Water supply plant drainage intelligent control system based on data atlas

Abstract

The invention discloses a waterworks drainage intelligent control system based on a data atlas, which belongs to the technical field of waterworks drainage control and comprises an initial module, a demand adjustment module, a monitoring module, an evaluation module and a control module. The initial module is used for setting each reference map according to user requirements and establishing an element attribute library; the demand adjustment module is used for performing map processing according to map demands of the user to obtain a demand map; the monitoring module is used for monitoring the waterworks in real time to obtain corresponding map monitoring data; the evaluation module is used for monitoring and evaluating according to the monitoring data of each map to obtain each monitoring and evaluation demand of the user; setting a unit map corresponding to each monitoring evaluation demand; extracting the corresponding atlas monitoring data according to each unit atlas to obtain unit data corresponding to each unit atlas; evaluating according to the data of each unit to obtain a monitoring evaluation result; and the control module is used for performing control processing according to the reference evaluation result and the monitoring evaluation result.

Description

Technical Field

[0001] The present invention belongs to the technical field of waterworks drainage control, and in particular is an intelligent drainage control system for a waterworks based on a data map. Background Art

[0002] In today's era of rapid development of informatization and intelligence, water plants are an important part of urban water supply systems. Their operating efficiency and water quality management are directly related to the quality of life of residents and the safety and stability of cities. With the increasing maturity of technologies such as big data and artificial intelligence, data graphs, as an efficient way of data organization and management, are gradually being widely used in all walks of life. However, in the field of water plant management, traditional data management models often have problems such as data islands and information fragmentation, which makes it difficult to meet the increasingly complex and sophisticated management needs.

[0003] In the existing technology, although some systems have tried to integrate various data resources in water plants by building data graphs, most of these systems are based on fixed data structures and requirements for graph design, lacking flexibility and scalability. When user needs change, it is often necessary to redesign and build a new data graph, which not only increases the system development cost and maintenance difficulty, but also limits the application scope and use effect of the system.

[0004] Based on this, the present invention provides a waterworks drainage intelligent control system based on data maps. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned solutions, the present invention provides an intelligent drainage control system for a waterworks based on a data map.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The intelligent control system for waterworks drainage based on data graph includes initial module, demand adjustment module, monitoring module, evaluation module and control module;

[0008] The initial module is used to set each benchmark map according to user needs and establish an element attribute library, and the element attribute library is used to store each element and the attribute data corresponding to each element.

[0009] Furthermore, the method for determining each element and attribute data in the element attribute library includes:

[0010] Acquire atlas material data in real time, identify each reserve element according to each atlas material data, and set corresponding reserve attributes for each reserve element; establish a reserve library according to each reserve element and reserve attribute;

[0011] A matching analysis model is established based on the reserve library. The expression of the matching analysis model is:

[0012]

[0013] Where: (si, Y) is the input data, si represents the corresponding reserve element in the reserve, i = 1, 2, ..., n, n is a positive integer; Y is the water plant information; the output data is the matching analysis result, and the matching analysis result is attribute data or nothing;

[0014] The user's water plant information is obtained, and the water plant information is analyzed by the matching analysis model to obtain matching analysis results corresponding to each reserve element; and each element and element attribute is determined according to each matching analysis result.

[0015] Furthermore, the reserve library is updated based on the update of the atlas material data, and each reserve element and reserve attribute stored in the reserve library is checked.

[0016] The demand adjustment module is used to perform graph processing according to the graph demand of the user, identify the graph demand of the user, and determine the processing category corresponding to the graph demand, wherein the processing category includes a graph adjustment category and a new graph category;

[0017] When the processing category is the atlas adjustment category, the corresponding benchmark atlas is adjusted according to the atlas requirements;

[0018] When the processing category is a new graph category, a corresponding data graph is generated according to the graph requirements and element attribute library and marked as a requirement graph.

[0019] Furthermore, the method for adjusting the corresponding reference atlas according to the atlas requirement includes:

[0020] Identify the adjustment element corresponding to the graph requirement, match the corresponding reserve attribute from a preset reserve library according to the adjustment element, and determine the attribute data of the adjustment element according to the reserve attribute;

[0021] A reference map that needs to be adjusted is determined according to each adjustment element; and the reference map is adjusted according to the attribute data of each adjustment element.

[0022] Furthermore, the method for generating a corresponding data graph according to the graph requirements and the element attribute library includes:

[0023] Determine each requirement element according to the graph requirement; match the attribute data corresponding to each requirement element from the element attribute library;

[0024] The demand elements are associated with each other according to the attribute data to form an element association diagram, and a data map is generated based on the element association diagram.

