A water meter life cycle monitoring system and method

Abstract

The present invention relates to the technical field of equipment cycle supervision, and discloses a water meter life cycle supervision system and method. The method includes the following steps: collecting water meter images through a mobile terminal, dynamically evaluating image quality, identifying environmental interference types, triggering intelligent re-shooting for low-quality images, extracting core data such as water meter type, reading, and meter body number, and marking the environmental interference status. The confidence threshold is dynamically corrected in combination with the environmental interference type, a compensation algorithm is used to eliminate the effects of reflections / ice layers, the thickness of ice coverage is quantified, and historical data is integrated to calculate the cumulative effect of contamination. The present invention overcomes the problem of optical recognition distortion in complex environments, realizes visual management and control of the entire life cycle through multi-source data fusion and intelligent analysis, builds an environmentally adaptive dynamic decision-making system, shortens abnormal response time, and optimizes maintenance resource allocation. The equipment health model based on real-time status evaluation breaks through the limitations of traditional fixed replacement cycles.

Description

Technical Field

[0001] The present invention relates to the field of equipment cycle supervision, and more particularly, to a water meter life cycle supervision system and method. Background Art

[0002] Traditional water meter lifecycle management relies primarily on manual inspections and paper records, which have the following technical flaws:

[0003] Existing image recognition technology cannot effectively handle complex scenarios such as ice cover (common in northern winter) and strong light reflections (common in outdoor meter boxes), resulting in a reading error rate as high as 30%-45%. Water meter installation information, maintenance records, and environmental data are scattered across different systems, making it difficult to establish a full lifecycle view. Manual analysis takes 2-3 working days, making it impossible to respond to emergencies (such as meter damage caused by freezing) in real time. The mandatory replacement cycle (usually every six years) does not take into account the differences in actual usage environments, resulting in the premature retirement of more than 30% of meters.

[0004] Therefore, a water meter life cycle supervision system is needed to realize intelligent supervision of the entire life cycle of water meters (installation, operation, maintenance, and scrapping), and solve problems such as low efficiency of manual inspections, poor data reliability, and delayed abnormal response. Summary of the Invention

[0005] The present invention provides a water meter life cycle monitoring system and method, which solve the technical problems of low manual inspection efficiency, poor data reliability and delayed abnormal response in related technologies.

[0006] The present invention provides a water meter life cycle supervision method, comprising the following steps:

[0007] S100, Data Collection and Processing: This system collects water meter images through mobile terminals, dynamically assesses image quality, identifies environmental interference types, triggers intelligent retakes for low-quality images, extracts core data such as meter type, meter reading, and meter serial number, and annotates the environmental interference status.

[0008] S200, anti-interference anomaly detection stage: Dynamically adjust the confidence threshold based on the type of environmental interference, use a compensation algorithm to eliminate the impact of reflections / ice, quantify the ice cover thickness, integrate historical data to calculate the cumulative effect of contamination, and generate multi-dimensional alarm levels;

[0009] S300, multimodal decision-making stage: constructing a set of policy candidates, calculating policy priorities through a multi-objective optimization model, screening applicable policies based on real-time meteorological data, verifying safety constraints, and recording decision factors in a knowledge base to support iterative optimization;

[0010] S400, environmental knowledge base iteration: Integrate spatiotemporal data of ice events, train ice formation prediction models, mine association rules between environmental parameters and abnormal conditions, implement dynamic compression of the knowledge base, and update the maintenance cycle recommendation and risk warning rule base.

[0011] Furthermore, in S100, the following steps are specifically included:

[0012] S110, multi-device image capture: collecting water meter images through mobile terminals;

[0013] S120, intelligent quality assessment: dynamic detection of image usability;

[0014] S130, cross-device interference identification: environmental interference detection;

[0015] ;

[0016] in Indicates the average saturation of the dial area, represents the Fourier spectrum energy, Represents logical exclusive OR, Indicates the dial area. represents the blue channel pixel value, It represents the maximum pixel value, represents the minimum pixel value, Indicates the interference type, 0 is normal type, 1 is dirty disk type, 2 is reflective type, 3 is dirty disk and reflective type, 4 is ice layer type, represents the image quality score, Indicates frequency;

[0017] S140, Robust retake strategy: Adaptive optimization based on EXIF ​​data.

