Smart water monitoring method and system based on digital twin

Abstract

The present invention relates to the field of data analysis technology, and specifically to a smart water management monitoring method and system based on digital twins, including: obtaining the degree of abnormality of various water management data at each moment based on historical data of various water management data, and obtaining the degree of correlation between various water management data and the abnormal independence of various water management data at each moment; obtaining the derived data of each device and the causal data of various water management data, directly affecting devices and indirectly affecting devices, combining the degree of abnormality of water management data, abnormal independence and the degree of correlation between water management data, obtaining the fault factors of each directly affecting device and the fault factors of each indirectly affecting device of water management data at each moment, thereby obtaining the faulty device and marking the faulty device in the digital twin model of the water management system. The present invention accurately monitors and locates faults in the water management system by analyzing the correlation between different water management data in the water management system.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a smart water management monitoring method and system based on digital twins. Background Art

[0002] Traditional water monitoring methods mainly rely on dispersed sensors, manual inspections and data aggregation to process and monitor water data. Traditional water monitoring systems often collect data at a fixed sampling frequency, and there is a certain delay in data transmission and processing, which makes it impossible to timely reflect the dynamic changes of the water system in emergency situations; at the same time, traditional detection systems mainly focus on recording and displaying the current situation, and it is difficult to achieve homogeneous policies, fault prediction and early warning responses based on real-time data, and it is difficult to meet the requirements of urban water environment safety and water resources optimization and scheduling; that is, traditional methods of monitoring and locating faults in water systems do not take into account the interaction between various process links in the water system, and cannot accurately monitor and locate faults in the water system. Summary of the Invention

[0003] The present invention provides a smart water monitoring method and system based on digital twins to solve the existing problem: the traditional method of monitoring and locating faults in the water system does not take into account the interaction between the various process links in the water system and cannot accurately monitor and locate faults in the water system.

[0004] The digital twin-based smart water monitoring method and system of the present invention adopts the following technical solutions:

[0005] One embodiment of the present invention provides a smart water management monitoring method based on digital twins, which includes the following steps:

[0006] Obtain various water data from various modules within the water system;

[0007] Based on the historical data of various water service data in each module, the abnormality degree of various water service data in each module at each moment is obtained, and the correlation degree between various water service data in each module is obtained; based on the abnormality degree of various water service data in each module at each moment and the correlation degree between various water service data in each module, the abnormal independence of various water service data in each module at each moment is obtained;

[0008] Obtain the derived data of each device in all modules, and the cause data, directly influencing devices, and indirectly influencing devices of various water service data in all modules. Combined with the abnormal independence of various water service data in each module at each moment, the abnormal degree of various water service data in each module at each moment, and the degree of correlation between various water service data in each module, obtain the failure factors of each directly influencing device and each indirectly influencing device of various water service data in each module at each moment;

[0009] According to the failure factors of each directly affecting device and each indirectly affecting device of various water service data in each module at each moment, the faulty devices in each module are obtained and marked in the digital twin model of the water service system.

[0010] Preferably, the specific method of obtaining the abnormality degree of various water service data in each module at each moment includes:

[0011] For the The next moment In the module Water service data, obtain the first All the modules in Water service data, the first in history All the modules in Water service data and The next moment In the module The water service data is used as the data set, and the LOF outlier detection algorithm is used to obtain the first The moment In the module The LOF value of the water service data is used as the first The moment In the module The abnormality of water service data.

[0012] Preferably, the specific method of obtaining the correlation between various water service data in various modules includes:

[0013] Obtain the abnormality of all water service data in all modules at all historical moments. In the module Water service data and In the module All water service data; all historical moments are downloaded In the module The abnormality of this water service data is the highest at any time in history. In the module The absolute value of the Pearson correlation coefficient between the abnormality of the water service data is used as the first In the module Water service data and In the module The degree of correlation between various water service data.