[0025] Furthermore, before generating a data map based on the element association diagram, the user can select a preset map mode.

[0026] The monitoring module is used to perform real-time monitoring of the water plant and obtain monitoring data; identify each benchmark map and demand map, classify the monitoring data according to each benchmark map and demand map, obtain map monitoring data corresponding to each benchmark map and demand map, and display each map monitoring data in real time.

[0027] The evaluation module is used to perform monitoring evaluation according to each atlas monitoring data, obtain a benchmark evaluation method, analyze the monitoring data based on the benchmark evaluation method, obtain corresponding benchmark evaluation results, and display the benchmark evaluation results to the user;

[0028] Obtain the user's monitoring and evaluation requirements; identify the attribute data corresponding to each element in the benchmark map and the demand map; segment the corresponding benchmark map and demand map according to each monitoring and evaluation requirement to form a unit map corresponding to each monitoring and evaluation requirement;

[0029] Extracting corresponding atlas monitoring data according to each of the unit atlases to obtain unit data corresponding to each of the unit atlases;

[0030] An evaluation is performed based on each of the unit data to obtain a monitoring and evaluation result corresponding to each of the monitoring and evaluation requirements, and each of the monitoring and evaluation results is displayed to a user.

[0031] Further, the method of evaluating based on unit data includes:

[0032] Identify the attribute data of each element corresponding to the unit data, obtain the associated material data of each element according to each attribute data, and generate an associated verification model according to each associated material data; the associated verification model is used to determine whether the association between the elements is abnormal;

[0033] The unit data is evaluated in real time through the associated verification model to obtain monitoring and evaluation results corresponding to the monitoring and evaluation requirements.

[0034] Furthermore, the method for generating the associated verification model according to each associated material data includes:

[0035] Generate an element association model according to the unit map, wherein the element association model is a data model for representing the association between elements;

[0036] The element association model is iteratively trained through the association material data, and an association verification model is established through the element association model after successful training. The expression of the association verification model is:

[0037]

[0038] Where: q is the input data, which is the unit data; the output data is the associated check value GL(q), which is 1 or 0;

[0039] When the correlation check value is 1, the correlation between the elements is evaluated to be abnormal;

[0040] When the correlation check value is 0, the correlation between the elements is assessed to be normal.

[0041] The control module is used to perform control processing according to the benchmark assessment results and the monitoring assessment results, identify the corresponding benchmark assessment results and the monitoring assessment results in real time, and perform control processing according to the benchmark assessment results and the monitoring assessment results in a preset control manner.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] Through the cooperation between the initial module, demand adjustment module, monitoring module, evaluation module and control module, the corresponding data map can be dynamically established according to the data map requirements of existing users, and new data maps can be intelligently generated in the future, which significantly improves the flexibility and scalability of data management. This means that water plants can adapt to changing needs more quickly and reduce the system development costs and time caused by changes in demand. Comprehensive attribute information is set for elements such as equipment and water quality parameters in the water plant, and chain or mesh splitting is allowed based on these attribute information to form unit maps. This enables managers to have a deeper understanding of the relationship between the elements, such as the correlation between water quality parameters at each stage, the mutual influence between equipment, etc., so as to make more accurate and scientific water resource management decisions based on these data. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] like Figure 1 As shown, the intelligent control system for waterworks drainage based on data graph includes an initial module, a demand adjustment module, a monitoring module, an evaluation module and a control module;

[0048] The initial module is used to set the corresponding data map according to user needs, marked as a reference map, and set comprehensive attribute data for each element in the water plant, and establish an element attribute library according to the attribute data corresponding to each element.

[0049] When a user has the need to apply this system, the platform will establish a benchmark map based on the user's management needs through existing data map technology, such as a process flow chart, equipment relationship diagram, water quality change diagram, etc.; in the process of establishing the benchmark map, the various attributes of each element can be simultaneously determined to form attribute data; elements refer to various water quality parameters, equipment, external environmental factors, user feedback, etc. in the water plant; attributes are their corresponding stages, correlations, properties, uses, etc.

[0050] In one embodiment, each element and attribute data is set by the platform manually or by existing methods.

