Remote monitoring and intelligent scheduling combined water supply pipe network optimization method and system

Abstract

The embodiment of the invention relates to the technical field of water supply network optimization, and particularly discloses a remote monitoring and intelligent scheduling combined water supply network optimization method and system. According to the embodiment of the invention, periodic remote monitoring and data storage are carried out on a plurality of key monitoring nodes; performing data preprocessing and anomaly detection; when the abnormal condition exists, scheduling optimization analysis is carried out on the water supply network, and an optimization scheduling strategy is generated; and performing remote scheduling control according to the optimized scheduling strategy, and performing deviation monitoring and dynamic adjustment control on the plurality of key monitoring nodes. Periodic remote monitoring and anomaly detection can be carried out on a plurality of key monitoring nodes, when an abnormal condition exists, scheduling optimization and remote scheduling control are carried out on the water supply network, deviation monitoring and dynamic adjustment control are carried out, and therefore the abnormal condition existing in the water supply network is found in time, and the water supply network safety is improved. And the problems of water resource waste, substandard water quality, high water supply energy consumption, low user satisfaction and the like can be effectively avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of water supply network optimization, and in particular relates to a water supply network optimization method and system combining remote monitoring and intelligent scheduling. Background Art

[0002] The water supply network is a complex network system composed of a series of interconnected pipes, valves, pumping stations, water tanks, water towers and other facilities. It is used to transport purified drinking water from water sources (such as water plants) to user terminals (such as residential houses, commercial buildings, industrial facilities, etc.). It is an important part of the urban water supply system and shoulders the important task of ensuring water supply for urban residents' daily life, industrial production and public facilities.

[0003] In the existing technology, the level of intelligent monitoring and control of the water supply network is insufficient, and the scheduling and control of the water supply network mainly relies on experience. It is difficult to carry out complete and effective monitoring, collection and abnormal analysis of the water supply network. It is impossible to detect abnormal conditions in the water supply network in time, which easily leads to problems such as waste of water resources, substandard water quality, high water supply energy consumption, and low user satisfaction. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a water supply network optimization method and system that combines remote monitoring and intelligent scheduling, aiming to solve the problems raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A water supply network optimization method combining remote monitoring and intelligent scheduling, the method specifically comprising the following steps:

[0007] Identify multiple key monitoring nodes of the water supply network, conduct periodic remote monitoring and data storage for multiple key monitoring nodes according to the preset monitoring and collection cycle, and obtain monitoring data from multiple nodes;

[0008] Performing data preprocessing and anomaly detection on the monitoring data of the plurality of nodes to determine whether there is an abnormal condition;

[0009] When abnormal conditions occur, extract abnormal monitoring data, obtain multiple basic scheduling requirements, perform scheduling optimization analysis on the water supply network, and generate optimized scheduling strategies;

[0010] According to the optimized scheduling strategy, remote scheduling control is carried out, and deviation monitoring and dynamic adjustment control are carried out on multiple key monitoring nodes.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the determining of multiple key monitoring nodes of the water supply network, performing periodic remote monitoring and data storage on the multiple key monitoring nodes according to a preset monitoring and collection cycle, and obtaining the monitoring data of the multiple nodes specifically include the following steps:

[0012] Identify multiple key monitoring nodes in the water supply network;

[0013] According to a preset monitoring collection cycle, periodically generate and send node monitoring instructions to multiple key monitoring nodes;

[0014] Receiving monitoring feedback data from a plurality of key monitoring nodes;

[0015] Effectively extract and store multiple monitoring feedback data to obtain multiple node monitoring data.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the performing of data preprocessing and anomaly detection on the monitoring data of the plurality of nodes to determine whether an abnormal condition exists specifically includes the following steps:

[0017] performing data cleaning on the plurality of node monitoring data to generate a plurality of cleaned monitoring data;

[0018] performing standardization processing on the plurality of cleaning monitoring data to generate a plurality of standard monitoring data;

[0019] fusing a plurality of the standard monitoring data to generate fused monitoring data;

[0020] Anomaly detection is performed on the fused monitoring data to determine whether any abnormal conditions exist.

