A multidimensional analysis method and system based on water conservancy data

Abstract

The present invention provides a multidimensional analysis method and system based on water conservancy data, which monitors and collects data in real time by deploying multiple water conservancy sensors, and transmits the data to a data processing platform using a communication network. First, the collected sensor data is normalized for correlation to unify the data format and eliminate the influence of dimensional differences. Then, the scene image of the sensor's surrounding environment is analyzed by image recognition technology to further judge environmental changes, and the image data is correlated with the sensor data for analysis. Next, data correction and correlation calculation are performed based on the comparison of water characteristics and adjacent sensor data. Based on the analysis results, the confidence of each sensor data is evaluated to determine whether it meets the preset confidence standard. When the sensor data is abnormal, an early warning mechanism is triggered to notify relevant personnel to handle it. This method can effectively improve the monitoring accuracy and response speed of water conservancy data and ensure the safety and stability of the water conservancy system.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a multidimensional analysis method and system based on water conservancy data. Background Art

[0002] Traditional water safety inspection methods often rely on manual inspections and simple sensor monitoring. Manual inspections are limited by time, personnel experience, and work intensity, and may result in problems such as untimely inspections and missed inspection points. For example, manual inspections are generally carried out according to a fixed schedule. If an emergency occurs between two inspections, such as a sudden rupture of a pipeline or a sharp rise in water levels, it is difficult to detect it in time.

[0003] When relying solely on sensor monitoring, if the sensor fails or is disturbed, the lack of other auxiliary verification methods may lead to erroneous monitoring results. Moreover, a single type of sensor can only monitor specific physical quantities, and cannot fully reflect the actual status of the water conservancy system, and cannot use advanced artificial intelligence technology to perform intelligent diagnosis and prediction of the water conservancy system. It is difficult to identify complex abnormal patterns and potential safety hazards through learning historical data and real-time data, and it is difficult to achieve intelligent safety management. Summary of the invention

[0004] The present invention solves the technical problem that adaptive adjustment cannot be performed according to the actual operation status and environmental changes of the water conservancy system by proposing a multidimensional analysis method and system based on water conservancy data.

[0005] One of the embodiments of the present invention provides a multidimensional analysis method based on water conservancy data, comprising the following steps: deploying multiple water conservancy system sensors in a water conservancy system to monitor and collect sensor data in real time, and transmitting the collected sensor data to a data processing platform through a communication network; performing correlation normalization processing on the collected sensor data, unifying sensor data from different sources into a standardized format, and eliminating the influence of data dimension differences; through image recognition, real-time analysis and acquisition of scene images of the sensor's surrounding environment, further judging the scene change situation, generating corresponding image data, and performing correlation analysis with the sensor data; according to the characteristics of the physical water area where the sensor is located, referring to and comparing the normalized data values ​​of adjacent sensors in the same water area, performing data correction and calculating data correlation; based on the data correlation analysis results, judging the confidence of each sensor data, generating a data confidence index, and judging whether it meets the set confidence standard; according to the judgment result of the data confidence, setting a set of early warning rules, the early warning rules include comprehensive judgment conditions based on multiple factors such as data anomalies, data fluctuation thresholds, scene image changes, etc.; when the real-time results of the sensor data meet the early warning rules, the early warning mechanism is immediately triggered to notify relevant operators to respond and process.

[0006] In one of the embodiments, the process further includes encoding the water conservancy system sensor: performing GPS encoding based on the sensor to determine the location data of the water conservancy sensor in the water conservancy system; and performing function encoding based on the sensor to determine the type of parameter measured by the sensor.

[0007] In one of the embodiments, the steps of the GPS encoding process include: using a GPS device to obtain the geographic location information of the sensor and perform multi-source positioning fusion; presetting GPS encoding rules, converting the longitude and latitude coordinates into digital codes, adding check bits to the codes, and detecting whether errors occur in the code during transmission or storage; storing the encoded location data in a database, and real-time monitoring and updating of the sensor location information.

[0008] In one of the embodiments, the sensor function coding process includes the following steps: defining a sensor class based on the sensor coding, including the GPS information attributes and function information attributes of the sensor; creating a water conservancy multidimensional data analysis system class, adding the sensor to the system, finding a sensor matching the given number, and returning its type; if no matching sensor is found, returning None; creating a sensor instance and adding it to the system, creating sensor instances of different types, and adding each sensor instance to the data analysis system.