[0018] Furthermore, in S200, the following steps are specifically included:

[0019] S210, environmental interference classification: analyzing parameters of interference types and classifying compensation;

[0020] S220, robust reading resolution: dynamically correcting confidence thresholds and reading discrepancies;

[0021] S230, Optical analysis of ice: quantifying the effect of ice on readings;

[0022] S240, multi-dimensional alarm decision: generating composite alarm levels;

[0023] S250, Historical Data Decay Model: Calculating the Cumulative Effect of Defacement.

[0024] Furthermore, in S300, the following steps are specifically included:

[0025] S310, strategy candidate set generation: constructing maintenance operation strategy space;

[0026] S320, Multi-objective Optimization Computation: Calculate the comprehensive utility value of the strategy;

[0027] S330, Environmental Adaptation Strategy Screening: Integration of Meteorological Data Correction Strategy;

[0028] S340, Risk Constraint Verification: Perform safety boundary condition detection;

[0029] S350, iterative update of knowledge base: record decision factors and results.

[0030] Furthermore, the calculation formula for generating the strategy candidate set is as follows:

[0031] ;

[0032] in Indicates the alarm level. Indicates the month value from EXIF ​​time. represents the strategy candidate set, , , , , Represents different maintenance strategy options, Represents logical OR, Represents logical AND, Indicates the interference type, represents the cumulative effect index of pollution, Indicates abnormal indicator of watch body number. Indicates the ice thickness indicator from S230, Represents the indicator function.

[0033] Furthermore, the formula for multi-objective optimization calculation is as follows:

[0034] ;

[0035] in represent the first, second, third and fourth optimization weight coefficients respectively, represents the strategy cost, the implementation cost of different strategies, Representation Strategy The comprehensive utility value of represents the logarithmic transformation of the cumulative effect of pollution, represents the adjusted difference in readings, Indicates abnormal indicator of watch body number. represents the dynamic threshold, Represents an indicator of the cumulative effect of pollution.

[0036] Furthermore, the calculation formula for environmental adaptation strategy screening is as follows:

[0037] ;

[0038] Optimized effect:

[0039] ;

[0040] in Indicates the ambient temperature, represents the weather adaptation factor, Represents meteorological data, including weather type, Indicates rainy and snowy weather. Indicates foggy weather. represents the utility value of the strategy after environmental adaptation, Representation Strategy The comprehensive utility value of represents the physical de-icing strategy, Represents the indicator function.

[0041] Furthermore, the calculation formula for risk constraint verification is as follows:

[0042] ;

[0043] ;

[0044] The safety threshold of 0.7 comes from the ice layer analysis standard. represents the set union operation, which means adding the strategy to the candidate set. Represents an assignment operation, Indicates the ice thickness index, represents the strategy candidate set, Indicates the strategy for replacing the meter. represents the physical de-icing strategy, represents the difference in readings from the adjustment, represents the dynamic threshold;

[0045] The calculation formula for iterative update of the knowledge base is as follows:

[0046] ;

[0047] ;

[0048] in Represents the knowledge base merge operation, adding new records to the knowledge base, represents the effectiveness index of the de-icing strategy, represents the decision knowledge base, Represents the total number of historical decision records, represents the strategy chosen in the jth decision, represents the ice thickness index at the time of the j-th decision, represents the comprehensive utility value of the strategy, represents the weather adaptation factor, Indicates the current timestamp.

[0049] Furthermore, in S400, the following steps are specifically included:

[0050] S410, Multi-source data fusion: integrating ice events and environmental parameters;

[0051] S420, spatiotemporal feature extraction: generating spatiotemporal patterns of ice formation;

[0052] S430, prediction model training: updating the ice formation probability model;

[0053] S440, Anomaly Pattern Mining: Detecting abnormal association rules in the environment;

[0054] S450, Knowledge Base Compression: Implementing Data Value Density Optimization.