[0021] As a further limitation of the technical solution of the embodiment of the present invention, when an abnormal condition exists, extracting abnormal monitoring data, obtaining multiple basic scheduling requirements, performing scheduling optimization analysis on the water supply network, and generating an optimized scheduling strategy specifically include the following steps:

[0022] When an abnormal condition exists, extracting abnormal monitoring data from the fused monitoring data;

[0023] Obtain multiple basic scheduling requirements;

[0024] Analyzing the abnormal monitoring data to determine abnormal correction requirements;

[0025] Constructing a multi-objective optimization function according to the abnormality correction requirement and the plurality of basic scheduling requirements;

[0026] According to the multi-objective optimization function, a scheduling optimization analysis is performed on the water supply network to generate an optimized scheduling strategy.

[0027] As a further limitation of the technical solution of the embodiment of the present invention, the expression of the multi-objective optimization function is:

[0028]

[0029] Among them, f i (A) represents the function expression of the i-th basic scheduling requirement. There are n basic scheduling requirements in total. i is the basic weight corresponding to the i-th basic scheduling demand, f(B) is the function expression of the abnormal correction demand, ω b The exception weight corresponding to the exception correction requirement.

[0030] As a further limitation of the technical solution of the embodiment of the present invention, the remote scheduling control according to the optimized scheduling strategy and the deviation monitoring and dynamic adjustment control of the multiple key monitoring nodes specifically include the following steps:

[0031] Generate multiple optimization scheduling instructions according to the optimization scheduling strategy;

[0032] Sending a plurality of the optimization scheduling instructions to a plurality of pipe network control nodes;

[0033] Performing deviation monitoring on the plurality of key monitoring nodes to obtain a plurality of deviation monitoring data;

[0034] Analyzing the plurality of deviation monitoring data to determine whether there are abnormal deviations;

[0035] When there is an abnormal deviation, the abnormal deviation node is determined and dynamically adjusted and controlled.

[0036] A water supply network optimization system combining remote monitoring and intelligent scheduling, comprising a periodic remote monitoring unit, an abnormality detection and judgment unit, a scheduling optimization analysis unit, and a remote scheduling control unit, wherein:

[0037] The periodic remote monitoring unit is used to determine multiple key monitoring nodes of the water supply network, and perform periodic remote monitoring and data storage on multiple key monitoring nodes according to the preset monitoring and collection cycle to obtain monitoring data of multiple nodes;

[0038] An anomaly detection and judgment unit, used to perform data preprocessing and anomaly detection on the monitoring data of the plurality of nodes to determine whether there is an abnormal condition;

[0039] The scheduling optimization analysis unit is used to extract abnormal monitoring data when abnormal conditions occur, obtain multiple basic scheduling requirements, perform scheduling optimization analysis on the water supply network, and generate an optimized scheduling strategy;

[0040] The remote scheduling control unit is used to perform remote scheduling control according to the optimized scheduling strategy, and to perform deviation monitoring and dynamic adjustment control on multiple key monitoring nodes.

[0041] As a further limitation of the technical solution of the embodiment of the present invention, the abnormality detection and judgment unit specifically includes:

[0042] A data cleaning module, configured to clean the monitoring data of the plurality of nodes to generate a plurality of cleaned monitoring data;

[0043] A standardization processing module, configured to perform standardization processing on the plurality of cleaning monitoring data to generate a plurality of standard monitoring data;

[0044] A fusion processing module, configured to perform fusion processing on a plurality of the standard monitoring data to generate fused monitoring data;

[0045] The anomaly detection module is used to perform anomaly detection on the fused monitoring data to determine whether an abnormal condition exists.

[0046] As a further limitation of the technical solution of the embodiment of the present invention, the scheduling optimization analysis unit specifically includes:

[0047] A data extraction module, configured to extract abnormal monitoring data from the fused monitoring data when an abnormal condition exists;

[0048] Basic demand acquisition module, used to obtain multiple basic scheduling requirements;

[0049] A correction requirement determination module, configured to analyze the abnormal monitoring data and determine the abnormal correction requirement;

[0050] An objective optimization function construction module, configured to construct a multi-objective optimization function according to the abnormality correction requirement and the plurality of basic scheduling requirements;

[0051] The scheduling optimization analysis module is used to perform scheduling optimization analysis on the water supply network according to the multi-objective optimization function and generate an optimized scheduling strategy.