[0009] Furthermore, a water conservancy multidimensional data analysis system class is created to add sensor instances to the system. This method can receive a sensor instance as a parameter and store it in a data structure of the system, such as a list or dictionary. When adding a sensor, the sensor number can be checked to ensure the uniqueness of the number. If a duplicate number is found, an exception can be thrown or other appropriate processing methods can be taken. Find the sensor that matches the given number and return its type. Implement a search method that receives a sensor number as a parameter and traverses the stored sensor instances in the system to find the sensor that matches the given number. If a matching sensor is found, its type attribute value is returned. If no matching sensor is found, None is returned. Create multiple types of sensor instances and determine the type of sensor to be created based on actual needs. For example, different types of instances such as water level sensors, flow sensors, and water quality sensors can be created. For each type of sensor, set its number and type attributes. The number is generated according to the rules to ensure uniqueness. The type attribute is set according to the actual function of the sensor. Add each sensor instance to the data analysis system and use the add sensor method of the water conservancy multidimensional data analysis system class to add the created sensor instances to the system one by one. During the adding process, verification and error handling are performed to ensure the validity of the sensor instance and the stability of the system.

[0010] In one of the embodiments, the data correlation normalization processing includes the following steps: obtaining the timestamp information of each sensor data; synchronizing and normalizing the data of each sensor according to the timestamp; using the weighted average method to calculate the correlation between the sensor data, and outputting the standardized data set.

[0011] In one embodiment, the image recognition technology uses a real-time camera to capture images of the surrounding environment of the water area, and uses a deep learning algorithm or other pattern recognition method to process the images to determine environmental changes and generate corresponding scene image data.

[0012] In one of the embodiments, the data comparison process of adjacent sensors further includes: determining the time window overlap of adjacent sensor data values, and analyzing the data timing consistency between sensors; if the sensor data value fluctuation exceeds a preset threshold, further confirming it by a redundant data source verification method.

[0013] In one embodiment, the warning rules include the following conditions: triggering a warning when sensor data exceeds a preset extreme value threshold, which includes a maximum value and a minimum value; triggering a warning when the correlation of adjacent sensor data is lower than a preset threshold; triggering a warning when an abnormality occurs in the image of the sensor's surrounding environment; triggering a warning when the sensor's confidence is lower than a preset minimum confidence standard.

[0014] In one embodiment, when the early warning mechanism is triggered, it further includes real-time evaluation of the operating status of the water conservancy system, automatically adjusting the control strategy of the water conservancy facilities according to the early warning level, and notifying the operating personnel to respond in real time by automatically pushing messages, generating alarm reports or other means. Furthermore, the real-time monitoring step includes setting the sampling frequency and data transmission frequency of the sensor for continuous monitoring and data collection of the water conservancy system.

[0015] The present invention also provides an embodiment, including a multidimensional analysis system based on water conservancy data, which is applied to a multidimensional analysis method based on water conservancy data, including a water conservancy data receiving module, which deploys multiple water conservancy system sensors in the water conservancy system to monitor and collect sensor data in real time, and transmits the collected sensor data to a data processing platform through a communication network; a water conservancy data processing module, which performs correlation normalization processing on the collected sensor data, unifies sensor data from different sources into a standardized format, and eliminates the influence of data dimension differences; an image recognition module, which uses image recognition to analyze and obtain scene images of the sensor's surrounding environment in real time, further judges scene changes, generates corresponding image data, and compares the sensor data with the image processing module. The sensor is used for correlation analysis; the data correction module, based on the characteristics of the physical water area where the sensor is located, refers to and compares the normalized data values ​​of adjacent sensors in the same water area, performs data correction and calculates data correlation; the standard judgment module, based on the data correlation analysis results, judges the confidence of each sensor data, generates a data confidence index, and judges whether it meets the set confidence standard; the early warning module, according to the judgment result of the data confidence, sets a set of early warning rules, the early warning rules include comprehensive judgment conditions based on multiple factors such as data anomalies, data fluctuation thresholds, scene image changes, etc.; the notification module, when the real-time results of the sensor data meet the early warning rules, immediately triggers the early warning mechanism and notifies the relevant operators to respond and process.

