users, and construct a unified water quality database master table. Table 2 shows a specific example of constructing a unified water quality database master table using the Python language, but the present invention is not limited to this and can also be implemented using other programming languages.

[0043] Table 2

[0044]

[0045] To ensure data quality, the aggregated water quality data also needs to be cleaned. This invention does not limit the order of data aggregation and cleaning; aggregation can be performed before cleaning, or cleaning can be performed before aggregation, or data cleaning can be performed simultaneously during the data aggregation process, thus standardizing the detection category names and units of the water quality data to be processed.

[0046] Figure 3 The process of cleaning the raw dataset obtained from the water quality table summary is shown.

[0047] The cleaning process can include two stages. In the first stage, the names of the water quality data testing categories are standardized; in the second stage, the testing units for the water quality data are standardized. There is no restriction on the order of these two stages; the first stage can be performed sequentially, followed by the second stage, or vice versa, or they can be performed simultaneously. The following example illustrates this. Figure 3 Let's take an example to illustrate.

[0048] First, we will proceed with the first stage of processing, as follows.

[0049] Step S310: Standardize the names of the test categories. For example, different expressions for the same test category, such as "bod", "bod5", and "bod5 biochemical oxygen demand", can be identified by using a pre-summarized "Excel data dictionary" that records the names of the test categories, and then standardized to "BOD5", thereby achieving standardization of the test category names.

[0050] Step S311: Determine if the standardization of detection category names is complete. Iterate through each detection category name and determine if the standardization process is complete. If "yes," proceed to step S314 to output the result of the standardized detection category names. If "no," proceed to step S312 to determine if the detection category name is recognizable. If recognizable, return to step S310 to continue the standardization process; if not recognizable, proceed to manual intervention step S313 until all involved detection category names are standardized.

[0051] Next, the second stage of processing will be carried out, and the specific process is as follows.

[0052] Step S320: Standardize the units of the detection categories. For example, by calling a preset unit conversion script (such as "all_to_mg_trans", "Ca_Mg_trans", "no_trans", etc.), the units of each detection category can be standardized to the target unit, thereby achieving automatic unit conversion.

[0053] Step S321: Determine if the unification of all detection category units is complete. Iterate through each detection category unit and determine if the unification process is complete. If "yes," proceed to step S324 to output the result of the unified detection category unit. If "no," proceed to step S322 to determine if the detection category unit is identifiable. If identifiable, return to step S320 to continue the unification process; if not identifiable, proceed to manual intervention step S323 until all involved detection category units are unified.

[0054] In step S313 or step S323, if an unrecognizable name or unit is found during the standardization process of the detection category name or unit, it can be recorded and classified into the "Name Conversion Table" or "Unit Conversion Table" for subsequent manual intervention and dictionary updates.

[0055] 3. Step S3: Perform visual analysis on the pretreated water quality data.

[0056] Based on the unified water quality database table with standardized testing category names and units, Power BI tools can be used to create corresponding visual reports for different elements (such as experiment type, industry classification, wastewater type, water quality characteristics, project distribution, annual project quantity, etc.). Figure 4 As shown in the diagram, through visual reports, users can intuitively view information such as the regional distribution, time trends, category classification, industry affiliation, and data range of projects, achieving multi-dimensional filtering and chart linkage in a "single-image" manner, which is conducive to quickly forming insights and making technical decisions.

[0057] Furthermore, to ensure data accuracy and security, the original test reports can be embedded as links into the database master table and / or visual reports. Through user permission settings, users can easily access and view the corresponding detailed reports, achieving data traceability and transparent management.

[0058] 4. Step S4: Establish a water quality data trend analysis model and make predictions and recommend solutions based on the model.

[0059] Based on the summary database table and visualization reports of water quality data, it is possible to construct, for example... Figure 5A and 5B The diagram shows a water quality trend analysis model.

[0060] Figure 5A This study demonstrates the correlation between O / C (oxygen content in organic matter) and COD removal rate during water treatment. By comparing different conditions and samples, and differentiating the correspondence between O / C and COD removal rate under different types of conditions, such as different catalysts and different treatment processes, it can help technicians select better water treatment solutions. In addition, by observing the fluctuation pattern of O / C with COD removal rate (such as the peak range of O / C at low COD removal rates), it provides data basis for adjusting treatment parameters (such as reaction time and dosage), thereby guiding technicians to optimize the process.