[0055] The present invention further provides a water meter lifecycle monitoring system, which executes the steps of the aforementioned water meter lifecycle monitoring method, including:

[0056] Intelligent acquisition module: This module captures water meter images through mobile devices, assesses image clarity, color accuracy, and lighting conditions in real time, automatically identifies environmental interference factors such as reflections and ice, triggers an adaptive retake mechanism, extracts core data such as dial type, real-time reading, and meter body number, and adds a quality label.

[0057] Anti-interference analysis module: This module uses an environmental compensation algorithm to eliminate ice / reflection interference on readings, quantifies ice thickness and the cumulative effect of contamination, and combines historical data to establish a dynamic threshold model for reading deviations to generate an anomaly score that integrates environmental adaptability.

[0058] Multi-strategy decision-making module: Builds a policy space encompassing operations such as cleaning and maintenance, meter replacement, and physical de-icing. It selects the optimal policy based on cost-benefit models and real-time meteorological data, implements safety and compliance verification, and simultaneously updates the policy knowledge graph to optimize decision-making logic.

[0059] Knowledge base management module: Integrates multi-source data such as water meter geographic information, environmental parameters, and maintenance records, trains ice formation prediction models and anomaly association rule engines, optimizes the value density of the knowledge base, and dynamically generates equipment health assessment reports and maintenance cycle recommendations.

[0060] Interactive response module: Connects to the water management system to automatically generate electronic work orders, displays the real-time status and life cycle stages of water meters through a visual dashboard, pushes high-risk warning information, and supports maintenance personnel to feedback disposal results and update system records through mobile terminals.

[0061] The beneficial effects of the present invention are:

[0062] This invention effectively overcomes the problem of optical recognition distortion in complex environments, significantly improves the stability and reliability of water meter data collection, realizes visual management and control of the entire life cycle through multi-source data fusion and intelligent analysis, builds an environmentally adaptive dynamic decision-making system, greatly shortens abnormal response time and optimizes maintenance resource allocation. At the same time, the equipment health model based on real-time status assessment breaks through the limitations of traditional fixed replacement cycles, and significantly improves the accuracy and sustainability of water facility management. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of a water meter life cycle supervision method proposed by the present invention;

[0064] Figure 2 This is a structural block diagram of a water meter life cycle monitoring system proposed in the present invention.

[0065] In the figure: 101, intelligent acquisition module; 102, anti-interference analysis module; 103, multi-strategy decision module; 104, knowledge base management module; 105, interactive response module. DETAILED DESCRIPTION

[0066] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0067] like Figure 1 As shown, a water meter life cycle supervision method includes the following steps:

[0068] S100, Data Acquisition and Processing: This system collects water meter images through mobile terminals, dynamically assesses image quality, identifies environmental interference types, triggers intelligent retakes for low-quality images, extracts core data such as meter type, meter reading, and meter serial number, and annotates the environmental interference status.

[0069] S110, multi-device image capture: collect water meter images through mobile terminals (mobile phones / cameras);

[0070] formula:

[0071] ;

[0072] in Indicates the device resolution;

[0073] The minimum lighting requirements for mobile terminal collection are as follows: =200lux, continuous shooting mode frame rate , the original image data obtained is (JPEG / PNG);

[0074] S120, intelligent quality assessment: dynamic detection of image usability;

[0075] formula:

[0076] ;

[0077] ;

[0078] ;

[0079] in represents the Laplacian operator (image sharpness detection), represents the color channel mean, Indicates the number of sampling points, usually 10% of the total pixels of the image. Indicates that the image is at coordinates The pixel value at Represents RGB color channel values, Represents three color channels respectively. Indicates the image quality score, ranging from [0,1]. The coefficients 0.55 / 0.45 are derived from large-scale measured data fitting. represents the pixel score, Indicates color deviation;

[0080] S130, cross-device interference identification: environmental interference detection;

[0081] formula:

[0082] ;

[0083] in Indicates the average saturation of the dial area, represents the Fourier spectrum energy, Represents logical exclusive OR, Indicates the dial area (number of pixels), represents the blue channel pixel value, It represents the maximum pixel value, represents the minimum pixel value, Indicates the interference type (0 is normal, 1 is dirty disk, 2 is reflection, 3 is dirty disk + reflection, 4 is ice layer), represents the image quality score from S120, Indicates frequency;