[0016] The multidimensional analysis method and system based on water conservancy data provided in the above embodiments have the following beneficial effects:

[0017] The present invention provides a multidimensional analysis method based on water conservancy data. By deploying multiple sensors in the water conservancy system and transmitting data in real time, continuous monitoring and comprehensive coverage of the water conservancy system are achieved. Reasonable sampling frequency and data transmission frequency are set to ensure the timeliness and accuracy of the data, providing a reliable basis for subsequent analysis and decision-making. Data correlation normalization processing eliminates the dimensional differences of sensor data from different sources, making the data more standardized. The weighted average method is used to calculate the correlation, and the standardized data set is output, which is convenient for comprehensive analysis and comparison. Image recognition and association analysis technology adds a new dimension to water conservancy system monitoring. Real-time images of the sensor's surrounding environment are captured, and images are processed using deep learning algorithms, etc., to judge scene changes and generate image data, which are associated with sensor data for analysis, so as to more comprehensively understand the operating status of the water conservancy system and changes in the surrounding environment. Data correction and correlation calculation are based on physical water characteristics and adjacent sensor data, which improves the accuracy and reliability of the data. Data confidence judgment provides an important basis for early warning decision-making. The early warning rule comprehensively considers multiple factors. When the sensor data meets the early warning conditions, the early warning mechanism can be triggered in time to notify the operator to respond and process. At the same time, when the warning is triggered, the operating status of the water conservancy system is evaluated in real time, and the control strategy of the water conservancy facilities can be automatically adjusted according to the warning level, which improves the safety and emergency response capability of the water conservancy system. GPS coding and function coding in the sensor coding process ensure the accurate determination of the sensor location and the clear identification of the function, which facilitates the management and data analysis of the sensor. In short, this method provides a strong guarantee for the intelligent management and safe operation of the water conservancy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0019] Figure 1 A working step diagram of a multidimensional analysis method based on water conservancy data provided by an embodiment of the present invention;

[0020] Figure 2 A flowchart of the data comparison process of adjacent sensors provided by an embodiment of the present invention;

[0021] Figure 3 A block diagram of a multi-dimensional analysis system based on water conservancy data provided in another embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0024] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features limited to "first" and "second" may explicitly or implicitly include at least one of the features. In addition, if "and / or" or "and / or" appears in the full text, its meaning includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or solutions that satisfy both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0025] Example 1

[0026] refer to Figure 1-2One embodiment of the present invention provides a multi-dimensional analysis method based on water conservancy data, comprising the following steps: deploying multiple water conservancy system sensors in a water conservancy system to monitor and collect sensor data in real time, and transmitting the collected sensor data to a data processing platform through a communication network; performing correlation normalization processing on the collected sensor data, unifying sensor data from different sources into a standardized format, and eliminating the influence of data dimension differences; through image recognition, real-time analysis and acquisition of scene images of the sensor's surrounding environment, further judging the scene change situation, generating corresponding image data, and performing correlation analysis with the sensor data; according to the characteristics of the physical water area where the sensor is located, referring to and comparing the normalized data values ​​of adjacent sensors in the same water area, performing data correction and calculating data correlation; based on the data correlation analysis results, judging the confidence of each sensor data, generating a data confidence index, and judging whether it meets the set confidence standard; according to the judgment result of the data confidence, setting a set of early warning rules, the early warning rules include comprehensive judgment conditions based on multiple factors such as data anomalies, data fluctuation thresholds, scene image changes, etc.; when the real-time results of the sensor data meet the early warning rules, the early warning mechanism is immediately triggered to notify the relevant operating personnel to respond and process.

[0027] This embodiment mainly focuses on the integrated analysis of multi-dimensional data in the water conservancy system, and improves the intelligent level of water conservancy system management through a deep learning model, especially in the process of data collection, processing, analysis and judgment, using data sources and technologies of multiple dimensions for comprehensive analysis. The first step of multi-dimensional analysis is to collect data from multiple sensors, which come from different parts of the water conservancy system and may monitor different physical quantities (such as water flow, water level, temperature, humidity, pressure, etc.). The multi-dimensional data provided by these sensors need to be integrated and standardized so that they can be effectively compared and analyzed on a unified data platform. The collected data is transmitted to the data processing platform through a communication network, and then normalized to convert the output values ​​of different sensors into the same standard format. In this process, the dimensions of the sensor data are different (such as water level, flow rate, pressure, etc.), but they are all unified into a standard format, eliminating the impact of dimensional differences, so that these data from different sources can be analyzed in multiple dimensions in the same data set. Through image recognition technology, the solution not only relies on sensor data, but also introduces image data analysis of the on-site environment, which makes data analysis not only limited to numerical data, but also combines visual information to form the second dimension of multi-dimensional analysis. The scene images of the sensor's surrounding environment are collected and analyzed in real time, and changes in the environment (such as changes in the water surface, the status of surrounding equipment, etc.) are judged through image recognition, and these visual data are correlated with the sensor data for analysis. This combination of image data and traditional sensor data makes the multi-dimensional analysis of the same environment more comprehensive and accurate.