[0014] Preferably, the specific method of obtaining the abnormal independence of various water service data in each module at each moment includes:

[0015] Preset an abnormality threshold and impact timeframe , for the The next moment In the module Water service data, if The next moment In the module The abnormality of water service data is greater than or equal to , then the moments ago All moments within the minute are recorded as The impact moment of the moment, and the All water service data in the module before the module are recorded as In the module Genesis data of water affairs data;

[0016] According to In the module The degree of correlation between the water affairs data and its cause data, combined with the The impact of the moment In the module The abnormal degree of water service data and its cause data is obtained The next moment In the module The specific calculation formula for the abnormal independence of water service data is:

[0017]

[0018] Where, Indicates the The next moment In the module The unusual independence of water service data; Indicates the The next moment In the module The degree of abnormality of water service data; Indicates the In the module The number of types of data that cause water affairs data; Indicates the The number of moments of influence of each moment; Indicates the In the module Water service data and its The degree of correlation between the causal data; Indicates the The moment The impact moment In the module The first The degree of abnormality of the causal data; represents the absolute value function; represents the linear normalization function.

[0019] Preferably, the specific method of obtaining the derived data of each device in all modules, the cause data of various water service data in all modules, the directly affected devices and the indirectly affected devices includes:

[0020] Obtain water data affected by each device in all modules of the water system;

[0021] For the In the module device, the In the module The water service data affected by the equipment is recorded as In the module Derived data for each device;

[0022] For the In the module Water service data, The module affects The equipment that collects water service data is recorded as In the module The direct impact of water data on equipment; and In the module The direct impact of the data on the cause of water affairs data is recorded as the first In the module This equipment has an indirect impact on water service data.

[0023] Preferably, the specific method of obtaining the failure factors of each directly affecting device and the failure factors of each indirectly affecting device of various water service data in each module at each moment includes:

[0024] For any water service data in any module at any time, if the abnormality of the water service data in the module at the time is greater than or equal to , then according to the abnormality degree of the water service data and its cause data in the module at the time, the correlation degree between the water service data and its cause data in the module at the time, and the abnormal independence of the water service data in the module at the time, obtain the fault factors of each device directly affecting the water service data in the module at the time;

[0025] According to the abnormality degree of the derived data of the indirectly influencing equipment of the water data in the module at the moment, the correlation degree between the derived data of the indirectly influencing equipment and the water data in the module at the moment, combined with the abnormal independence of the water data in the module at the moment, the failure factors of each indirectly influencing equipment of the water data in the module at the moment are obtained.

[0026] Preferably, the specific calculation formula for obtaining the failure factors of each directly affecting equipment in the water service data in the module at the moment is:

[0027]

[0028] Where, Indicates the first water service data in the module at the time A failure factor that directly affects the equipment; Indicates the abnormal independence of the water service data in the module at the time; Indicates the first The amount of derived data that directly affects the device; Indicates the first water service data in the module at the time Directly affects the equipment the degree of abnormality of the derived data; Indicates the number of types of causal data of the water affairs data in the module; Indicates the number of impact moments of the said moment; Indicates the In the module Water service data and its The degree of correlation between the causal data; Indicates the time The first water service data in the module at the impact moment The degree of abnormality of the causal data; represents the linear normalization function.

[0029] Preferably, the failure factors of each indirect influencing device of the water service data in the module at the moment are obtained, and the specific calculation formula included is:

[0030]

[0031] Where, Indicates the first water service data in the module at the time Failure factors that indirectly affect equipment; Indicates the abnormal independence of the water service data in the module at the time; Indicates the first water service data in the module at the time The number of derived data that indirectly affects the device; Indicates the number of impact moments of the said moment; Indicates the time The water affairs data in the module at the time of impact and its The first one that indirectly affects the equipment The degree of correlation between the derived data; Indicates the time The first water service data in the module at the impact moment The first one that indirectly affects the equipment the degree of abnormality of the derived data; represents the linear normalization function.

[0032] Preferably, the specific method of obtaining the faulty device in each module includes:

[0033] Preset a failure factor threshold , when the abnormality of any water service data in any module at any time is greater than or equal to , obtain the failure factors of each direct device and indirect device of the water service data in the module at the time, and connect the device with the largest failure factor and the device with a failure factor greater than or equal to The device is marked as a faulty device.

[0034] Another embodiment of the present invention provides a smart water management monitoring system based on digital twins, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of the above-mentioned smart water management monitoring method based on digital twins.