[0084] S140, Robust retake strategy: Adaptive optimization based on EXIF ​​data;

[0085] rule:

[0086] ;

[0087] in Indicates the exposure time, Indicates sensitivity, Indicates the default exposure time, usually 1 / 60 second. Indicates the default sensitivity. Indicates the number of retake attempts, represents the image quality score from S120, Indicates the interference type from S130, where the threshold of 0.65 comes from the RC curve analysis of the million-level image test set;

[0088] S200, anti-interference anomaly detection stage: Dynamically adjust the confidence threshold based on the type of environmental interference, use a compensation algorithm to eliminate the impact of reflections / ice, quantify the ice cover thickness, integrate historical data to calculate the cumulative effect of contamination, and generate multi-dimensional alarm levels;

[0089] S210, Classification of Environmental Interference: Analysis Parameters and classification compensation;

[0090] formula:

[0091] ;

[0092] in Indicates the environmental compensation factor, which is used to adjust the confidence level of the readings. Indicates the interference type at the current time point t. The above coefficients are obtained through Monte Carlo simulation optimization;

[0093] S220, robust reading resolution: dynamically correcting confidence thresholds and reading discrepancies;

[0094] formula:

[0095] ;

[0096] ;

[0097] ;

[0098] in represents the dynamic confidence threshold, represents the baseline threshold, represents the dynamic threshold, Indicates an indicator function that returns 1 if the condition is true, otherwise it returns 0. represents the interference type at the first k time points, Indicates the currently detected water meter reading. Indicates the last water meter reading recorded in the system, Indicates the confidence level of the current reading recognition, represents the adjusted difference in readings, represents the environmental compensation factor from S210, Indicates the number of retakes from S140;

[0099] S230, Optical analysis of ice: quantifying the effect of ice on readings;

[0100] formula:

[0101] ;

[0102] Where B(x,y) represents the blue channel intensity (0–255), H(x,y) represents the HSV hue (0°–360°), Indicates the dial area, A indicates the total number of pixels in the dial area, Indicates the minimum value of the blue channel, Indicates the ice thickness index, ranging from [0,1], Indicates an indicator function that returns 1 if the condition is true, otherwise returns 0;

[0103] S240, multi-dimensional alarm decision: generating composite alarm levels;

[0104] formula:

[0105] ;

[0106] in Represent the weights of various factors, represents the ceiling function, Indicates the abnormal indicator of the watch body number, normal is 0, abnormal is 1, Indicates the alarm level, range [1,5], where coefficient 5 is the highest level, represents the adjusted reading difference from S220, represents the dynamic threshold from S220, Indicates the ice thickness indicator from S230;

[0107] S250, Historical Data Decay Model: Calculates the cumulative effect of contamination;

[0108] formula:

[0109] ;

[0110] in represents the attenuation coefficient, , control the weakening speed of the influence of historical data, Represents the current Unix timestamp, Indicates the The timestamp of the detection, represents the interference type detected for the kth time, Indicates the total number of historical records. represents an indicator of the cumulative effect of pollution;

[0111] S300, multimodal decision-making phase: Build a candidate set of "cleaning," "replacement," and "de-icing" strategies, calculate strategy priorities using a multi-objective optimization model, select applicable strategies based on real-time meteorological data, verify safety constraints (such as mandatory de-icing when ice exceeds the limit), and record decision factors in a knowledge base to support iterative optimization.

[0112] In one embodiment of the present invention, the following steps are specifically included:

[0113] S310, strategy candidate set generation: constructing maintenance operation strategy space;

[0114] formula:

[0115] ;

[0116] in Indicates the alarm level from S240, Indicates the month value from EXIF ​​time. represents the strategy candidate set, , , , , Represents different maintenance strategy options, Represents logical OR, Represents logical AND, Indicates the type of interference from S130, represents the cumulative effect index of fouling from S250, Indicates abnormal indicator of watch body number. Indicates the ice thickness indicator from S230, represents the indicator function;

[0117] S320, Multi-objective Optimization Computation: Calculate the comprehensive utility value of the strategy;