[0028] The normalized data of adjacent sensors in the same water area are compared and corrected based on the correlation between the sensors. This correction process involves an in-depth analysis of the relationship between different sensors (data sources of different dimensions) to ensure the accuracy of the data. Data correction is performed based on the physical and environmental correlation between sensors, combined with historical data, real-time data and environmental image data. Through this multi-dimensional data comparison and correction, the system can eliminate possible errors and make the analysis results more reliable and accurate. Through the correlation analysis of the data, the confidence of each sensor data is judged, and the data confidence index is generated to determine whether the data meets the preset confidence standard. This process fully reflects the role of multi-dimensional data (numerical data and environmental image data) in judging data quality and credibility. The collected multi-dimensional data is comprehensively evaluated, and the confidence of each sensor data is calculated using the results of the data correlation analysis. This evaluation standard not only relies on a single data source, but also comprehensively considers data from multiple dimensions to ensure that the system can judge the reliability of the data.

[0029] refer to Figure 2 , the data comparison process for adjacent sensors includes: obtaining data from adjacent sensors: obtaining data from two adjacent sensors from the system as the basis for data comparison. Determining the overlap of time windows: confirming whether the timestamps of the two sensors overlap to ensure that the two data can be compared. If they do not overlap, the time window may need to be adjusted or the data may need to be ignored. Analyzing the consistency of data timing: checking whether the data of the two sensors are consistent. If problems with the time sequence are found, the data may need to be adjusted. Determining whether the data fluctuation exceeds the threshold: judging whether the difference in the values ​​of the two sensors is too large according to the preset threshold, the situation exceeding the threshold indicates that there may be a problem and needs to be confirmed by redundant data sources. Verifying the redundant data source: if the data fluctuation exceeds the preset threshold, use redundant sensors or other available data sources to confirm the data. The results of the redundant data source can help verify the credibility of the data. Output verification results: according to the verification results of the redundant data source, the validity of the data is finally confirmed. If the redundant data source confirms that the data is reliable, the data is valid; otherwise, the data is invalid.

[0030] Multi-factor warning rules are not only based on a single data threshold or outlier, but also combine multiple factors, such as data outliers, data fluctuation thresholds, environmental changes, etc. for comprehensive judgment. This multi-factor judgment mechanism is a typical multi-dimensional analysis method. When setting the warning rules, it not only includes simple judgments based on data fluctuation thresholds, but also introduces judgments on scene image changes, and comprehensively considers multiple dimensions (such as data value fluctuations, environmental changes, etc.) to trigger the warning mechanism. This multi-dimensional warning mechanism can improve the accuracy of warnings and response efficiency.

[0031] Early warning mechanism and response: When the real-time results of sensor data meet the preset multi-dimensional early warning rules, the system immediately triggers the early warning mechanism and notifies relevant operators to respond and handle. This early warning mechanism is based on the joint analysis of multi-dimensional data sources and can identify potential problems more accurately. By analyzing multi-dimensional data (sensor data and image data), the system determines whether to trigger an early warning based on multiple factors (such as data fluctuations, scene changes, etc.). This early warning response mechanism based on multi-dimensional data can respond to complex environmental changes more efficiently.

[0032] Sensor deployment and data collection. Multiple water conservancy system sensors should be reasonably deployed in the water conservancy system to ensure that the monitoring area can be fully covered. The type of sensor can be selected according to actual needs, such as water level sensor, flow sensor, water quality sensor, etc. Set the sampling frequency and data transmission frequency of the sensor to achieve continuous monitoring and data collection of the water conservancy system. The sampling frequency should be set according to the change speed and accuracy requirements of the monitoring parameters, and the data transmission frequency should ensure that the data can be transmitted to the data processing platform in a timely manner. The sensor transmits the collected sensor data to the data processing platform through the communication network. The communication network can be wired or wireless to ensure the stability and reliability of data transmission.

