A monitoring and correction method for evaporation data integrating multiple detection conditions

Abstract

The invention belongs to the technical field of data monitoring and correction, and specifically discloses an evaporation data monitoring and correction method integrating multiple detection conditions, comprising: constructing a monitoring process, connecting an input node and an output node by setting n judgment nodes; setting a judgment threshold on each judgment node, judging the detection condition in which the evaporation data is generated by the judgment threshold, and marking the evaporation data with a label according to the detection condition; inputting the evaporation data obtained in units of a first time period at the input node; performing m corrections on the evaporation data according to the label; until the evaporation data reaches an output node, the evaporation data is output as a correction result in units of the first time period at the output node; and having the following advantages: correcting the evaporation data step by step through multiple judgment nodes, marking and correcting accurately, improving the accuracy of the data, reducing manual intervention through automated processing, and ensuring the uniqueness and reliability of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring and correction, and in particular to an evaporation data monitoring and correction method integrating multiple detection conditions. Background Art

[0002] Traditional evaporation monitoring is mainly done manually, requiring manual visits to the evaporation observation site to read and calculate the evaporation value. When collecting data, the data will be judged based on historical experience and the actual meteorological conditions to determine whether the data is available. With the development of information technology, the application of automatic evaporation equipment has been gradually promoted, and the timeliness and density of data monitoring have been guaranteed through automatic monitoring. However, the accuracy and availability of automatic evaporation data cannot be guaranteed when the automatic equipment is running in situations such as water replenishment and overflow, and when meteorological factors such as rainfall and strong winds have a significant impact on the real-time evaporation data observation.

[0003] Therefore, a evaporation data monitoring and correction method integrating multiple detection conditions is proposed to solve the above-mentioned problems. Summary of the invention

[0004] The present invention aims to provide an evaporation data monitoring and correction method integrating multiple detection conditions, so as to solve or improve at least one of the above-mentioned technical problems.

[0005] In view of this, a first aspect of the present invention is to provide an evaporation data monitoring and correction method integrating multiple detection conditions.

[0006] A first aspect of the present invention provides an evaporation data monitoring and correction method integrating multiple detection conditions, comprising the following steps: constructing a monitoring process including an input node and an output node, wherein the input node and the output node are connected by setting n judgment nodes; the judgment nodes are unidirectionally connected to each other to transmit the evaporation data step by step; setting a different judgment threshold value on each of the judgment nodes, so that when the evaporation data reaches the judgment node, the detection condition in which the evaporation data is generated is judged by the judgment threshold value, and the evaporation data is labeled by the detection condition; the evaporation data obtained in units of a first time period is input at the input node; when the evaporation data is transmitted between the judgment nodes, m corrections are performed on the evaporation data according to the label; until the evaporation data reaches the output node, the evaporation data is output as a correction result in units of the first time period at the output node.

[0007] In any of the above technical solutions, after the evaporation data is transmitted from the input node, the evaporation data monitoring and correction method further includes: setting a time slice of a scale, dividing the first time period into z second time periods through the time slice; copying the evaporation data and distributing it according to the second time periods, so as to obtain the distributed secondary data in each second time period.

[0008] In any of the above technical solutions, in each of the first time periods, the evaporation data in units of the first time period and the secondary data in z second time periods are transmitted between the input node and the judgment node and between adjacent judgment nodes.

[0009] In any of the above technical solutions, the judgment threshold includes an environmental factor value that can affect the accuracy of the evaporation data, a preset evaporation value for judging whether the evaporation data is within a reasonable range, an environmental action time value set by the scale of the time slice, and an error detection value for judging whether the evaporation data deviates before and after correction.

[0010] In any of the above technical solutions, the environmental factor value, the preset evaporation value, the environmental action time value and the error detection value are arranged in sequence along the transmission direction of the evaporation data; along the transmission direction, the later judgment threshold affects the earlier judgment threshold.

[0011] In any of the above technical solutions, the step of performing m corrections on the evaporation data according to the label includes: calculating the evaporation data through the secondary data of z second time periods in the current first time period; comparing and screening the evaporation data input by the input node and the calculated evaporation data, and transmitting the screening result to the output node.