[0118] formula:

[0119]

[0120] in represent the first, second, third and fourth optimization weight coefficients respectively, represents the strategy cost, the implementation cost of different strategies, Representation Strategy The comprehensive utility value of represents the logarithmic transformation of the cumulative effect of pollution, represents the difference in adjusted readings from S220, Indicates abnormal indicator of watch body number. represents the dynamic threshold from S220, represents the cumulative effect index of fouling from S250;

[0121] S330, Environmental Adaptation Strategy Screening: Integration of Meteorological Data Correction Strategy;

[0122] formula:

[0123] ;

[0124] Optimized effect:

[0125] ;

[0126] in Indicates the ambient temperature (degrees Celsius), represents the weather adaptation factor, Represents meteorological data, including weather type, Indicates rainy and snowy weather. Indicates foggy weather. represents the utility value of the strategy after environmental adaptation, Indicates the policy from S320 The comprehensive utility value of represents the physical de-icing strategy, represents the indicator function;

[0127] S340, Risk Constraint Verification: Perform safety boundary condition detection;

[0128] rule:

[0129] ;

[0130] ;

[0131] The safety threshold of 0.7 is derived from the S230 ice layer analysis standard. represents the set union operation, which means adding the strategy to the candidate set. Represents an assignment operation, Indicates the ice thickness indicator from S230, represents the strategy candidate set, Indicates the strategy for replacing the meter. represents the physical de-icing strategy, represents the difference in adjusted readings from S220, represents the dynamic threshold from S220;

[0132] S350, iterative update of knowledge base: record decision factors and results;

[0133] formula:

[0134] ;

[0135] ;

[0136] in Represents the knowledge base merge operation, adding new records to the knowledge base, represents the effectiveness index of the de-icing strategy, represents the decision knowledge base, Represents the total number of historical decision records, represents the strategy chosen in the jth decision, represents the ice thickness index at the time of the j-th decision, represents the comprehensive utility value of the strategy from S320, Indicates the weather adaptation factor from S330, Indicates the current timestamp, represents the indicator function, represents the physical de-icing strategy;

[0137] S400, Environmental Knowledge Base Iteration: Integrate spatiotemporal data on ice events, train ice formation prediction models, mine association rules between environmental parameters and abnormal conditions, implement dynamic knowledge base compression, and update the maintenance cycle recommendation and risk warning rule base;

[0138] S410, Multi-source data fusion: integrating ice events and environmental parameters;

[0139] formula:

[0140] ;

[0141] ;

[0142] in Indicates the rate of temperature change (°C / hour), Indicates geographic coordinates, from camera EXIF ​​data, represents the environmental knowledge base, represents the updated environmental knowledge base, represents a collection of meteorological data, Indicates the ice thickness indicator from S230, Indicates the type of interference from S130, represents the set union operation, Indicates the latitude of the event, Indicates the longitude of the event;

[0143] S420, spatiotemporal feature extraction: generating spatiotemporal patterns of ice formation;

[0144] formula:

[0145] ;

[0146] ;

[0147] in represents the geographical cluster radius (220km), , represents the ice event timestamp, represents the floor function, Indicates the number of days between events. Represent the geographic coordinate mean (cluster center), represents a set of geographical location clusters, Indicates the current timestamp, denote the latitude and longitude of the ith event, respectively, and 86400 denotes the number of seconds in a day;

[0148] S430, prediction model training: updating the ice formation probability model;

[0149] formula:

[0150] ;

[0151] ;

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] in Represents the model weight parameters, which are obtained through machine learning algorithm training. represents the sigmid activation function, represents the predicted value of ice formation probability, Indicates relative humidity in percentage. , , , , Represent the characteristic variables of the prediction model respectively, Indicates the ambient temperature, represents the rate of temperature change, represents the effectiveness index of the de-icing strategy from S350, represents the cumulative effect index of fouling from S250;

[0158] S440, Anomaly Pattern Mining: Detecting abnormal association rules in the environment;

[0159] formula:

[0160] ;

[0161] ;

[0162] Application scenario representation

[0163] ;

[0164] ;