[0035] The beneficial effects of the technical solution of the present invention are as follows: the present application obtains the degree of abnormality of various water service data at each moment through the historical data of various water service data, and obtains the degree of correlation between various water service data and the abnormal independence of various water service data at each moment. Since when a certain water service data in a module of the water service system is abnormal, it will cause abnormalities in other water service data in all modules, which means that the degree of correlation between various water service data in various modules of the water service system is stronger. After obtaining the degree of correlation between water service data, it is possible to evaluate whether the deviation of water service data in a module from the normal value is affected by water service data in other modules or by equipment in the module itself, thereby obtaining the abnormal independence of water service data.

[0036] Obtain the derived data of each device and the cause data of various water data, directly affecting devices and indirectly affecting devices. When a device fails, all derived data of the faulty device will be affected. The degree of abnormality, independence of abnormality and the degree of correlation between water data can be further combined to obtain the failure factors of each directly affecting device and each indirectly affecting device of the water data at each moment, so as to obtain the faulty device and mark the faulty device in the digital twin model of the water system. This application provides the accuracy of locating faulty devices in the water system by analyzing the correlation between different water data in the water system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a flowchart of the steps of the digital twin-based smart water monitoring method of the present invention. DETAILED DESCRIPTION

[0039] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of the digital twin-based smart water monitoring method and system proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0041] The specific scheme of the smart water monitoring method and system based on digital twin provided by the present invention is described in detail below with reference to the accompanying drawings.

[0042] See also Figure 1 , which shows a flowchart of a smart water management monitoring method based on digital twins provided by one embodiment of the present invention, the method comprising the following steps:

[0043] Step S001: Acquire various water service data in various modules within the water service system.

[0044] It should be noted that the water system as a complete water treatment system includes but is not limited to a water intake module, a coagulation module, a sedimentation module, a filtration module, a disinfection module, and a water delivery module; any failure in any module will have an impact on subsequent modules, causing abnormalities in the water data in the subsequent modules. Therefore, it is not possible to simply determine whether the water data at each location is abnormal to accurately obtain the location of the fault in the water system. Therefore, the present invention proposes a smart water monitoring method based on digital twins.

[0045] It's important to note that the digital twin model of a water system, through real-time collection and integration of multidimensional data, can comprehensively and accurately reflect the physical state of the water system. This model can simulate water flow, water quality changes, equipment operating status, and other aspects of the system, reflecting the system's current state and operational trends in real time. Deep learning analysis and traceability of real-time and historical data can accurately pinpoint fault locations in the water system, thereby promoting digital, intelligent, and efficient monitoring and management of the water system. Therefore, it's essential to first collect water data from each module within the water system.

[0046] Specifically, various water data sensors are installed in each module in the water system to collect various water data in each module at each moment.

[0047] It should be noted that, in this embodiment, various water service data are collected at a time interval of 0.1 seconds, and the various water service data sensors include but are not limited to: flow data, water pressure data, pH value, turbidity and other data.

[0048] At this point, various water data in each module within the water system are obtained.

[0049] Step S002: Based on the historical data of various water affairs data in each module, obtain the abnormality degree of various water affairs data in each module at each moment, and obtain the correlation degree between various water affairs data in each module; based on the abnormality degree of various water affairs data in each module at each moment and the correlation degree between various water affairs data in each module, obtain the abnormal independence of various water affairs data in each module at each moment.

[0050] It should be noted that after obtaining the water data in each module within the water system, the water data in each module within the water system can be input into the digital twin model of the water system to monitor whether there is a fault in the water system and accurately locate the location of the fault in the water system; and this implementation is a smart water monitoring method based on digital twins, specifically a method of inputting the water data in each module within the water system into the digital twin model of the water system, and then monitoring and locating the fault location of the water system through the digital twin model of the water system.

[0051] It needs to be further explained that the water system is a complete water treatment system, and its various modules together constitute a complete water treatment process: from the water intake module to the coagulation module, the coagulation module to the sedimentation module, the sedimentation module to the filtration module, the filtration module to the disinfection module, and the disinfection module to the water supply module. When any fault occurs in any module, it will cause some water data in the subsequent modules of this module to change. For example: insufficient dosage in the coagulation module in the water system will cause the turbidity of the water body in the subsequent sedimentation module to be too high, and then the pressure difference of the filter tank in the subsequent filtration module will be too large. At this time, the filtration module has not failed, but the filtration module has collected abnormal water data. Therefore, simply based on the location of the sensor that collects the abnormal data, the fault location cannot be accurately located. It is necessary to analyze the degree of correlation between various water data in each module to accurately locate the fault location in the water system.