[0051] In one embodiment, manually setting all elements and attribute data will consume a lot of energy, so the following method is proposed in this embodiment:

[0052] Summarize the historical data maps in the field of water plants to establish data, and mark them as map material data; identify the map material data, obtain various elements, and mark them as reserve elements; and set corresponding attribute data for each reserve element, and mark them as reserve attributes; establish a reserve library according to each reserve element and reserve attribute;

[0053] The reserve library will be updated based on the update of the atlas material data; and the platform will verify the data stored in the reserve library in combination with artificial intelligence or manual methods to ensure the accuracy of the stored reserve elements and reserve attributes;

[0054] A matching analysis model is established based on the reserve library. The matching analysis model is used to match the water plant information of the water plant according to the reserve library to determine whether the water plant has the corresponding reserve elements, that is, to select each reserve element for comparison and matching with the water plant information; when the match is successful, it is considered to meet the matching requirements, and the reserve attributes are adjusted according to the functional role and connection relationship of the reserve elements in the water plant, that is, the attributes that the elements in the water plant do not have are removed, because the reserve attributes are comprehensive attributes that the reserve elements may have, so they need to be eliminated accordingly; the corresponding element attributes are output; when the match fails, it is considered to not meet the matching requirements, and the output is none; each element can be determined later according to the element attributes; the expression of the matching analysis model is In the formula: (si, Y) is the input data, si represents the corresponding reserve element in the reserve, i=1, 2, ..., n, n is a positive integer; Y is the water plant information; the output data is the matching analysis result, and the matching analysis result is attribute data or none; specifically, matching judgment analysis is performed based on the existing technology combined with the above principles; such as establishing an intelligent model based on a neural network, combining the successfully trained intelligent model to perform matching judgment and reserve element screening.

[0055] The user's water plant information is obtained, and the water plant information is analyzed through a matching analysis model to obtain matching analysis results corresponding to each reserve element; and each element and element attribute is determined according to each matching analysis result.

[0056] The demand adjustment module is used to mark as a map demand when the user has corresponding new demands, such as newly added elements, changes in element attributes, new mapping demands based on corresponding monitoring demands, etc.

[0057] Identify graph requirements and perform graph processing based on graph requirements.

[0058] Methods for performing graph processing according to graph requirements include:

[0059] Identify the processing category corresponding to the graph requirement, which includes graph adjustment category and new graph category. Graph adjustment category mainly adjusts the base graph for adding new elements, changing element attributes, etc.; new graph category mainly adjusts the data graph that needs to be regenerated as a non-base graph.

[0060] When the processing category is the atlas adjustment category, the corresponding benchmark atlas is adjusted according to the atlas requirements;

[0061] When the processing category is a new graph category, a corresponding data graph is generated according to the graph requirements and element attribute library and marked as a requirement graph.

[0062] In one embodiment, the method for adjusting the corresponding reference atlas according to the atlas requirement includes:

[0063] Identify the elements that need to be adjusted according to the graph requirements, mark them as adjustment elements, match the corresponding reserve attributes from the reserve library according to the adjustment elements, and determine the attribute data of the adjustment elements according to the reserve attributes; the user can select this, or the attribute data can be determined in combination with the matching analysis model;

[0064] Determine the corresponding baseline map that needs to be adjusted according to each adjustment element; adjust the corresponding baseline map according to the attribute data of each adjustment element; that is, make corresponding adjustments to the baseline map according to the change of its attribute relationship and the attribute data of the newly added elements; specifically, a corresponding map adjustment model can be established based on a CNN network or a DNN network, and a corresponding training set can be established manually for training, the training set includes input data and output data, the input data is the baseline map, the adjustment elements and the element attributes; the output data is the adjusted baseline map; and the map adjustment model after successful training is used for analysis and adjustment.

[0065] In one embodiment, the corresponding reference atlas is adjusted according to the atlas requirements, and can also be adjusted by the user.

[0066] In one embodiment, a method for generating a demand graph according to graph requirements and an element attribute library includes:

[0067] Determine each element according to the map requirements, that is, each element to which the requirement needs to be applied; for example, for drainage monitoring requirements, corresponding monitoring equipment, drainage equipment, water quality parameters and other elements in the corresponding area are required; determine according to the function, purpose, method, etc. corresponding to the requirement; mark the corresponding element as a requirement element; match the attribute data corresponding to each requirement element from the element attribute library according to each requirement element;

[0068] According to the attribute data, the demand elements are associated with each other to obtain the corresponding element association diagram, that is, the demand elements are connected with corresponding lines according to the association relationship between them, such as sequence relationship, influence relationship, and association relationship. Different relationships are connected with different lines; the demand elements are distributed based on the minimum possible line crossing;

[0069] Generate a requirements graph based on the element association diagram.