[0061] Figure 5BThis illustrates the dynamic relationship between the instantaneous O3 (ozone) conversion rate and the O3 conversion concentration. Different colors / markers represent different test groups (e.g., O3-1#-1, O3-2#-2, etc.), allowing for comparison of O3 conversion patterns under different conditions (e.g., different reactors, operating parameters). Figure 5B It can be seen that the changes in O3 conversion concentration are not the same in different groups, which is related to the degree of recalcitrant degradation of pollutants in wastewater, redox properties, and mass transfer efficiency of the reaction equipment. Based on Figure 5B The schematic diagram can be used to determine the ease with which pollutants react with ozone, optimize the ozone dosing process in water treatment, determine the economical and efficient O3 dosage, and evaluate the impact of different conditions on mass transfer rate.

[0062] Based on the constructed water quality data trend analysis model, the system can generate fitted curves and derive corresponding mathematical formulas. After users input water quality data for a new project, the system can predict key process parameters, such as dosage, reaction time, residence time, and filtration rate, based on the model. It can also calculate average values, providing data support and decision-making reference for water treatment in new projects. Furthermore, the system can train and optimize the water quality trend analysis model based on newly uploaded or updated unified water quality data and publish updated results.

[0063] Using the method provided by this invention, when a user inputs background information about the project (such as industry, wastewater type, main pollutants, etc.) and conditions (e.g., design influent and effluent indicators and proposed process schemes), the system can automatically filter matching historical schemes from the database or call AI to generate recommended schemes. The following is an example of calling AI to generate specific recommended schemes.

[0064] The following are examples of prompt words:

[0065] You are a water treatment process design expert with 20 years of experience, specializing in municipal / industrial wastewater, water supply treatment, and advanced treatment technologies.

[0066] Based on all the information below, please generate a "Water Treatment Technical Solution for Project XX".

[0067] Require:

[0068] 1. The logic is rigorous, the data is closed-loop, and the process is feasible;

[0069] 2. The chapters are complete and can be directly used for feasibility studies / preliminary designs / tender documents;

[0070] 3. Perform "triple verification" on historical data, model predictions, and recommended processes, and provide confidence levels and risk warnings;

[0071] 4. Output format: Markdown, including chart placeholders (hint "Insert chart here");

[0072] 5. Language: Chinese, with accurate terminology, avoiding colloquialisms;

[0073] 6. If there are conflicting data, please point out the contradictions and provide decision-making suggestions.

[0074] ---

[0075] ### 1. Project Background

[0076] {Project Background}

[0077] ### 2. Design Parameters

[0078] {Design Parameters}

[0079] ### 3. Proposed Process (Optional)

[0080] {Proposed parameters}

[0081] ### 4. Historical Data Package

[0082] #### 4.1 Historical Report

[0083] {Historical Report}

[0084] #### 4.2 Historical Data

[0085] {

[0086] - Influent and effluent water quality

[0087] - Removal rate (%) for each unit

[0088] - Energy consumption, chemical consumption, sludge production (kg / m³), etc.

[0089] }

[0090] #### 4.3 Historical Schemes (Previously Used Processes)

[0091] {Previous processes, investment, operating costs, and success / failure cases}

[0092] ### 5. Recommended Process Knowledge Base

[0093] {Official technical manuals, design specifications, patents, manufacturer samples, etc.}

[0094] ### 6. Model Prediction Data

[0095] - {Design influent, predict effluent quality, energy consumption, and chemical consumption for each process}

[0096] ## Please output the solution (Markdown) according to the following sections.