[0052] Specifically, for the The next moment In the module Water service data, obtain the first All the modules in Water service data, the first in history All the modules in Water service data and The next moment In the module The water service data is used as the data set, and the LOF outlier detection algorithm is used to obtain the first The moment In the module The LOF value of the water service data is used as the first The moment In the module The abnormality degree of the water service data is determined. Since the LOF outlier detection algorithm is a well-known prior art, it will not be described in detail in this embodiment.

[0053] Similarly, obtain the abnormality of all water service data in all modules at all historical moments. In the module Water service data and In the module Water service data (described and Can be equal, the and can be equal); all historical moments under In the module The abnormality of this water service data is the highest at any time in history. In the module The absolute value of the Pearson correlation coefficient between the abnormality of the water service data is used as the first In the module Water service data and In the module The degree of correlation between the various water service data is not described in detail in this embodiment because the Pearson correlation coefficient is a well-known prior art.

[0054] It should be noted that when a certain water service data in a certain module in the water service system is abnormal, it will cause abnormalities in other water service data in all modules, which means that the correlation between various water service data in each module in the water service system is stronger. Therefore, this embodiment uses the Pearson correlation coefficient of the abnormality level of various water service data in each module as the correlation degree between various water service data in each module.

[0055] It should be further explained that the water system is composed of multiple modules, each of which is interconnected through various devices. After obtaining the degree of correlation between various water data in each module, when the water data in the module deviates from the normal value, the degree of correlation between various water data in each module can be used to evaluate whether the deviation of the water data in the module from the normal value is affected by the water data in other modules or the equipment in this module.

[0056] Preferably, in a specific embodiment of the present invention, a threshold value of abnormality is preset. and impact timeframe , and The specific value of can be set according to the actual situation. This embodiment does not make a hard requirement. 、 Take the following as an example, for The next moment In the module Water service data, if The next moment In the module The abnormality of water service data is greater than or equal to , then the moments ago All moments within the minute are recorded as The impact moment of the moment, and the All water service data in the module before the module are recorded as In the module Genesis data of water affairs data;

[0057] According to In the module The degree of correlation between the water affairs data and its cause data, combined with the The impact of the moment In the module The abnormal degree of water service data and its cause data is obtained The next moment In the module The specific calculation formula for the abnormal independence of water service data is:

[0058]

[0059] Where, Indicates the The next moment In the module The unusual independence of water service data; Indicates the The next moment In the module The degree of abnormality of water service data; Indicates the In the module The number of types of data that cause water affairs data; Indicates the The number of moments of influence of each moment; Indicates the In the module Water service data and its The degree of correlation between the causal data; Indicates the The moment The impact moment In the module The first The degree of abnormality of the causal data; represents the absolute value function; Represents a linear normalization function, whose specific normalization range is all abnormal data .

[0060] It should be noted that the abnormal independence of water service data indicates whether the abnormality of the water service data is caused by the influence of other water service data. The greater the abnormal independence of water service data, the less the abnormality of water service data is caused by the influence of other water service data. The cause data indicates whether the abnormality of the water service data is affected by other water service data. In the module The data that influences the water affairs data; the causal data and the In the module The degree of correlation between the water affairs data is the degree to which the causal data can affect the In the module and because it takes a certain amount of time for the causal data to affect the water data in subsequent modules, this embodiment presets an impact time range, and evaluates the abnormal independence of the water data based on the degree of abnormality of the causal data within the impact time range.

[0061] It should be further explained that when The impact of the moment In the module When the water service data deviates from the normal data, it will have a greater impact on the In the module The cause data of this water affairs data is the same as the The next moment In the module The smaller the difference in abnormal degree of the water service data, the more likely it is the first The water affairs data in the module before the module caused the In the module The water service data is abnormal.

[0062] At this point, the abnormal independence of various water service data in each module at each time is obtained.