[0052] As a further limitation of the technical solution of the embodiment of the present invention, the remote scheduling control unit specifically includes:

[0053] An instruction generation module, configured to generate a plurality of optimization scheduling instructions according to the optimization scheduling strategy;

[0054] An instruction sending module, configured to send a plurality of the optimization scheduling instructions to a plurality of pipe network control nodes;

[0055] A deviation monitoring module is used to perform deviation monitoring on a plurality of the key monitoring nodes and obtain a plurality of deviation monitoring data;

[0056] An abnormal deviation judgment module is used to analyze the plurality of deviation monitoring data to determine whether there is an abnormal deviation;

[0057] The dynamic adjustment control module is used to determine the abnormal deviation node when there is an abnormal deviation, and dynamically adjust and control the abnormal deviation node.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The embodiment of the present invention periodically remotely monitors and stores data on multiple key monitoring nodes; performs data preprocessing and anomaly detection; performs scheduling optimization analysis on the water supply network when an abnormal condition exists, generates an optimized scheduling strategy; performs remote scheduling control according to the optimized scheduling strategy, and performs deviation monitoring and dynamic adjustment control on multiple key monitoring nodes. The system can periodically remotely monitor and detect anomalies on multiple key monitoring nodes, and when an abnormal condition exists, optimizes and remotely controls the water supply network, and performs deviation monitoring and dynamic adjustment control, thereby promptly discovering abnormal conditions in the water supply network and effectively avoiding problems such as water resource waste, substandard water quality, high water supply energy consumption, and low user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0061] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0062] Figure 2 A flowchart of periodic remote monitoring and data storage in the method provided by an embodiment of the present invention is shown.

[0063] Figure 3 A flow chart of data preprocessing and anomaly detection in the method provided by an embodiment of the present invention is shown.

[0064] Figure 4 A flow chart of scheduling optimization analysis in the method provided by an embodiment of the present invention is shown.

[0065] Figure 5 A flowchart of remote scheduling control in the method provided by an embodiment of the present invention is shown.

[0066] Figure 6 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0067] Figure 7The figure shows a structural block diagram of an abnormality detection and judgment unit in a system provided by an embodiment of the present invention.

[0068] Figure 8 The structure block diagram of the scheduling optimization analysis unit in the system provided by the embodiment of the present invention is shown.

[0069] Figure 9 The figure shows a structural block diagram of a remote scheduling control unit in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] It is understandable that in the existing technology, the level of intelligent monitoring and control of the water supply network is insufficient, and the scheduling and control of the water supply network mainly relies on experience. It is difficult to carry out complete and effective monitoring, collection and abnormal analysis of the water supply network, and it is impossible to detect abnormal conditions in the water supply network in time, which can easily lead to problems such as waste of water resources, substandard water quality, high water supply energy consumption, and low user satisfaction.

[0072] To solve the above problems, the embodiment of the present invention determines multiple key monitoring nodes of the water supply network, and performs periodic remote monitoring and data storage on the multiple key monitoring nodes according to a preset monitoring and collection cycle to obtain monitoring data of multiple nodes; performs data preprocessing and anomaly detection on the monitoring data of multiple nodes to determine whether there are abnormal conditions; when an abnormal condition exists, extracts the abnormal monitoring data, obtains multiple basic scheduling requirements, performs scheduling optimization analysis on the water supply network, and generates an optimized scheduling strategy; performs remote scheduling control according to the optimized scheduling strategy, and performs deviation monitoring and dynamic adjustment control on multiple key monitoring nodes. It is possible to perform periodic remote monitoring and anomaly detection on multiple key monitoring nodes, and when an abnormal condition exists, perform scheduling optimization and remote scheduling control on the water supply network, and perform deviation monitoring and dynamic adjustment control, so as to timely discover abnormal conditions in the water supply network, and effectively avoid problems such as waste of water resources, substandard water quality, high water supply energy consumption, and low user satisfaction.

[0073] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0074] Specifically, a water supply network optimization method combining remote monitoring and intelligent scheduling includes the following steps:

[0075] Step S101: determine multiple key monitoring nodes of the water supply network, perform periodic remote monitoring and data storage on the multiple key monitoring nodes according to a preset monitoring and collection cycle, and obtain monitoring data of multiple nodes.