[0097] 1. Project Overview

[0098] 2. Design Basis and Standards

[0099] 3. Analysis of influent water quality characteristics (including historical data comparison)

[0100] 4. Effluent compliance judgment matrix (list + heat map)

[0101] 5. Comparison of process routes

[0102] 5.1 Candidate process list (including historical cases and model predictions)

[0103] 5.2 Multi-indicator decision making (AHP-TOPSIS, weights: investment 0.25, operation 0.25, compliance reliability 0.3, carbon emissions 0.2)

[0104] 5.3 Recommended process master plan (flowchart placeholder)

[0105] 6. Design calculations for major structures

[0106] 7. Sludge / Odor / Noise / Carbon Emission Support Solutions

[0107] 8. Energy consumption, drug consumption, and operating costs (model prediction vs. historical benchmarking)

[0108] 9. Risk assessment and mitigation measures (technology, water quality, policy, carbon market)

[0109] 10. Implementation Plan (Preliminary Testing → Detailed Design → EPC → Commissioning)

[0110] 11. Conclusions and Recommendations (including "If the influent COD > XXX, an emergency process package needs to be triggered")

[0111] ---

[0112] ### Additional Instructions

[0113] - All data citations must be properly attributed to their sources (historical data / models / literature / standards).

[0114] - Provide "confidence interval + boundary conditions" for model predictions.

[0115] - If historical data conflicts with the model, please prioritize using "cross-validation + sensitivity analysis" to resolve the issue, and provide the final value selection principle.

[0116] - Finally, output a one-page "Execution Summary" (≤400 characters) for leaders to make quick decisions.

[0117] In summary, this invention not only enables rapid retrieval and access to project data, improving data support efficiency, but also supports the system integration and comparative analysis of data from multiple projects, facilitating the extraction of patterns and differences. Furthermore, by leveraging AI and Python tools, this invention enhances the efficiency and accuracy of data cleaning and analysis, automates data processing, and constructs a data reporting system with interactive filtering and graphical display capabilities, thereby enhancing data visualization and analyzability and revitalizing historical data assets. Additionally, this invention can provide recommended solutions for future projects. For example, by training trend chart models for prediction and combining them with AI commands, it can provide analysis and evaluation of new water quality projects, automatically generating suggestions on dosage and reaction time.

[0118] On the other hand, such as Figure 6 As shown, the present invention provides an intelligent water quality data processing system, the various modules of which can implement the various steps of the aforementioned method. Specifically, the system includes:

[0119] Data entry module 601: Used to enter water quality data according to a pre-determined or designed structured data entry template. Specifically, data entry can be performed manually or automatically. In automatic capture mode, the system can automatically download the original test report uploaded to the cloud storage system via the cloud storage download module, extract key content from the original test report using text parsing technology through the report content extraction module, and then automatically identify and capture the data from the key content using natural language processing technology based on preset keyword recognition rules, filling them into the index table and water quality detail table respectively.

[0120] Data preprocessing module 602: Used for preprocessing the entered water quality data. Water quality data preprocessing includes summarization and cleaning. Standardizing the detection category names and units of the summarized water quality data can improve the accuracy of data analysis.

[0121] Data storage / update module 603: This module stores various data generated during water quality data processing, such as entered data, preprocessed data results, visualization analysis results, visualization reports, constructed water quality trend analysis models, and historical recommended solutions. Furthermore, when new reports or data are uploaded to the cloud, the system can automatically parse and update the results to data storage / update module 603 via the AI ​​data extraction module, without manual intervention. Users can also set up scheduled refresh tasks in Power BI tools to automatically connect to data sources and publish updated results to data storage / update module 603. Similarly, it can automatically link to other local data sources, such as Excel files and SQL databases, for automatic data update and storage.

[0122] Data Analysis Module 604: This module is used for visual analysis of pretreated water quality data, allowing users to intuitively view information such as the project's regional distribution, time trends, category classification, industry affiliation, and data scope. Furthermore, by setting user permissions, users can easily access and view corresponding detailed analysis reports, achieving data traceability and transparent management.

[0123] Modeling and Prediction Module 605: This module is used to establish a water quality data trend analysis model and to make predictions and recommend solutions based on this model. Using the entered water quality data and historical water quality data, a visual water quality trend analysis model is constructed. This model can also be trained and optimized, enabling it to predict key process parameters and provide recommended solutions.

[0124] In addition, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent water quality data processing method described above.

[0125] Furthermore, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned intelligent water quality data processing method.