[0063] Step S003: Obtain the derived data of each device in all modules, and the cause data, directly affecting devices and indirectly affecting devices of various water data in all modules. Combined with the abnormal independence of various water data in each module at each moment, the abnormal degree of various water data in each module at each moment and the degree of correlation between various water data in each module, obtain the failure factors of each directly affecting device and the failure factors of each indirectly affecting device of various water data in each module at each moment.

[0064] It should be noted that when a certain water service data in a certain module at a certain moment is abnormal data, it means that a fault has occurred in the water service system. The smaller the abnormal independence of the water service data, the more likely it is that the cause of the abnormal water service data is affected by the water service data in other modules, that is, the fault in the water service system is more likely to be located in other modules. At the same time, since there are various devices in each module in the water service system, and each device can affect a variety of water service data, for example: the dosing pump in the coagulation module will affect the turbidity and pH value of the water body in the coagulation module; therefore, it is necessary to accurately locate the fault location in the water service system based on the abnormal independence of the abnormal water service data and the water service data affected by each device in each module.

[0065] Specifically, obtain the water data affected by each device in all modules of the water system;

[0066] For the In the module device, the In the module The water service data affected by the equipment is recorded as In the module Derived data for each device;

[0067] For the In the module Water service data, The module affects The equipment that collects water service data is recorded as In the module The direct impact of water data on equipment; and In the module The direct impact of the data on the cause of water affairs data is recorded as the first In the module This equipment has an indirect impact on water service data.

[0068] It's important to note that devices that directly impact water data are those that directly affect that data, while devices that indirectly impact water data are those that indirectly affect that data through the water system's process flow. When a device fails, all derived data from the failed device is affected, allowing this information to be used as a basis for locating the fault within the water system.

[0069] Preferably, in a specific embodiment of the present invention, for any water service data in any module at any time, if the abnormality of the water service data in the module at the time is greater than or equal to , then based on the abnormality degree of the water service data and its cause data in the module at the time, the correlation degree between the water service data and its cause data in the module at the time, and the abnormal independence of the water service data in the module at the time, the failure factors of each directly affecting device of the water service data in the module at the time are obtained, and the specific calculation formula is:

[0070]

[0071] Where, Indicates the first water service data in the module at the time A failure factor that directly affects the equipment; Indicates the abnormal independence of the water service data in the module at the time; Indicates the first The amount of derived data that directly affects the device; Indicates the first water service data in the module at the time Directly affects the equipment the degree of abnormality of the derived data; Indicates the number of types of causal data of the water affairs data in the module; Indicates the number of impact moments of the said moment; Indicates the In the module Water service data and its The degree of correlation between the causal data; Indicates the time The first water service data in the module at the impact moment The degree of abnormality of the causal data; Represents a linear normalization function, whose specific normalization range is all the water service data in the module at the moment that directly affects the equipment .

[0072] It should be noted that the failure factor that directly affects the device indicates the possibility of failure of the device. When the device fails, all the derived data of the device will be affected, resulting in a large degree of abnormality in all the derived data of the device, that is, The larger the value of , the more likely the device is to fail. Since the failure of the derived data of the device may be caused by the abnormality of the causal data of the derived data, it is necessary to constrain the derived data according to the degree of correlation between the derived data and its causal data, combined with the degree of the causal data of the derived data, that is, The larger the value of The smaller the value, the more likely the device is to fail.

[0073] It should be further explained that since the abnormality of water data in a module may be caused by equipment failure in other modules, in order to accurately locate the fault location in the water system, it is also necessary to combine the abnormality level of water data in other modules and the degree of correlation between various water data in each module to obtain the indirect impact of abnormal water data on equipment failure factors; in this way, the fault location in the water system can be accurately located.

[0074] Furthermore, based on the degree of abnormality of the derived data of the equipment indirectly influencing the water service data in the module at the moment, the degree of correlation between the derived data of the equipment indirectly influencing the water service data in the module at the moment, and the abnormal independence of the water service data in the module at the moment, the failure factor of each equipment indirectly influencing the water service data in the module at the moment is obtained, and the specific calculation formula is:

[0075]

[0076] Where, Indicates the first water service data in the module at the time Failure factors that indirectly affect equipment; Indicates the abnormal independence of the water service data in the module at the time; Indicates the first water service data in the module at the time The number of derived data that indirectly affects the device; Indicates the number of impact moments of the said moment; Indicates the time The water affairs data in the module at the time of impact and its The first one that indirectly affects the equipment The degree of correlation between the derived data; Indicates the time The first water service data in the module at the impact moment The first one that indirectly affects the equipment the degree of abnormality of the derived data; Represents a linear normalization function, whose specific normalization range is all the water service data in the module at the moment that directly affects the equipment .