[0076] In an embodiment of the present invention, by determining multiple key monitoring nodes in the water supply network, including water sources, pumping stations, pipeline branches, user ports, etc., node monitoring instructions are periodically generated according to a preset monitoring and collection cycle, and the node monitoring instructions are sent to multiple key monitoring nodes. Then, monitoring feedback data from multiple key monitoring nodes is received, and then the multiple monitoring feedback data are effectively extracted and data stored and processed to obtain multiple node monitoring data.

[0077] Specifically, Figure 2 A flowchart of periodic remote monitoring and data storage in the method provided by an embodiment of the present invention is shown.

[0078] Among them, in the preferred embodiment provided by the present invention, the multiple key monitoring nodes of the water supply network are determined, and the multiple key monitoring nodes are periodically remotely monitored and data stored according to a preset monitoring and collection cycle. The acquisition of the monitoring data of the multiple nodes specifically includes the following steps:

[0079] Step S1011, determining multiple key monitoring nodes of the water supply network;

[0080] Step S1012: periodically generate and send node monitoring instructions to the plurality of key monitoring nodes according to a preset monitoring collection cycle;

[0081] Step S1013, receiving monitoring feedback data from a plurality of key monitoring nodes;

[0082] Step S1014: effectively extract and store the plurality of monitoring feedback data to obtain a plurality of node monitoring data.

[0083] Furthermore, the water supply network optimization method combining remote monitoring and intelligent scheduling further includes the following steps:

[0084] Step S102 : performing data preprocessing and anomaly detection on the monitoring data of the plurality of nodes to determine whether an abnormal condition exists.

[0085] In an embodiment of the present invention, data cleaning is performed on multiple node monitoring data to remove noise data in the multiple node monitoring data to generate multiple cleaned monitoring data, and then the multiple cleaned monitoring data are standardized to convert the data into a unified scale and unit to generate multiple standard monitoring data. Then, according to factors such as time series and location, the multiple standard monitoring data are fused to generate fused monitoring data, and a statistical anomaly detection algorithm or a machine learning anomaly detection algorithm is used to perform anomaly detection on the fused monitoring data to determine whether there is an abnormal condition.

[0086] Specifically, Figure 3 A flow chart of data preprocessing and anomaly detection in the method provided by an embodiment of the present invention is shown.

[0087] In a preferred embodiment of the present invention, the data preprocessing and anomaly detection of the plurality of node monitoring data to determine whether an abnormal condition exists specifically includes the following steps:

[0088] Step S1021, performing data cleaning on the plurality of node monitoring data to generate a plurality of cleaned monitoring data;

[0089] Step S1022, performing standardization processing on the plurality of cleaning monitoring data to generate a plurality of standard monitoring data;

[0090] Step S1023, fusing the plurality of standard monitoring data to generate fused monitoring data;

[0091] Step S1024: perform anomaly detection on the fused monitoring data to determine whether there is any abnormality.

[0092] Furthermore, the water supply network optimization method combining remote monitoring and intelligent scheduling further includes the following steps:

[0093] Step S103: When an abnormal situation occurs, extract abnormal monitoring data, obtain multiple basic scheduling requirements, perform scheduling optimization analysis on the water supply network, and generate an optimized scheduling strategy.

[0094] In an embodiment of the present invention, when an abnormal condition exists, abnormal monitoring data is extracted from the fused monitoring data, and the abnormality type is determined. Then, according to the abnormality type, the abnormal monitoring data is corrected and planned to determine the abnormality correction requirements. At the same time, multiple basic scheduling requirements are obtained. Then, according to the abnormality correction requirements and the multiple basic scheduling requirements, a multi-objective optimization function is constructed. Then, based on the multi-objective optimization function, a scheduling optimization analysis is performed on the water supply network to generate an optimized scheduling strategy. Specifically, the expression of the multi-objective optimization function is:

[0095]

[0096] Among them, f i (A) represents the function expression of the i-th basic scheduling requirement. There are n basic scheduling requirements in total. i is the basic weight corresponding to the i-th basic scheduling demand, f(B) is the function expression of the abnormal correction demand, ω b The exception weight corresponding to the exception correction requirement.

[0097] It is understandable that the types of abnormalities may include pressure abnormalities, flow abnormalities, water quality abnormalities, etc.; multiple basic scheduling requirements may include water supply energy consumption, pipeline leakage rate, water quality compliance rate, etc.