[0070] A demand graph can be generated according to the element association graph based on various intelligent technologies. For example, a corresponding graph generation model is established based on a CNN network or a DNN network, and a corresponding training set is established manually for training. The training set includes input data and output data. The input data is the element association graph, and the output data is the demand graph. In actual applications, various graph modes can be preset for users to choose from, and the graph is generated according to the selected graph mode. The graph mode refers to the corresponding display style.

[0071] The monitoring module is used to perform real-time monitoring of the water plant and obtain corresponding monitoring data; identify each benchmark map and demand map, classify the monitoring data according to each benchmark map and demand map, and obtain the map monitoring data corresponding to each benchmark map and demand map. The map monitoring data is the corresponding data in the data map that needs to be monitored; and display each map monitoring data in real time.

[0072] The evaluation module is used to perform monitoring and evaluation based on the monitoring data of each graph, and obtain a benchmark evaluation method. The benchmark evaluation method is an evaluation method set according to an existing evaluation method, such as whether a water quality parameter exceeds the standard, whether the equipment temperature is abnormal, etc.; based on the benchmark evaluation method, the monitoring data is analyzed to obtain the corresponding benchmark evaluation results, and the benchmark evaluation results are displayed;

[0073] When the user has other monitoring and evaluation requirements, the monitoring and evaluation requirements are obtained. The monitoring and evaluation requirements are to evaluate certain specific monitoring targets according to user needs, which have great flexibility, such as a related monitoring and evaluation in the drainage stage, a related monitoring and evaluation in the disinfection stage, etc.; identify the attribute data corresponding to each element in the baseline map and the demand map; divide the corresponding baseline map and demand map according to each monitoring and evaluation requirement to form a unit map corresponding to each monitoring and evaluation requirement, that is, determine the elements related to it in the monitoring and evaluation requirement and the data map, and then extract them according to the correlation between the monitoring and evaluation requirement and each element to form a unit map corresponding to the monitoring and evaluation requirement;

[0074] Extract corresponding atlas monitoring data according to each unit atlas to obtain unit data corresponding to each unit atlas, where the unit data is the atlas monitoring data related to the unit atlas;

[0075] Evaluate the data of each unit, obtain the monitoring and evaluation results corresponding to each monitoring and evaluation demand, and display each monitoring and evaluation result to the user.

[0076] In one embodiment, the method for performing evaluation based on each unit data is: evaluating the unit data based on the prior art.

[0077] In one embodiment, the method of evaluating based on unit data includes:

[0078] Identify the attribute data of each element corresponding to the unit data, and obtain the associated material data of each element according to each attribute data, that is, the relevant historical monitoring data under the normal state with correlation, so as to understand the normal relationship between the corresponding data of each element; generate the correlation verification model according to each associated material data;

[0079] The unit data is evaluated through the association verification model to determine whether there is any abnormal association between the elements and obtain the monitoring evaluation results.

[0080] The method for generating an associated verification model according to each associated material data includes:

[0081] Generate an element association model according to the unit map, the element association model is a data model used to represent the association between elements; iteratively train the element association model through the associated material data, that is, determine the parameter groups corresponding to each element according to the associated material data, and train through each parameter group to understand the parameter groups that may appear in the normal state, and then when an abnormal situation occurs later, an associated abnormality will appear; corresponding iterative training can be achieved based on the existing technology;

[0082] The association verification model is established through the element association model after successful training. The expression of the association verification model is: In the formula: q is the input data, which is the unit data; the output data is the correlation check value GL(q), which is 1 or 0; the element correlation model is used to determine whether q is associated with abnormalities;

[0083] When the correlation check value is 1, the correlation between the elements is evaluated to be abnormal;

[0084] When the correlation check value is 0, the correlation between the elements is assessed to be normal.

[0085] The control module is used to perform control processing according to the benchmark assessment results and the monitoring assessment results, identify the corresponding benchmark assessment results and the monitoring assessment results in real time, and perform control processing according to the benchmark assessment results and the monitoring assessment results in accordance with a preset control method; the preset control method is set according to user needs.