[0077] It should be noted that the failure factor that indirectly affects the device indicates the possibility of failure of the device. When the device fails, all the derived data of the device will be affected, resulting in a large degree of abnormality in all the derived data of the device. At the same time, the derived data of the device must have a strong correlation with the abnormal data that deviates from the normal range. Therefore, The larger the value of , the more likely the device is to fail, and the smaller the abnormal independence of the water service data in the module at the moment is, the more likely it is that the indirect device causes the abnormal water service data. Therefore, The smaller the value of , the more likely the indirect impact device is to fail.

[0078] At this point, the failure factors of each direct device and indirect device of each water service data in each module at each time are obtained.

[0079] Step S004: According to the failure factors of each directly affecting device and each indirectly affecting device of various water data in each module at each moment, obtain the faulty device in each module and mark the faulty device in the digital twin model of the water system.

[0080] It should be noted that, by obtaining the fault factors of each direct device and indirect device of various water service data in each module at each moment through step S003, the fault location in the water service system can be accurately located according to the fault factors of each direct device and indirect device of various water service data in each module at each moment.

[0081] Specifically, a fault factor threshold is preset , The specific value of can be set according to the actual situation. This embodiment does not make a hard requirement. For example, when the abnormality of any water data in any module at any time is greater than or equal to , obtain the failure factors of each direct device and indirect device of the water service data in the module at the time, and connect the device with the largest failure factor and the device with a failure factor greater than or equal to The faulty equipment is recorded as faulty equipment and marked in the digital twin model of the water system.

[0082] Another embodiment of the present invention provides a smart water management monitoring system based on digital twins, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the smart water management monitoring method based on digital twins in steps S001 to S004 is implemented.

[0083] 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, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The smart water monitoring method based on digital twin is characterized by: The method comprises the following steps: Obtain various water data from various modules within the water system; Based on the historical data of various water service data in each module, the abnormality degree of various water service data in each module at each moment is obtained, and the correlation degree between various water service data in each module is obtained; based on the abnormality degree of various water service data in each module at each moment and the correlation degree between various water service data in each module, the abnormal independence of various water service data in each module at each moment is obtained; Obtain the derived data of each device in all modules, and the cause data, directly influencing devices, and indirectly influencing devices of various water service data in all modules. Combined with the abnormal independence of various water service data in each module at each moment, the abnormal degree of various water service data in each module at each moment, and the degree of correlation between various water service data in each module, obtain the failure factors of each directly influencing device and each indirectly influencing device of various water service data in each module at each moment; Based on the failure factors of each directly affecting device and each indirectly affecting device in each module at each moment, the faulty devices in each module are obtained and marked in the digital twin model of the water system. The specific method for obtaining the abnormal independence of various water service data in each module at each time includes: Preset an abnormality threshold and impact timeframe , for the The next moment In the module Water service data, if The next moment In the module The abnormality of water service data is greater than or equal to , then the moments ago All moments within the minute are recorded as The impact moment of the moment, and the All water service data in the module before the module are recorded as In the module Genesis data of water affairs data; According to In the module The degree of correlation between the water affairs data and its cause data, combined with the The impact of the moment In the module The abnormal degree of water service data and its cause data is obtained The next moment In the module The specific calculation formula for the abnormal independence of water service data is: Where, Indicates the The next moment In the module The unusual independence of water service data; Indicates the The next moment In the module The degree of abnormality of water service data; Indicates the In the module The number of types of data that cause water affairs data; Indicates the The number of moments of influence of each moment; Indicates the In the module Water service data and its The degree of correlation between the causal data; Indicates the The moment The impact moment In the module The first The degree of abnormality of the causal data; represents the absolute value function; represents the linear normalization function.

2. The smart water affairs monitoring method based on digital twin according to claim 1 is characterized in that: The specific method for obtaining the abnormality degree of various water service data in each module at each moment includes: For the The next moment In the module Water service data, obtain the first All the modules in Water service data, the first in history All the modules in Water service data and The next moment In the module The water service data is used as the data set, and the LOF outlier detection algorithm is used to obtain the first The moment In the module The LOF value of the water service data is used as the first The moment In the module The abnormality of water service data.