[0098] Specifically, Figure 4 A flow chart of scheduling optimization analysis in the method provided by an embodiment of the present invention is shown.

[0099] Among them, in the preferred embodiment provided by the present invention, when an abnormal condition exists, extracting abnormal monitoring data, obtaining multiple basic scheduling requirements, performing scheduling optimization analysis on the water supply network, and generating an optimized scheduling strategy specifically include the following steps:

[0100] Step S1031, when an abnormal condition exists, extracting abnormal monitoring data from the fused monitoring data;

[0101] Step S1032, obtaining multiple basic scheduling requirements;

[0102] Step S1033, analyzing the abnormal monitoring data to determine the abnormality correction requirements;

[0103] Step S1034: constructing a multi-objective optimization function according to the abnormality correction requirement and the plurality of basic scheduling requirements;

[0104] Step S1035: Perform scheduling optimization analysis on the water supply network according to the multi-objective optimization function to generate an optimized scheduling strategy.

[0105] Furthermore, the water supply network optimization method combining remote monitoring and intelligent scheduling further includes the following steps:

[0106] Step S104: performing remote scheduling control according to the optimized scheduling strategy, and performing deviation monitoring and dynamic adjustment control on the plurality of key monitoring nodes.

[0107] In an embodiment of the present invention, according to the optimization scheduling strategy, multiple optimization scheduling instructions are generated, and the control relationship between the multiple optimization scheduling instructions and the multiple pipeline control nodes is determined. According to the multiple control relationships, the multiple optimization scheduling instructions are sent to the multiple corresponding pipeline control nodes to realize remote scheduling control, and deviation monitoring is performed on multiple key monitoring nodes, and multiple deviation monitoring data are obtained. According to the preset abnormal deviation threshold, the multiple deviation monitoring data are analyzed to determine whether there is an abnormal deviation. When there is an abnormal deviation, the abnormal deviation node is determined, and the abnormal deviation node is dynamically adjusted and controlled to realize continuous self-optimization and adjustment of abnormal scheduling processing, ensuring that the pipeline network always operates in the optimal state.

[0108] Specifically, Figure 5 A flowchart of remote scheduling control in the method provided by an embodiment of the present invention is shown.

[0109] Among them, in the preferred embodiment provided by the present invention, the remote scheduling control according to the optimized scheduling strategy and the deviation monitoring and dynamic adjustment control of the multiple key monitoring nodes specifically include the following steps:

[0110] Step S1041, generating multiple optimization scheduling instructions according to the optimization scheduling strategy;

[0111] Step S1042, sending the plurality of optimization scheduling instructions to a plurality of pipe network control nodes;

[0112] Step S1043: performing deviation monitoring on the plurality of key monitoring nodes to obtain a plurality of deviation monitoring data;

[0113] Step S1044, analyzing the plurality of deviation monitoring data to determine whether there is an abnormal deviation;

[0114] Step S1045 : When there is an abnormal deviation, determine the abnormal deviation node, and dynamically adjust and control the abnormal deviation node.

[0115] Further, Figure 6 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0116] Among them, in another preferred embodiment provided by the present invention, the water supply network optimization system combining remote monitoring and intelligent scheduling includes:

[0117] The periodic remote monitoring unit 101 is used to determine multiple key monitoring nodes of the water supply network, perform periodic remote monitoring and data storage on the multiple key monitoring nodes according to a preset monitoring collection cycle, and obtain monitoring data of multiple nodes.

[0118] In an embodiment of the present invention, the periodic remote monitoring unit 101 determines multiple key monitoring nodes in the water supply network, including water sources, pumping stations, pipeline branches, user ports, etc., and periodically generates node monitoring instructions according to a preset monitoring and collection cycle. The node monitoring instructions are sent to multiple key monitoring nodes, and then the monitoring feedback data of the multiple key monitoring nodes are received. After that, the multiple monitoring feedback data are effectively extracted and stored and processed to obtain multiple node monitoring data.

[0119] The abnormality detection and judgment unit 102 is used to perform data preprocessing and abnormality detection on the monitoring data of the plurality of nodes to determine whether an abnormal condition exists.