[0086] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0087] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The intelligent control system for waterworks drainage based on data graph is characterized by: It includes initial module, demand adjustment module, monitoring module, evaluation module and control module; The initial module is used to set each benchmark map according to user needs and establish an element attribute library, and the element attribute library is used to store each element and the attribute data corresponding to each element; The demand adjustment module is used to perform graph processing according to the graph demand of the user, identify the graph demand of the user, and determine the processing category corresponding to the graph demand, wherein the processing category includes a graph adjustment category and a new graph category; When the processing category is the atlas adjustment category, the corresponding benchmark atlas is adjusted according to the atlas requirements; When the processing category is a new graph category, a corresponding data graph is generated according to the graph requirements and element attribute library and marked as a requirement graph; The monitoring module is used to monitor the water plant in real time and obtain monitoring data; identify each benchmark map and demand map, classify the monitoring data according to each benchmark map and demand map, obtain the map monitoring data corresponding to each benchmark map and demand map, and display each map monitoring data in real time; The evaluation module is used to perform monitoring evaluation according to each atlas monitoring data, obtain a benchmark evaluation method, analyze the monitoring data based on the benchmark evaluation method, obtain corresponding benchmark evaluation results, and display the benchmark evaluation results to the user; Obtain users' monitoring and evaluation requirements; Identify the attribute data corresponding to each element in the baseline map and the demand map; According to the monitoring and evaluation requirements, the corresponding benchmark map and demand map are divided to form a unit map corresponding to each monitoring and evaluation requirement; Extracting corresponding atlas monitoring data according to each of the unit atlases to obtain unit data corresponding to each of the unit atlases; Performing an evaluation according to each of the unit data, obtaining a monitoring and evaluation result corresponding to each of the monitoring and evaluation requirements, and presenting each of the monitoring and evaluation results to a user; The control module is used to perform control processing according to the benchmark assessment results and the monitoring assessment results, identify the corresponding benchmark assessment results and the monitoring assessment results in real time, and perform control processing according to the benchmark assessment results and the monitoring assessment results in a preset control manner.

2. The intelligent control system for waterworks drainage based on data atlas according to claim 1 is characterized in that: Methods for determining the data of each element and attribute in the element attribute library include: Acquire atlas material data in real time, identify each reserve element according to each atlas material data, and set corresponding reserve attributes for each reserve element; establish a reserve library according to each reserve element and reserve attribute; A matching analysis model is established based on the reserve library. The expression of the matching analysis model is: Where: (si, Y) is the input data, si represents the corresponding reserve element in the reserve, i = 1, 2, ..., n, n is a positive integer; Y is the water plant information; the output data is the matching analysis result, and the matching analysis result is attribute data or nothing; The user's water plant information is obtained, and the water plant information is analyzed by the matching analysis model to obtain matching analysis results corresponding to each reserve element; and each element and element attribute is determined according to each matching analysis result.

3. The intelligent control system for waterworks drainage based on data atlas according to claim 2 is characterized in that: The reserve library is updated based on the update of the atlas material data, and the reserve elements and reserve attributes stored in the reserve library are verified.

4. The intelligent control system for waterworks drainage based on data atlas according to claim 1 is characterized in that: Methods for adjusting the corresponding benchmark atlas according to atlas requirements include: Identify the adjustment element corresponding to the graph requirement, match the corresponding reserve attribute from a preset reserve library according to the adjustment element, and determine the attribute data of the adjustment element according to the reserve attribute; A reference map that needs to be adjusted is determined according to each adjustment element; and the reference map is adjusted according to the attribute data of each adjustment element.

5. The waterworks drainage intelligent control system based on data atlas according to claim 4 is characterized in that: The method of generating a corresponding data graph according to the graph requirements and the element attribute library includes: Determine each requirement element according to the graph requirement; match the attribute data corresponding to each requirement element from the element attribute library; The demand elements are associated with each other according to the attribute data to form an element association diagram, and a data map is generated based on the element association diagram.

6. The waterworks drainage intelligent control system based on data atlas according to claim 5 is characterized in that: Before generating a data map based on the element association diagram, the user can select a preset map mode.

7. The intelligent control system for waterworks drainage based on data atlas according to claim 1 is characterized in that: Methods for evaluating unit data include: Identify the attribute data of each element corresponding to the unit data, obtain the associated material data of each element according to each attribute data, and generate an associated verification model according to each associated material data; the associated verification model is used to determine whether the association between the elements is abnormal; The unit data is evaluated in real time through the associated verification model to obtain monitoring and evaluation results corresponding to the monitoring and evaluation requirements.

8. The intelligent control system for waterworks drainage based on data atlas according to claim 7 is characterized in that: The method for generating an associated verification model according to each associated material data includes: Generate an element association model according to the unit map, wherein the element association model is a data model for representing the association between elements; The element association model is iteratively trained through the association material data, and an association verification model is established through the element association model after successful training. The expression of the association verification model is: Where: q is the input data, which is the unit data; the output data is the associated check value GL(q), which is 1 or 0; When the correlation check value is 1, the correlation between the elements is evaluated to be abnormal; When the correlation check value is 0, the correlation between the elements is assessed to be normal.

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