3. The smart water affairs monitoring method based on digital twin according to claim 1 is characterized in that: The specific method for obtaining the degree of correlation between various water service data in various modules is as follows: Obtain the abnormality of all water service data in all modules at all historical moments. In the module Water service data and In the module All water service data; all historical moments are downloaded In the module The abnormality of this water service data is the highest at any time in history. In the module The absolute value of the Pearson correlation coefficient between the abnormality of the water service data is used as the first In the module Water service data and In the module The degree of correlation between various water service data.

4. The smart water affairs monitoring method based on digital twin according to claim 1 is characterized in that: The specific method for obtaining the derived data of each device in all modules, the cause data of various water service data in all modules, the directly affected devices and the indirectly affected devices includes: Obtain water data affected by each device in all modules of the water system; For the In the module device, the In the module The water service data affected by the equipment is recorded as In the module Derived data for each device; For the In the module Water service data, The module affects The equipment that collects water service data is recorded as In the module The direct impact of water data on equipment; and In the module The direct impact of the data on the cause of water affairs data is recorded as the first In the module This equipment indirectly affects water service data.

5. The smart water affairs monitoring method based on digital twin according to claim 1 is characterized in that: The specific method of obtaining the failure factors of each directly affecting device and the failure factors of each indirectly affecting device of various water service data in each module at each moment includes: For any water service data in any module at any time, if the abnormality of the water service data in the module at the time is greater than or equal to , then according to the abnormality degree of the water service data and its cause data in the module at the time, the correlation degree between the water service data and its cause data in the module at the time, and the abnormal independence of the water service data in the module at the time, obtain the fault factors of each device directly affecting the water service data in the module at the time; According to the abnormality degree of the derived data of the indirectly influencing equipment of the water data in the module at the moment, the correlation degree between the derived data of the indirectly influencing equipment and the water data in the module at the moment, combined with the abnormal independence of the water data in the module at the moment, the failure factors of each indirectly influencing equipment of the water data in the module at the moment are obtained.

6. The digital twin-based smart water management monitoring method according to claim 5 is characterized in that: The specific calculation formula for obtaining the failure factors of each directly affecting equipment in the water service data in the module at the time is: Where, Indicates the first water service data in the module at the time A failure factor that directly affects the equipment; Indicates the abnormal independence of the water service data in the module at the time; Indicates the first The amount of derived data that directly affects the device; Indicates the first water service data in the module at the time Directly affects the equipment the degree of abnormality of the derived data; Indicates the number of types of causal data of the water affairs data in the module; Indicates the number of impact moments of the said moment; Indicates the In the module Water service data and its The degree of correlation between the causal data; Indicates the time The first water service data in the module at the impact moment The degree of abnormality of the causal data; represents the linear normalization function.

7. The digital twin-based smart water management monitoring method according to claim 5 is characterized in that: The specific calculation formula for obtaining the failure factors of each indirect influencing device of the water service data in the module at the moment is: Where, Indicates the first water service data in the module at the time Failure factors that indirectly affect equipment; Indicates the abnormal independence of the water service data in the module at the time; Indicates the first water service data in the module at the time The number of derived data that indirectly affects the device; Indicates the number of impact moments of the said moment; Indicates the time The water affairs data in the module at the time of impact and its The first one that indirectly affects the equipment The degree of correlation between the derived data; Indicates the time The first water service data in the module at the impact moment The first one that indirectly affects the equipment the degree of abnormality of the derived data; represents the linear normalization function.

8. The smart water affairs monitoring method based on digital twin according to claim 1 is characterized in that: The specific method of obtaining the faulty device in each module includes: Preset a failure factor threshold , when the abnormality of any water service data in any module at any time is greater than or equal to , obtain the failure factors of each direct device and indirect device of the water service data in the module at the time, and connect the device with the largest failure factor and the device with a failure factor greater than or equal to The device is marked as a faulty device.

9. A smart water monitoring system based on digital twins, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the digital twin-based smart water monitoring method as described in any one of claims 1 to 8 are implemented.

Images