[0120] In an embodiment of the present invention, the anomaly detection judgment unit 102 performs data cleaning on multiple node monitoring data, removes noise data in the multiple node monitoring data, generates multiple cleaned monitoring data, and then standardizes the multiple cleaned monitoring data to convert the data into a unified scale and unit to generate multiple standard monitoring data. Then, according to factors such as time series and location, the multiple standard monitoring data are fused to generate fused monitoring data, and a statistical anomaly detection algorithm or a machine learning anomaly detection algorithm is used to perform anomaly detection on the fused monitoring data to determine whether there is an abnormal condition.

[0121] Specifically, Figure 7 FIG. 1 is a structural block diagram of the abnormality detection and judgment unit 102 in the system provided by an embodiment of the present invention.

[0122] In a preferred embodiment of the present invention, the abnormality detection and judgment unit 102 specifically includes:

[0123] A data cleaning module 1021 is configured to clean the plurality of node monitoring data to generate a plurality of cleaned monitoring data;

[0124] A standardization processing module 1022 is used to perform standardization processing on the plurality of cleaning monitoring data to generate a plurality of standard monitoring data;

[0125] A fusion processing module 1023 is used to perform fusion processing on the plurality of standard monitoring data to generate fused monitoring data;

[0126] The anomaly detection module 1024 is used to perform anomaly detection on the fused monitoring data to determine whether an abnormal condition exists.

[0127] Furthermore, the water supply network optimization system combining remote monitoring and intelligent scheduling also includes:

[0128] The scheduling optimization analysis unit 103 is used to extract abnormal monitoring data when an abnormal situation occurs, obtain multiple basic scheduling requirements, perform scheduling optimization analysis on the water supply network, and generate an optimized scheduling strategy.

[0129] In an embodiment of the present invention, when an abnormal condition exists, the scheduling optimization analysis unit 103 extracts abnormal monitoring data from the fused monitoring data and determines the abnormality type. Then, according to the abnormality type, the abnormal monitoring data is corrected and planned to determine the abnormality correction requirements. At the same time, multiple basic scheduling requirements are obtained. Then, according to the abnormality correction requirements and the multiple basic scheduling requirements, a multi-objective optimization function is constructed. Then, based on the multi-objective optimization function, the water supply network is scheduled and optimized to generate an optimized scheduling strategy. Specifically, the expression of the multi-objective optimization function is:

[0130]

[0131] Among them, f i (A) represents the function expression of the i-th basic scheduling requirement. There are n basic scheduling requirements in total. i is the basic weight corresponding to the i-th basic scheduling demand, f(B) is the function expression of the abnormal correction demand, ω b The exception weight corresponding to the exception correction requirement.

[0132] Specifically, Figure 8 It shows a structural block diagram of the scheduling optimization analysis unit 103 in the system provided by an embodiment of the present invention.

[0133] In a preferred embodiment of the present invention, the scheduling optimization analysis unit 103 specifically includes:

[0134] The data extraction module 1031 is used to extract abnormal monitoring data from the fused monitoring data when an abnormal condition exists;

[0135] A basic demand acquisition module 1032 is used to acquire multiple basic scheduling requirements;

[0136] A correction requirement determination module 1033 is configured to analyze the abnormal monitoring data and determine the abnormality correction requirement;

[0137] An objective optimization function construction module 1034 is configured to construct a multi-objective optimization function according to the abnormality correction requirement and the plurality of basic scheduling requirements;

[0138] The scheduling optimization analysis module 1035 is used to perform scheduling optimization analysis on the water supply network according to the multi-objective optimization function and generate an optimized scheduling strategy.

[0139] Furthermore, the water supply network optimization system combining remote monitoring and intelligent scheduling also includes:

[0140] The remote scheduling control unit 104 is used to perform remote scheduling control according to the optimized scheduling strategy, and to perform deviation monitoring and dynamic adjustment control on the multiple key monitoring nodes.