[0033] Data correlation normalization processing is performed to obtain the timestamp information of each sensor data. The timestamp can record the acquisition time of the sensor data and provide a basis for the subsequent data synchronization alignment. The data of each sensor is synchronized and aligned according to the timestamp. Since the data acquisition time of different sensors may be different, the data needs to be synchronized and aligned for subsequent correlation calculation and normalization processing. Normalization processing is performed to unify sensor data from different sources into a standardized format to eliminate the impact of data dimension differences. Normalization processing can be performed in a variety of ways, such as minimum-maximum normalization, Z-score normalization, etc. The weighted average method is used to calculate the correlation between the data of each sensor. The weighted average method can assign different weights to different sensor data according to their importance and reliability, so as to more accurately calculate the correlation between the data. The standardized data set is output to provide a basis for subsequent data analysis and processing.

[0034] Image recognition and association analysis: Through image recognition, the scene images of the sensor's surrounding environment are analyzed and obtained in real time. Image recognition can use deep learning algorithms or other pattern recognition methods to process the images of the surrounding environment of the water area taken by the camera to determine the changes in the environment. Further judge the changes in the scene and generate corresponding image data. Image data can include information such as objects, colors, textures, etc. in the environment, providing a basis for subsequent data analysis and early warning. Image data is associated with sensor data for analysis. Association analysis can integrate image data and sensor data by establishing a data model, so as to have a more comprehensive understanding of the operating status of the water conservancy system.

[0035] Data correction and correlation calculation, based on the characteristics of the physical waters where the sensors are located, refer to and compare the normalized data values ​​of adjacent sensors in the same waters. The data of adjacent sensors can provide a reference to help determine the accuracy and reliability of the current sensor data. Data correction is performed to eliminate errors and interference in sensor data. Data correction uses the weighted average method to calculate data correlation and analyze the data relationship between sensors. Data correlation is calculated using methods such as covariance to provide a basis for subsequent data confidence judgment and early warning.

[0036] Data confidence judgment: Based on the results of data correlation analysis, the confidence of each sensor data is judged. The confidence can reflect the reliability and accuracy of sensor data and provide a basis for subsequent early warning decisions. Generate data confidence indicators to determine whether they meet the set confidence standards. The confidence standard can be set according to actual needs. For example, if the confidence is greater than a certain threshold, the data is considered reliable, otherwise the data is considered unreliable.

[0037] Warning rule setting: According to the judgment results of data confidence, a set of warning rules are set. The warning rules should comprehensively consider multiple factors such as data anomalies, data fluctuation thresholds, scene image changes, etc. to ensure that the warning mechanism can be triggered in a timely and accurate manner.

[0038] The warning rules include the following conditions: When the sensor data exceeds the preset maximum / minimum threshold, the warning is triggered. The maximum / minimum threshold can be set according to the type of sensor and the range of the monitoring parameters. When the sensor data exceeds the range, it is considered that there may be an abnormal situation. When the correlation of adjacent sensor data is lower than the preset threshold, the warning is triggered. The correlation threshold can be set according to actual needs. When the correlation of adjacent sensor data is low, it is considered that there may be sensor failure or environmental changes. When the image of the sensor's surrounding environment is abnormal, the warning is triggered. Image abnormalities can be judged by image recognition algorithms. For example, when abnormal objects or color changes appear in the environment, it is considered that there may be safety hazards. When the sensor confidence is lower than the preset minimum confidence standard, the warning is triggered. The confidence threshold can be set according to actual needs. When the sensor confidence is low, the data is considered unreliable and further inspection and processing may be required.

[0039] The early warning mechanism is triggered and responded to. When the real-time results of sensor data meet the early warning rules, the early warning mechanism is triggered immediately. The early warning mechanism can be implemented in a variety of ways, such as automatically pushing messages, generating alarm reports, and issuing sound and light alarms, to ensure that relevant operators can receive early warning information in a timely manner. Notify relevant operators to respond and handle. Operators can take corresponding measures based on the early warning information, such as checking sensor equipment, adjusting the operating parameters of water conservancy facilities, and conducting on-site inspections to ensure the safe operation of the water conservancy system. When the early warning mechanism is triggered, it further includes a real-time evaluation of the operating status of the water conservancy system. The real-time evaluation can comprehensively judge the operating status of the water conservancy system by analyzing information such as sensor data and image data, and provide a basis for subsequent control strategy adjustments.

[0040] Automatically adjust the control strategy of water conservancy facilities according to the warning level. The warning level can be divided according to factors such as the degree of abnormality of sensor data and environmental changes. Different warning levels correspond to different control strategies, such as adjusting the gate opening and starting backup equipment.