[0012] In any of the above technical solutions, m is an integer not less than zero.

[0013] In any of the above technical solutions, the step of calculating the evaporation data using the secondary data of z second time periods includes: identifying all the tags of the current evaporation data and configuring a calculation formula according to the tags;

[0014] In the first time period, the evaporation data is calculated using the secondary data of z second time periods using the calculation formula currently configured.

[0015] In any of the above technical solutions, the step of comparing and screening the evaporation data input by the input node and the calculated evaporation data includes: setting the value of the judgment threshold at the current judgment node according to the error range of the evaporation equipment for obtaining the evaporation data; calculating the absolute value of the difference between the input evaporation data and the calculated evaporation data; judging whether the absolute value of the difference is less than the judgment threshold, if so, retaining the input evaporation data, otherwise retaining the calculated evaporation data.

[0016] In any of the above technical solutions, the correction result output by the output node is the evaporation data before correction or the evaporation data after correction.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] By setting n judgment nodes between the input node and the output node, the evaporation data undergoes multiple judgments and corrections during the transmission process. This multi-level correction mechanism can effectively identify and correct data errors caused by environmental factors (such as rainfall, strong winds) or equipment abnormalities (such as water replenishment, overflow). At each judgment node, the evaporation data is labeled accordingly according to different judgment thresholds, and accurate corrections can be made for different detection conditions (such as temperature changes, humidity changes), thereby improving data accuracy.

[0019] By setting different judgment thresholds and labels, the system can identify the various detection conditions of evaporation data and perform corresponding processing. This enables the system to maintain a high level of monitoring accuracy when facing complex and changing environmental conditions, and perform m corrections on the evaporation data according to actual needs to ensure the best correction effect in different situations.

[0020] The one-way connection and step-by-step transmission mechanism between judgment nodes make the data processing flow clear and reduce the complexity of data processing. Through pre-set judgment thresholds and labels, the system can automatically complete data judgment and correction, reducing dependence on manual intervention and improving data processing efficiency.

[0021] At the output node, the corrected evaporation data is output in units of the first time period to ensure that the final output data is unique, avoiding data redundancy problems in the database. No matter how many times the data is corrected during the transmission process, accurate evaporation data will eventually be output, avoiding missing or blank data and providing reliable data support for subsequent analysis and decision-making.

[0022] The reliance on manual operation is reduced, and errors and delays that may be caused by manual reading and calculation are avoided. In situations where the automatic evaporation equipment may be affected by factors such as water replenishment, overflow, rainfall, strong winds, etc., resulting in the inability to guarantee data accuracy and availability, the present invention provides an effective solution to ensure the reliability of automatic monitoring data.

[0023] Additional aspects and advantages of embodiments according to the present invention will become apparent in the following description or may be learned through practice of embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0025] Figure 1 are the method steps of the present invention;

[0026] Figure 2 A monitoring process diagram of the present invention;

[0027] Figure 3 It is a system logic block diagram of the present invention;

[0028] Figure 4 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0029] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0031] See also Figure 1-Figure 4 , the following describes some embodiments of the present invention, the evaporation data monitoring and correction method integrating multiple detection conditions.

[0032] The embodiment of the first aspect of the present invention proposes an evaporation data monitoring and correction method integrating multiple detection conditions. In some embodiments of the present invention, Figure 1 As shown, the evaporation data monitoring and correction method includes the following steps:

[0033] S101, construct a monitoring process including input nodes and output nodes, the monitoring process is used to monitor and correct the evaporation data of each time unit, the input node and the output node are connected by setting n judgment nodes, n is an integer greater than one; the judgment nodes are unidirectionally connected to transmit the evaporation data step by step, and the evaporation data can be judged and corrected during the transmission process.