[0141] In an embodiment of the present invention, the remote scheduling control unit 104 generates multiple optimization scheduling instructions according to the optimization scheduling strategy, and determines the control relationship between the multiple optimization scheduling instructions and the multiple pipeline control nodes. According to the multiple control relationships, the multiple optimization scheduling instructions are sent to the multiple corresponding pipeline control nodes to realize remote scheduling control, and deviation monitoring is performed on multiple key monitoring nodes, and multiple deviation monitoring data are obtained. According to the preset abnormal deviation threshold, the multiple deviation monitoring data are analyzed to determine whether there is an abnormal deviation, and then when there is an abnormal deviation, the abnormal deviation node is determined, and the abnormal deviation node is dynamically adjusted and controlled to realize continuous self-optimization and adjustment of abnormal scheduling processing, ensuring that the pipeline network always operates in the optimal state.

[0142] Specifically, Figure 9 FIG. 1 shows a structural block diagram of the remote scheduling control unit 104 in the system provided by an embodiment of the present invention.

[0143] Among them, in the preferred embodiment provided by the present invention, the remote scheduling control unit 104 specifically includes:

[0144] An instruction generation module 1041 is used to generate a plurality of optimization scheduling instructions according to the optimization scheduling strategy;

[0145] An instruction sending module 1042 is configured to send a plurality of the optimization scheduling instructions to a plurality of pipe network control nodes;

[0146] Deviation monitoring module 1043, configured to perform deviation monitoring on a plurality of key monitoring nodes and obtain a plurality of deviation monitoring data;

[0147] The abnormal deviation judgment module 1044 is used to analyze the plurality of deviation monitoring data to determine whether there is an abnormal deviation;

[0148] The dynamic adjustment control module 1045 is used to determine the abnormal deviation node when there is an abnormal deviation, and perform dynamic adjustment control on the abnormal deviation node.

[0149] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0150] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0151] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A water supply network optimization method combining remote monitoring and intelligent scheduling, characterized in that: The method specifically comprises the following steps: Identify multiple key monitoring nodes of the water supply network, conduct periodic remote monitoring and data storage on multiple key monitoring nodes according to the preset monitoring and collection cycle, and obtain monitoring data from multiple nodes; Performing data preprocessing and anomaly detection on the monitoring data of the plurality of nodes to determine whether there is an abnormal condition; When abnormal conditions occur, extract abnormal monitoring data, obtain multiple basic scheduling requirements, perform scheduling optimization analysis on the water supply network, and generate optimized scheduling strategies; According to the optimized scheduling strategy, remote scheduling control is carried out, and deviation monitoring and dynamic adjustment control are carried out on multiple key monitoring nodes.

2. The water supply network optimization method combining remote monitoring and intelligent scheduling according to claim 1 is characterized in that: The method of determining multiple key monitoring nodes of the water supply network, performing periodic remote monitoring and data storage on the multiple key monitoring nodes according to a preset monitoring and collection cycle, and obtaining monitoring data of the multiple nodes specifically includes the following steps: Identify multiple key monitoring nodes in the water supply network; According to a preset monitoring collection cycle, periodically generate and send node monitoring instructions to multiple key monitoring nodes; Receiving monitoring feedback data from a plurality of key monitoring nodes; Effectively extract and store multiple monitoring feedback data to obtain multiple node monitoring data.

3. The water supply network optimization method combining remote monitoring and intelligent scheduling according to claim 1 is characterized in that: The data preprocessing and anomaly detection of the monitoring data of the plurality of nodes to determine whether an abnormal condition exists specifically includes the following steps: performing data cleaning on the plurality of node monitoring data to generate a plurality of cleaned monitoring data; performing standardization processing on the plurality of cleaning monitoring data to generate a plurality of standard monitoring data; fusing a plurality of the standard monitoring data to generate fused monitoring data; Anomaly detection is performed on the fused monitoring data to determine whether any abnormal conditions exist.

4. The water supply network optimization method combining remote monitoring and intelligent scheduling according to claim 3 is characterized in that: When an abnormal situation occurs, extracting abnormal monitoring data, obtaining multiple basic scheduling requirements, performing scheduling optimization analysis on the water supply network, and generating an optimized scheduling strategy specifically include the following steps: When an abnormal condition exists, extracting abnormal monitoring data from the fused monitoring data; Obtain multiple basic scheduling requirements; Analyzing the abnormal monitoring data to determine abnormal correction requirements; Constructing a multi-objective optimization function according to the abnormality correction requirement and the plurality of basic scheduling requirements; According to the multi-objective optimization function, a scheduling optimization analysis is performed on the water supply network to generate an optimized scheduling strategy.