[0041] Sensor coding, the coding process of water conservancy system sensors, including GPS coding and function coding.

[0042] GPS encoding: Use GPS equipment to obtain the geographic location information of the sensor and perform multi-source positioning fusion. Multi-source positioning fusion can improve the accuracy and reliability of sensor location information and ensure that the location of the sensor in the water conservancy system can be accurately determined. Preset GPS encoding rules to convert longitude and latitude coordinates into digital codes. Digital coding can easily store and transmit sensor location information, and can also improve the efficiency of data processing. Add a check digit to the code to detect whether the code is wrong during transmission or storage. The check digit can ensure the integrity and accuracy of the sensor location information and avoid inaccurate positioning due to data errors. Store the encoded location data in the database to achieve real-time monitoring and updating of the sensor location information. Real-time monitoring and updating can ensure the accuracy and timeliness of the sensor location information, providing a basis for subsequent data analysis and processing.

[0043] Function coding: Define the sensor class, which contains two attributes: sensor number and type, to represent different sensors. The number can uniquely identify the sensor, and the type can represent the type of measurement parameters of the sensor, such as water level sensor, flow sensor, water quality sensor, etc. Create a water conservancy multidimensional data analysis system class and add the sensor to the system. The data analysis system class can manage and analyze sensor data and provide a basis for subsequent early warning decisions. Find the sensor that matches the given number and return its type. If no matching sensor is found, None is returned. Finding the sensor type by number can easily obtain the measurement parameter information of the sensor, providing a basis for data analysis and processing. Create different types of sensor instances and add each sensor instance to the data analysis system. The sensor instance can represent a specific sensor device. By adding the sensor instance to the data analysis system, unified management and analysis of multiple sensors can be achieved.

[0044] Sensors are the basis of this implementation method. It is necessary to select appropriate sensor types and parameters to ensure that various parameters of the water conservancy system can be accurately monitored. At the same time, the installation and maintenance of sensors also need to be paid attention to to ensure the stability and reliability of the sensors.

[0045] The communication network is the channel for sensor data transmission. It is necessary to select stable and reliable communication network equipment and protocols to ensure that data can be transmitted to the data processing platform in a timely and accurate manner. Data processing is the core of this implementation method. It is necessary to use advanced data processing algorithms and technologies to perform correlation normalization, image recognition, data correction, data confidence judgment and other operations on sensor data to ensure that the operating status of the water conservancy system can be accurately analyzed.

[0046] Early warning system technology: The early warning system is an important part of this implementation method. It needs to adopt reliable early warning algorithms and technologies to trigger the early warning mechanism in a timely and accurate manner according to the early warning rules, and notify relevant operating personnel to respond and handle the situation.

[0047] By comparing the difference between sensor data and actual water conservancy system parameters, the accuracy of this implementation is evaluated. Error analysis, correlation analysis and other methods can be used to evaluate the accuracy of sensor data. Reliability evaluation: The reliability of this implementation is evaluated by analyzing the stability and reliability of sensor data. The reliability of sensor data can be evaluated by variance analysis, confidence interval analysis and other methods. Early warning effect evaluation: The early warning effect of this implementation is evaluated by analyzing the triggering situation and response time of the early warning mechanism. The effectiveness of the early warning mechanism can be evaluated by statistical analysis, case analysis and other methods. Comprehensive evaluation: Comprehensively consider factors such as accuracy, reliability and early warning effect to conduct a comprehensive evaluation of this implementation. The hierarchical analysis method, fuzzy comprehensive evaluation method and other methods can be used to evaluate the comprehensive effect of this implementation.

[0048] Precautions during implementation: Sensor installation and maintenance: The installation location of the sensor should be reasonably selected to ensure that the various parameters of the water conservancy system can be accurately monitored. At the same time, the maintenance of the sensor also needs to be paid attention to. The working status of the sensor should be checked regularly to ensure the stability and reliability of the sensor. Communication network stability: The stability of the communication network is the key to data transmission. It is necessary to ensure the normal operation of the communication network equipment, timely eliminate network failures, and ensure that the data can be transmitted to the data processing platform in a timely and accurate manner. Data processing algorithm selection: The selection of data processing algorithms should be based on actual needs to ensure that the operating status of the water conservancy system can be accurately analyzed. At the same time, the performance of the data processing algorithm also needs to be considered to ensure that the data processing task can be completed within a reasonable time. Warning rule setting: The setting of warning rules should be reasonable and scientific, and comprehensively consider multiple factors such as data outliers, data fluctuation thresholds, scene image changes, etc., to ensure that the warning mechanism can be triggered in a timely and accurate manner. Operation personnel training: Relevant operators should receive professional training and be familiar with the operating procedures and warning mechanisms of this implementation method to ensure that they can respond to and process warning information in a timely and accurate manner.