[0034] Here, the input node is the starting point of data collection, responsible for receiving the raw evaporation data and related environmental data (such as temperature, humidity, and wind speed) from the evaporation measurement equipment. These data are the basis for subsequent analysis and correction; in actual applications, the input node may be connected to one or more evaporation pans or similar devices to collect hourly or daily water level change data and synchronized weather station data. Each judgment node evaluates and makes necessary corrections to the data sent by the previous node. The node checks whether there are anomalies in the data (such as unexpected water level changes) according to the set algorithm or rules, and adjusts the data to reflect a more accurate evaporation situation; the first judgment node may focus on detecting water level data and exclude abnormal water level changes caused by rainfall or equipment failure. The second node may check the impact of environmental factors on evaporation, such as adjusting the evaporation rate according to wind speed and temperature when necessary. The third node performs a comprehensive analysis and corrects the final evaporation amount in combination with all available data; n judgment nodes, where n is an integer greater than one, ensure that there are enough processing levels to gradually refine and correct the data. The output node is the end point of the data process and is responsible for summarizing all corrected evaporation data and outputting them for further analysis or decision-making. The output node is usually connected to a central data system. The summarized data can be used to generate water resource management reports or input into meteorological models to help predict weather and water cycle changes.

[0035] As can be seen from the above, the process ensures the continuity and integrity of the data by continuously collecting data from evaporation equipment and environmental sensors through input nodes. This design ensures that there is no time and information gap from data collection to processing, ensuring seamless data monitoring; by setting up multiple judgment nodes, the process realizes the step-by-step correction of evaporation data. Each node analyzes the data according to the preset logic, gradually eliminating anomalies caused by non-evaporation factors (such as rainfall or equipment failure), and the impact of environmental factors (such as wind speed and temperature changes) on the evaporation rate. This hierarchical processing method increases the accuracy of data correction. Each level of correction is based on the verification of the previous level, making the final data more accurate; at the final output node, all the data that has been corrected layer by layer are integrated and compiled into reports or directly transmitted to the data center. This step is not only data output, but also an encapsulation and summary of the entire data processing process. The output data provides a basis for subsequent decision-making and analysis.

[0036] In any of the above embodiments, m is an integer not less than zero, indicating that the evaporation data for each time unit can be directly adopted and applied or can be adopted and applied after being corrected at least once.

[0037] In this embodiment, m is an integer not less than zero, which is used to indicate the number of corrections that the evaporation data for each time unit needs to undergo to ensure the accuracy and availability of the data. The value of m determines the processing flow that the data needs to go through before being adopted: m=0: indicates that the evaporation data can be used directly without any correction after being collected. This is suitable for monitoring situations where the environment is relatively stable and the equipment is highly accurate, where the original readings of the data are accurate enough and do not require further processing; m≥1: indicates that the evaporation data for each time unit needs to be corrected at least once before being adopted. This is a necessary step in situations where the data may be affected by factors such as meteorological changes and equipment errors to ensure the accuracy of the data.

[0038] Suppose that in an area with complex meteorology, the evaporation monitoring system is set to m=2: Under this setting, the preliminary evaporation data monitored will first go through a data cleaning stage to check and remove abnormal data caused by unexpected rainfall last night. After the first correction, the data will enter the second correction stage, at which time the system will adjust the estimated value of the evaporation rate based on the actual temperature and wind speed in the last few hours.

[0039] By setting the value of m, the monitoring system can flexibly adjust the data processing process to ensure that data processing under different environments and requirements can meet the established accuracy standards. This strategy enables the data processing process to quickly respond to needs in simple situations and fully handle data errors that may occur in complex situations. The entire process maximizes the practicality and reliability of the data by adjusting the depth and breadth of data processing according to the value of m. This not only improves the quality of monitoring data, but also optimizes the use of resources and the formulation of management decisions. It is an efficient and economical data processing strategy.

[0040] S102, setting different judgment thresholds at each judgment node, so that when the evaporation data reaches the judgment node, the detection condition under which the evaporation data is generated is judged by the judgment threshold, the detection condition is the condition that can affect the detection result when the evaporation equipment obtains the evaporation data, and the detection condition at least includes environmental factors, and the evaporation data is labeled by the detection condition, and the labeling can be used to perform targeted judgment and correction in the subsequent steps of the monitoring process.