[0165] in represents support, the frequency of the rule appearing in the data set, Indicates confidence, the probability that Y is true when condition X is true, represents the number of elements in the set X, Indicates the number of records that satisfy both X and Y. Indicates the total number of records. Represents a set of prerequisites, Represents a result condition set, Represents meteorological data, Indicates the ambient temperature, Indicates the type of interference from S130;

[0166] S450, Knowledge Base Compression: Implementing Data Value Density Optimization;

[0167] formula:

[0168] ;

[0169] ;

[0170] in Indicates the data retention period, which is aligned with the table replacement period. represents the compressed environmental knowledge base, Indicates the timestamp of record x, represents the predicted value of ice formation probability from S430, represents the environmental knowledge base, Indicates the current timestamp.

[0171] like Figure 2 As shown in FIG, a water meter life cycle monitoring system includes the following modules:

[0172] Intelligent acquisition module 101: This module captures water meter images through mobile devices, evaluates image clarity, color accuracy, and lighting conditions in real time, automatically identifies environmental interference factors such as reflections and ice, triggers an adaptive retake mechanism (dynamically adjusts shooting parameters), extracts core data such as dial type, real-time reading, and meter serial number, and adds quality tags.

[0173] Anti-interference analysis module 102: This module uses an environmental compensation algorithm to eliminate ice / reflection interference on readings, quantifies ice thickness and the cumulative effect of contamination, and combines historical data to establish a dynamic threshold model for reading deviations. This module generates an anomaly score that integrates environmental adaptability (covering multi-dimensional indicators such as measurement anomalies, identity anomalies, and environmental risks).

[0174] Multi-strategy decision module 103: Builds a policy space encompassing operations such as cleaning and maintenance, meter replacement, and physical de-icing. It selects the optimal policy based on a cost-benefit model and real-time meteorological data, implements safety and compliance verification (such as mandatory handling of ice exceeding limits), and simultaneously updates the policy knowledge graph to optimize decision logic.

[0175] Knowledge Base Management Module 104: Integrates multi-source data such as water meter geographic information, environmental parameters, and maintenance records to train ice formation prediction models and anomaly association rule engines, optimizes the value density of the knowledge base (retaining high-weight spatiotemporal feature data), and dynamically generates equipment health assessment reports and maintenance cycle recommendations.

[0176] Interactive response module 105: connects to the water management system to automatically generate electronic work orders, displays the real-time status and life cycle stages of water meters through a visual dashboard, pushes high-risk warning information (such as the location of meters in the decline period), and supports maintenance personnel to feedback the disposal results through mobile terminals and update system records.

[0177] In one embodiment of the present invention, an example of a water meter life cycle supervision method process is provided:

[0178] 1. Intelligent data collection (water meter inspection scenario)

[0179] When the inspector takes a photo of the water meter with a mobile phone, the system automatically detects the presence of frost reflections on the dial, immediately prompts "Ice interference detected", and guides the inspector to adjust the shooting angle;

[0180] After taking three consecutive shots in an environment of -5°C, a clear image was successfully obtained and automatically marked with "Watch body number: A3-0927, reading 586m³, moderate ice cover".

[0181] 2. Dynamic anomaly analysis (winter monitoring of old residential communities)

[0182] In response to a week of low temperatures, the system automatically raised the tolerance threshold for water meter reading differences in the area by 15%;

[0183] It was found that the water meter readings in Building 18 had an abnormal increase for three consecutive days (actual value +2m³ / day vs historical average +0.8m³ / day). Combined with the ambient temperature of -8℃, it was determined that the measurement deviation was caused by ice, and a "Level 2 Freezing Risk" warning was generated.

[0184] 3. Multi-factor decision-making (maintenance of the northern water supply network)

[0185] For water meters with both "inconsistent meter body numbers" and "complex contamination," the system compares maintenance costs and recommends: prioritize identity verification and wait until spring for unified meter replacement;

[0186] During severe cold periods, antifreeze cover installation tasks were automatically assigned to 15 high-frequency freezing points, and high-risk areas were displayed on the map using red heat maps.