5. The water supply network optimization method combining remote monitoring and intelligent scheduling according to claim 4 is characterized in that: The expression of the multi-objective optimization function is: Among them, f i (A) represents the function expression of the i-th basic scheduling requirement. There are n basic scheduling requirements in total. ω i is the basic weight corresponding to the i-th basic scheduling demand, f(B) is the function expression of the abnormal correction demand, ω b The exception weight corresponding to the exception correction requirement.

6. The water supply network optimization method combining remote monitoring and intelligent scheduling according to claim 1 is characterized in that: The remote scheduling control according to the optimized scheduling strategy and the deviation monitoring and dynamic adjustment control of the multiple key monitoring nodes specifically include the following steps: Generate multiple optimization scheduling instructions according to the optimization scheduling strategy; Sending a plurality of the optimization scheduling instructions to a plurality of pipe network control nodes; Performing deviation monitoring on the plurality of key monitoring nodes to obtain a plurality of deviation monitoring data; Analyzing the plurality of deviation monitoring data to determine whether there are abnormal deviations; When there is an abnormal deviation, the abnormal deviation node is determined and dynamically adjusted and controlled.

7. The water supply network optimization system combining remote monitoring and intelligent scheduling is characterized by: The system includes a periodic remote monitoring unit, an abnormality detection and judgment unit, a scheduling optimization analysis unit and a remote scheduling control unit, wherein: The periodic remote monitoring unit is used to determine multiple key monitoring nodes of the water supply network, and perform periodic remote monitoring and data storage on multiple key monitoring nodes according to the preset monitoring and collection cycle to obtain monitoring data of multiple nodes; An anomaly detection and judgment unit, used to perform data preprocessing and anomaly detection on the monitoring data of the plurality of nodes to determine whether there is an abnormal condition; The scheduling optimization analysis unit is used to extract abnormal monitoring data when abnormal conditions occur, obtain multiple basic scheduling requirements, perform scheduling optimization analysis on the water supply network, and generate an optimized scheduling strategy; The remote scheduling control unit is used to perform remote scheduling control according to the optimized scheduling strategy, and to perform deviation monitoring and dynamic adjustment control on multiple key monitoring nodes.

8. The water supply network optimization system combining remote monitoring and intelligent scheduling according to claim 7 is characterized in that: The abnormality detection and judgment unit specifically includes: A data cleaning module, configured to clean the monitoring data of the plurality of nodes to generate a plurality of cleaned monitoring data; A standardization processing module, configured to perform standardization processing on the plurality of cleaning monitoring data to generate a plurality of standard monitoring data; A fusion processing module, configured to perform fusion processing on a plurality of the standard monitoring data to generate fused monitoring data; The anomaly detection module is used to perform anomaly detection on the fused monitoring data to determine whether an abnormal condition exists.

9. The water supply network optimization system combining remote monitoring and intelligent scheduling according to claim 8 is characterized in that: The scheduling optimization analysis unit specifically includes: A data extraction module, configured to extract abnormal monitoring data from the fused monitoring data when an abnormal condition exists; Basic demand acquisition module, used to obtain multiple basic scheduling requirements; A correction requirement determination module, configured to analyze the abnormal monitoring data and determine the abnormal correction requirement; An objective optimization function construction module, configured to construct a multi-objective optimization function according to the abnormality correction requirement and the plurality of basic scheduling requirements; The scheduling optimization analysis module is used to perform scheduling optimization analysis on the water supply network according to the multi-objective optimization function and generate an optimized scheduling strategy.

10. The water supply network optimization system combining remote monitoring and intelligent scheduling according to claim 7 is characterized in that: The remote dispatch control unit specifically includes: An instruction generation module, configured to generate a plurality of optimization scheduling instructions according to the optimization scheduling strategy; An instruction sending module, configured to send a plurality of the optimization scheduling instructions to a plurality of pipe network control nodes; A deviation monitoring module is used to perform deviation monitoring on a plurality of the key monitoring nodes and obtain a plurality of deviation monitoring data; An abnormal deviation judgment module is used to analyze the plurality of deviation monitoring data to determine whether there is an abnormal deviation; The dynamic adjustment control module is used to determine the abnormal deviation node when there is an abnormal deviation, and dynamically adjust and control the abnormal deviation node.

Images