[0041] Here, each judgment node has a specific threshold value to evaluate whether the evaporation data is affected by a specific operating condition. These thresholds are based on historical data and preset environmental factor standards, such as temperature, humidity, wind speed, etc. At the first judgment node, a threshold is set to detect whether the temperature exceeds 30 degrees Celsius, because high temperature may cause an increase in the evaporation rate. If the data shows that the temperature exceeds this threshold, the node will mark the evaporation data as "high temperature affected". The detection conditions include all environmental factors that may affect the accuracy of the evaporation data. These factors are analyzed by the judgment node to determine which specific conditions are effective when the data is collected. If the second judgment node detects an abnormally high wind speed, it may be regarded as a condition where the wind drying effect is significantly enhanced, and the evaporation data is marked as "wind drying affected" accordingly. By labeling the evaporation data, more accurate data correction can be performed in the subsequent steps of the monitoring process. These labels help the system identify data points that need special attention. The data marked with "high temperature affected" will be adjusted by a specific algorithm during subsequent processing to reflect the actual evaporation rate under high temperature, thereby ensuring the practicality and accuracy of the data.

[0042] As can be seen from the above, it is through detailed data analysis and labeling that each processing stage of evaporation data can be appropriately adjusted for specific environmental impacts. This not only improves the accuracy of data processing, but also increases the adaptability and responsiveness of the monitoring system; by detecting and labeling environmental factors in the evaporation data of each time unit, it ensures that subsequent processing stages can make accurate corrections for specific working conditions; the set thresholds and labeling systems make data processing no longer static, but can be flexibly adjusted according to actual conditions to better adapt to complex and changing environmental conditions.

[0043] S103, inputting evaporation data obtained in units of a first time period at an input node, where the first time period may be one day; while the evaporation data is being transmitted between judgment nodes, performing m corrections on the evaporation data according to the labels.

[0044] Here, the input node is responsible for collecting evaporation data within the set first time period, usually set to one day. This setting allows data collection to cover the evaporation of the entire daily cycle, including environmental factors directly related to evaporation, such as temperature, humidity, wind speed, etc. At a hydrological station, the input node automatically collects data from the evaporation measurement instrument every day. This includes evaporation from morning to night and synchronously collected temperature, humidity and wind speed data. This information is subsequently used to analyze daily evaporation trends and the impact of environmental factors.

[0045] During the transmission of evaporation data from the input node to the output node, it is corrected m times at each judgment node according to the pre-set labels. These corrections are based on the label to judge the specific environmental conditions of the evaporation data, such as high temperature or strong wind, and each correction is to accurately adjust the data to more accurately reflect the actual evaporation situation; if the evaporation data is marked as "high temperature impact", the first judgment node will check and adjust the evaporation that may be high due to higher than normal temperatures. At the second node, if the data is also marked as "strong wind", the evaporation is further corrected to take into account the impact of wind speed accelerating evaporation. Each correction step ensures that the data is more accurate and reflects the real environmental conditions.

[0046] As can be seen above, through continuous data collection and multiple corrections of the system, the entire process effectively improves the accuracy of evaporation data. From the complete collection of daily data to the precise correction of specific environmental factors, each step ensures the quality and reliability of the data; the corrected data can better serve applications such as water resources management, agricultural irrigation scheduling, and climate model analysis. Each correction based on environmental tags is to make the data more in line with the needs of actual applications, thereby improving the effectiveness of decision-making and the efficiency of resource use; by setting different m values, the system can be flexibly adjusted according to different monitoring conditions and data quality requirements. This not only improves the accuracy of data processing, but also enhances the system's ability to adapt to environmental changes.

[0047] Specifically, after the evaporation data is transmitted from the input node, the evaporation data monitoring and correction method further includes:

[0048] A time slice of a certain scale is set. The time slice is a time segment with adjustable time axis length, so as to determine a certain size of the divided time period. The first time period is divided into z second time periods by the time slice. The scale of the time slice can be one hour, two hours, three hours...twenty-four hours, or any time segment that can be divided by 24, so that z is a positive integer.