[0049] refer to Figure 3The present invention also provides an embodiment, including a multidimensional analysis system based on water conservancy data, which is applied to a multidimensional analysis method based on water conservancy data, including: a water conservancy data receiving module 1, which deploys multiple water conservancy system sensors in the water conservancy system to monitor and collect sensor data in real time, and transmits the collected sensor data to a data processing platform through a communication network; a water conservancy data processing module 2, which performs correlation normalization processing on the collected sensor data, unifies the sensor data from different sources into a standardized format, and eliminates the influence of data dimension differences; an image recognition module 3, which analyzes and obtains scene images of the sensor surrounding environment in real time through image recognition, further judges the scene changes, generates corresponding image data, and compares it with the sensor data. The sensor is used for performing correlation analysis based on the data; the data correction module 4 performs data correction and calculates data correlation based on the characteristics of the physical water area where the sensor is located and the normalized data values ​​of adjacent sensors in the same water area; the standard judgment module 5 judges the confidence of each sensor data based on the data correlation analysis results, generates a data confidence index, and judges whether it meets the set confidence standard; the early warning module 6 sets a set of early warning rules based on the judgment results of the data confidence, and the early warning rules include comprehensive judgment conditions based on multiple factors such as data anomalies, data fluctuation thresholds, and scene image changes; the notification module 7 immediately triggers the early warning mechanism when the real-time results of the sensor data meet the early warning rules, and notifies the relevant operating personnel to respond and process.

[0050] This embodiment proposes a multidimensional analysis method and system based on water conservancy data. By deploying multiple sensors in the water conservancy system and transmitting data in real time, continuous monitoring and comprehensive coverage of the water conservancy system are achieved. Reasonable sampling frequency and data transmission frequency are set to ensure the timeliness and accuracy of the data, providing a reliable basis for subsequent analysis and decision-making. Data correlation normalization processing eliminates the dimensional differences of sensor data from different sources, making the data more standardized. The weighted average method is used to calculate the correlation and output a standardized data set, which is convenient for comprehensive analysis and comparison. Image recognition and association analysis technology adds a new dimension to water conservancy system monitoring. Real-time images of the sensor's surrounding environment are captured, and images are processed using deep learning algorithms, scene changes are judged and image data is generated. Correlation analysis with sensor data can more comprehensively understand the operating status of the water conservancy system and changes in the surrounding environment. Data correction and correlation calculation are based on physical water characteristics and adjacent sensor data, which improves the accuracy and reliability of the data. Data confidence judgment provides an important basis for early warning decisions. The early warning rules take into account multiple factors. When the sensor data meets the early warning conditions, the early warning mechanism can be triggered in time to notify the operator to respond and process. At the same time, when the warning is triggered, the operating status of the water conservancy system is evaluated in real time, and the control strategy of the water conservancy facilities can be automatically adjusted according to the warning level, which improves the safety and emergency response capability of the water conservancy system. GPS coding and function coding in the sensor coding process ensure the accurate determination of the sensor location and the clear identification of the function, which facilitates the management and data analysis of the sensor. In short, this method provides a strong guarantee for the intelligent management and safe operation of the water conservancy system.

[0051] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A multidimensional analysis method based on water conservancy data, characterized in that: The following steps are involved: Deploy multiple water conservancy system sensors in the water conservancy system to monitor and collect sensor data in real time, and transmit the collected sensor data to the data processing platform through a communication network; Perform correlation normalization on the collected sensor data, unify sensor data from different sources into a standardized format, and eliminate the impact of data dimension differences; Through image recognition, the scene images of the sensor's surrounding environment are analyzed and acquired in real time, and the scene changes are further judged, the corresponding image data is generated, and the image data is correlated and analyzed with the sensor data; According to the characteristics of the physical waters where the sensors are located, the normalized data values ​​of adjacent sensors in the same waters are compared and compared to perform data correction and calculate data correlation; Based on the data correlation analysis results, the confidence of each sensor data is determined, and a data confidence index is generated to determine whether it meets the set confidence standard; According to the judgment result of data confidence, a set of early warning rules are set, which include comprehensive judgment conditions based on multiple factors such as data anomalies, data fluctuation thresholds, scene image changes, etc.; When the real-time results of sensor data meet the warning rules, the warning mechanism is immediately triggered to notify relevant operators to respond and handle the situation.