[0126] Those skilled in the art will understand that embodiments of the present invention can be embodied in the form of methods, systems, or computer program products. Accordingly, the present invention can take the form of pure hardware embodiments, pure software embodiments, or hybrid software and hardware embodiments. Furthermore, the present invention can also take the form of a computer program product, deployed and executed on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The technical solutions of the embodiments of the present invention can be implemented in various computer languages, such as Python, which is widely used in data processing and artificial intelligence (AI) development, business intelligence (BI) visualization tools (such as Tableau, Power BI, FineBI, etc.), and AI visualization tools (such as Python ecosystem visualization libraries such as Matplotlib, Seaborn, Plotly, etc.) to complete data presentation and interactive analysis.

[0127] It should be understood that each flowchart and / or block, and combinations of flowcharts and / or blocks in the flowcharts and / or block diagrams of this invention, can be implemented by computer program instructions. These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment, thereby generating a computer-implemented processing procedure and ultimately realizing the function specified by one or more flowcharts in the flowchart and one or more blocks in the block diagram.

[0128] Those skilled in the art will recognize that this disclosure can be implemented in other predetermined forms without altering the technical concept or essential features of this disclosure. Therefore, it should be understood that the above embodiments are merely examples and not limiting. The scope of this disclosure is defined by the appended claims, not by the detailed description. Therefore, it should be understood that all modifications or variations derived from the meaning and scope of the appended claims and their equivalents are within the scope of this disclosure.

[0129] Although this disclosure has been shown and described with reference to various embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made therein without departing from the spirit and scope of this disclosure as defined by the claims and their equivalents.

Claims

1. An intelligent water quality data processing method, the method comprising: Enter water quality data to create an index table and a detailed water quality table; Preprocess the entered water quality data; Visual analysis of pretreated water quality data; as well as Establish a water quality data trend analysis model and make predictions and recommend solutions based on the model.

2. The method as described in claim 1, wherein, The index table is linked to the water quality details table through the "TAG" field in the index table.

3. The method as described in claim 1, wherein, Entering water quality data includes pre-determining or designing structured data entry templates before entering the water quality data.

4. The method of claim 1, wherein, The water quality data to be entered includes: Automatically download and upload the original test report to the cloud storage system; Key content was extracted from the original test report using text parsing technology; and Based on preset keyword recognition rules, natural language processing technology is used to automatically identify and extract data from key content and fill them into the index table and water quality details table respectively.

5. The method of claim 1, wherein, Water quality data preprocessing includes data aggregation and data cleaning.

6. The method of claim 5, wherein, Data cleaning includes the following steps: Standardize the names and / or units of the detection categories (S310, S320). Determine whether all detection category names and / or units have been standardized (S311, S321). If all is completed, output the dataset with the detection category name and / or unit uniformity (S314, S324). If not all steps are completed, check if the category name and / or unit are identifiable (S313, S323). If identifiable, return to continue the unification process. If it is not identifiable, proceed to the manual intervention step (S314, S324).

7. The method of claim 1, further comprising: The original test report is embedded as a link into the report generated by the visualization analysis, allowing users to access and view the corresponding report according to the set permissions.

8. The method of claim 1, further comprising: When new data is detected being uploaded, the model is trained and optimized based on the newly uploaded data, and the updated results are automatically stored.

9. The method of claim 1, further comprising: Based on the background information and requirements of the water treatment project input by the user, the system automatically filters or generates matching recommended solutions.

10. An intelligent water quality data processing system, the system comprising: The data entry module is used to input water quality data to form an index table and a water quality detail table; The data preprocessing module is used to summarize and clean the entered water quality data; The data analysis module is used for visual analysis of pretreated water quality data; as well as The modeling and prediction module is used to build a water quality data trend analysis model and make predictions and recommendations based on the model.

11. The system of claim 10, further comprising: The data storage / update module is used to train and optimize the model based on newly uploaded data and store the updated results.

12. An electronic device, comprising: Memory, used to store one or more programs; One or more processors are communicatively coupled to the memory; Wherein, when one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-9.

13. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, enable the processor to perform the method of any one of claims 1-9.

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