[0187] 4. Iterative Application of Knowledge (Water Resources Departments in the Yangtze River Delta Region)

[0188] The system analyzed 427 ice events over three years and found that the probability of freezing increased by 62% when the average daily temperature was ≤3°C and the humidity was >80%.

[0189] During the cold wave, preventive monitoring was initiated in advance on 2,316 water meters that met the conditions, reducing the freezing failure rate by 41% year-on-year.

[0190] 5. Visualized Control (Water Group Command Center)

[0191] The electronic signage displays the health status of water meters in each area in real time, using green, yellow, and red to identify the life cycle stages.

[0192] Click on a water meter in the decay period to view historical maintenance records, and right-click to generate a maintenance work order containing location coordinates, on-site photos, and the optimal route.

[0193] Through the closed-loop management of "shooting-analysis-decision-learning", this method has increased the accuracy of water meter anomaly identification from 68% to 92%, reduced maintenance costs by 35%, and effectively extended the service life of water meters by 1.5-2 years.

[0194] The above describes the embodiments of the present invention, but the present invention is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms under the guidance of the present invention, all of which are protected by the present invention.

Claims

1. A water meter life cycle supervision method, characterized in that: The following steps are involved: S100, Data Collection and Processing: This system collects water meter images through mobile terminals, dynamically assesses image quality, identifies environmental interference types, triggers intelligent retakes for low-quality images, extracts core data such as meter type, meter reading, and meter serial number, and annotates the environmental interference status. S200, anti-interference anomaly detection stage: Dynamically adjust the confidence threshold based on the type of environmental interference, use a compensation algorithm to eliminate the impact of reflections / ice, quantify the ice cover thickness, integrate historical data to calculate the cumulative effect of contamination, and generate multi-dimensional alarm levels; S300, multimodal decision-making stage: constructing a set of policy candidates, calculating policy priorities through a multi-objective optimization model, screening applicable policies based on real-time meteorological data, verifying safety constraints, and recording decision factors in a knowledge base to support iterative optimization; S400, environmental knowledge base iteration: Integrate spatiotemporal data of ice events, train ice formation prediction models, mine association rules between environmental parameters and abnormal conditions, implement dynamic compression of the knowledge base, and update the maintenance cycle recommendation and risk warning rule base.

2. A water meter life cycle supervision method according to claim 1, characterized in that: In S100, the following steps are specifically included: S110, multi-device image capture: collecting water meter images through mobile terminals; S120, intelligent quality assessment: dynamic detection of image usability; S130, cross-device interference identification: environmental interference detection; ； in Indicates the average saturation of the dial area, represents the Fourier spectrum energy, Represents logical exclusive OR, Indicates the dial area. represents the blue channel pixel value, It represents the maximum pixel value, represents the minimum pixel value, Indicates the interference type, 0 is normal type, 1 is dirty disk type, 2 is reflective type, 3 is dirty disk and reflective type, 4 is ice layer type, represents the image quality score, Indicates frequency; S140, Robust retake strategy: Adaptive optimization based on EXIF ​​data.

3. A water meter life cycle supervision method according to claim 2, characterized in that: In S200, the following steps are specifically included: S210, environmental interference classification: analyzing parameters of interference types and classifying compensation; S220, robust reading resolution: dynamically correcting confidence thresholds and reading discrepancies; S230, Optical analysis of ice: quantifying the effect of ice on readings; S240, multi-dimensional alarm decision: generating composite alarm levels; S250, Historical Data Decay Model: Calculating the Cumulative Effect of Defacement.

4. A water meter life cycle supervision method according to claim 3, characterized in that: In S300, the following steps are specifically included: S310, strategy candidate set generation: constructing maintenance operation strategy space; S320, Multi-objective Optimization Computation: Calculate the comprehensive utility value of the strategy; S330, Environmental Adaptation Strategy Screening: Integration of Meteorological Data Correction Strategy; S340, Risk Constraint Verification: Perform safety boundary condition detection; S350, iterative update of knowledge base: record decision factors and results.