2. The multidimensional analysis method based on water conservancy data according to claim 1, characterized in that: It also includes encoding the water conservancy system sensor, performing GPS encoding based on the sensor to determine the location data of the water conservancy sensor in the water conservancy system; and performing function encoding based on the sensor to determine the parameter type measured by the sensor.

3. The multidimensional analysis method based on water conservancy data according to claim 2, characterized in that: The GPS encoding process steps include: Use GPS equipment to obtain the geographical location information of the sensor and perform multi-source positioning fusion; Preset GPS encoding rules, convert latitude and longitude coordinates into digital codes, add check bits to the codes, and detect whether errors occur during transmission or storage of the codes; The encoded location data is stored in the database, and the sensor location information is monitored and updated in real time.

4. The multidimensional analysis method based on water conservancy data according to claim 2, characterized in that: The sensor function coding process steps include: According to the sensor code, define the sensor class, including the GPS information attributes and function information attributes of the sensor; Create a water conservancy multidimensional data analysis system class, add sensors to the system, find the sensor that matches the given number, and return its type; If no matching sensor is found, None is returned; Create sensor instances and add them to the system, create multiple types of sensor instances, and add each sensor instance to the data analysis system.

5. The multidimensional analysis method based on water conservancy data according to claim 1, characterized in that: The data correlation normalization process comprises the following steps: Get the timestamp information of each sensor data; The data of each sensor is synchronized and aligned according to the timestamp and normalized; The weighted average method is used to calculate the correlation between the sensor data and output the standardized data set.

6. The multidimensional analysis method based on water conservancy data according to claim 1, characterized in that: The image recognition technology uses a real-time camera to capture images of the surrounding environment of the water area, and uses a deep learning algorithm or other pattern recognition method to process the images to determine environmental changes and generate corresponding scene image data.

7. The multidimensional analysis method based on water conservancy data according to claim 1, characterized in that: The data comparison process of adjacent sensors further includes: Determine the time window overlap of adjacent sensor data values ​​and analyze the data timing consistency between sensors; If the sensor data value fluctuation exceeds the preset threshold, it is further confirmed by using the redundant data source verification method.

8. The multidimensional analysis method based on water conservancy data according to claim 1, characterized in that: The warning rules These include the following conditions: When the sensor data exceeds the preset maximum threshold, an alarm is triggered; When the correlation of adjacent sensor data is lower than the preset threshold, an alarm is triggered; When the image of the sensor's surrounding environment is abnormal, an early warning is triggered; When the sensor confidence falls below the preset minimum confidence standard, an early warning is triggered.

9. The multidimensional analysis method based on water conservancy data according to claim 1, characterized in that: When the early warning mechanism is triggered, it further includes real-time evaluation of the operating status of the water conservancy system, automatic adjustment of the control strategy of the water conservancy facilities according to the early warning level, and notification of operating personnel to respond in real time by automatically pushing messages, generating alarm reports or other means.

10. A multidimensional analysis system based on water conservancy data, applied to a multidimensional analysis method based on water conservancy data, characterized in that: include, A water conservancy data receiving module deploys multiple water conservancy system sensors in the water conservancy system to monitor and collect sensor data in real time, and transmits the collected sensor data to the data processing platform through a communication network; The water conservancy data processing module performs correlation normalization on the collected sensor data, unifies the sensor data from different sources into a standardized format, and eliminates the impact of data dimension differences; The image recognition module uses image recognition to analyze and obtain scene images of the sensor's surrounding environment in real time, further determine scene changes, generate corresponding image data, and perform correlation analysis with sensor data; The data correction module performs data correction and calculates data correlation based on the characteristics of the physical water area where the sensor is located and by comparing the normalized data values ​​of adjacent sensors in the same water area; The standard judgment module judges the confidence of each sensor data based on the data correlation analysis results, generates a data confidence index, and determines whether it meets the set confidence standard; The early warning module sets a set of early warning rules according to the judgment result of data confidence, and the early warning rules include comprehensive judgment conditions based on multiple factors such as data anomalies, data fluctuation thresholds, scene image changes, etc.; The notification module immediately triggers the warning mechanism when the real-time results of the sensor data meet the warning rules, and notifies the relevant operators to respond and handle the situation.

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