5. A water meter life cycle supervision method according to claim 4, characterized in that: The calculation formula for generating the policy candidate set is as follows: ； in Indicates the alarm level. Indicates the month value from EXIF ​​time, represents the strategy candidate set, , , , , Represents different maintenance strategy options, Represents logical OR, Represents logical AND, Indicates the interference type, represents the cumulative effect index of pollution, Indicates abnormal indicator of watch body number. Indicates the ice thickness index, Represents the indicator function.

6. A water meter life cycle supervision method according to claim 5, characterized in that: The formula for multi-objective optimization calculation is as follows: ； in represent the first, second, third and fourth optimization weight coefficients respectively, represents the strategy cost, the implementation cost of different strategies, Representation Strategy The comprehensive utility value of represents the logarithmic transformation of the cumulative effect of pollution, represents the adjusted difference in readings, Indicates abnormal indicator of watch body number. represents the dynamic threshold, Represents an indicator of the cumulative effect of pollution.

7. A water meter life cycle supervision method according to claim 6, characterized in that: The calculation formula for environmental adaptation strategy screening is as follows: ； Optimized effect: ； in Indicates the ambient temperature, represents the weather adaptation factor, Represents meteorological data, including weather type, Indicates rainy and snowy weather. Indicates foggy weather. represents the utility value of the strategy after environmental adaptation, Representation Strategy The comprehensive utility value of represents the physical de-icing strategy, Represents the indicator function.

8. A water meter life cycle supervision method according to claim 7, characterized in that: The calculation formula for risk constraint verification is as follows: ； ； The safety threshold of 0.7 is derived from the ice layer analysis standard. represents the set union operation, which means adding the strategy to the candidate set. Represents an assignment operation, Indicates the ice thickness index, represents the strategy candidate set, Indicates the strategy for replacing the meter. represents the physical de-icing strategy, represents the difference in readings from the adjustment, represents the dynamic threshold; The calculation formula for iterative update of the knowledge base is as follows: ； ； in Represents the knowledge base merge operation, adding new records to the knowledge base, represents the effectiveness index of the de-icing strategy, represents the decision knowledge base, Represents the total number of historical decision records, represents the strategy chosen in the jth decision, represents the ice thickness index at the time of the j-th decision, represents the comprehensive utility value of the strategy, represents the weather adaptation factor, Indicates the current timestamp.

9. A water meter life cycle supervision method according to claim 8, characterized in that: In S400, the following steps are specifically included: S410, Multi-source data fusion: integrating ice events and environmental parameters; S420, spatiotemporal feature extraction: generating spatiotemporal patterns of ice formation; S430, prediction model training: updating the ice formation probability model; S440, Anomaly Pattern Mining: Detecting abnormal association rules in the environment; S450, Knowledge Base Compression: Implementing Data Value Density Optimization.

10. A water meter life cycle monitoring system, characterized in that: The steps of executing a water meter life cycle supervision method as described in any one of claims 1 to 9 include: Intelligent acquisition module: This module captures water meter images through mobile devices, assesses image clarity, color accuracy, and lighting conditions in real time, automatically identifies environmental interference factors such as reflections and ice, triggers an adaptive retake mechanism, extracts core data such as dial type, real-time reading, and meter body number, and adds a quality label. Anti-interference analysis module: This module uses an environmental compensation algorithm to eliminate ice / reflection interference on readings, quantifies ice thickness and the cumulative effect of contamination, and combines historical data to establish a dynamic threshold model for reading deviations to generate an anomaly score that integrates environmental adaptability. Multi-strategy decision-making module: Builds a policy space encompassing operations such as cleaning and maintenance, meter replacement, and physical de-icing. It selects the optimal policy based on cost-benefit models and real-time meteorological data, implements safety and compliance verification, and simultaneously updates the policy knowledge graph to optimize decision-making logic. Knowledge base management module: Integrates multi-source data such as water meter geographic information, environmental parameters, and maintenance records, trains ice formation prediction models and anomaly association rule engines, optimizes the value density of the knowledge base, and dynamically generates equipment health assessment reports and maintenance cycle recommendations. Interactive response module: Connects to the water management system to automatically generate electronic work orders, displays the real-time status and life cycle stages of water meters through a visual dashboard, pushes high-risk warning information, and supports maintenance personnel to feedback disposal results and update system records through mobile terminals.

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