[0049] The evaporation data is copied and distributed according to the second time period to obtain the distributed secondary data in each second time period. The evaporation data usually includes continuous evaporation data in a continuous time, so it can be continuously distributed through multiple time periods to obtain a smaller time scale in subsequent monitoring and increase the accuracy of correction.

[0050] In view of the above specific description, the time slice is used to subdivide the first time period (such as one day) into several second time periods. The scale of these time slices can be set to one hour, two hours, three hours, etc., up to 24 hours according to the needs. The selected time slice scale must be a value that is divisible by 24 to ensure that the whole day is evenly divided so that z (the number of second time periods) is a positive integer; if the monitoring project requires a detailed analysis of the evaporation difference between night and day, the time slice can be set to 12 hours, so that a day is divided into two time periods: daytime (for example, 6:00 AM to 6:00 PM) and nighttime (6:00 PM to 6:00 AM). This allows the analysis to be performed separately for these two different time periods.

[0051] The use of time slices makes data analysis more detailed, allowing researchers or technicians to analyze evaporation data within a specific time period, thereby accurately evaluating how environmental factors in different time periods affect the evaporation process; in an agricultural irrigation management project, the time slice is set to a time period of 3 hours to study the changes in evaporation rate during the period of rising temperature. Through this segmentation, the specific impact of daily temperature differences on evaporation can be tracked in detail, thereby optimizing irrigation strategies to reduce water waste.

[0052] The collected evaporation data is copied and distributed according to a preset second time period (time slice). The purpose of this is to break down the continuous evaporation data into smaller time units so that each unit has corresponding data representation. This method allows the data in each time slice to be analyzed and corrected independently, ensuring that the data in each small period of time accurately reflects its specific environmental conditions; suppose that in a hydrological monitoring project, the data needs to be analyzed in detail for changes in evaporation within each hour. In this case, the data from 6:00 am to 6:00 pm is copied and distributed into 12 one-hour time slices. The data for each hour can be evaluated independently, such as taking into account the immediate impact of factors such as temperature increases or rainfall.

[0053] By distributing continuous data into multiple shorter time periods, monitoring efforts can be more precisely targeted to evaporation characteristics during specific periods of time. This subdivision makes it possible to precisely adjust the environmental response and evaporation efficiency for each time slice; in an agricultural research scenario, researchers may need to analyze the rapid increase in evaporation within the first three hours after sunrise. By isolating these three hours of data, irrigation needs in the morning can be adjusted and responded to more accurately.

[0054] As can be seen above, by subdividing time slices, the data processing process can more accurately locate environmental changes in specific time periods, enhancing the accuracy and practicality of data analysis. This is especially important for evaporation data analysis that needs to consider intraday changes; setting different time slices allows researchers to flexibly choose the granularity of data analysis according to project requirements. This flexibility is key to assessing the impact of complex environmental variables, especially in areas where meteorological conditions change rapidly; accurate time slice segmentation helps decision makers better understand water demand and evaporation losses in different time periods, thereby making more scientific water resource allocation and scheduling decisions.

[0055] By breaking down the data into smaller time periods, the meticulousness of monitoring is greatly increased. The data for each time period can be analyzed and corrected individually to ensure the accuracy and reliability of the overall data; subdivided time slices allow responses to specific conditions in each time period, and the data correction for each time slice is based on the most relevant environmental factors in that time period. This not only improves the accuracy of data correction, but also makes the entire monitoring process more in line with actual application needs; by accurately monitoring the evaporation data of each time slice, resource managers can better understand the intraday dynamics of water and optimize the allocation and use of water resources. For example, accurate evaporation data can help decision makers develop more effective irrigation strategies and reduce water waste.

[0056] In any of the above embodiments, in each first time period, the evaporation data in units of the first time period and the secondary data of z second time periods are transmitted between the input node and the judgment node and between adjacent judgment nodes, so as to repeatedly compare the difference relationship between the evaporation data calculated on a smaller time scale and the evaporation data actually measured by the evaporation device.

[0057] In this embodiment, in each first time period (such as a day), the overall evaporation data and the secondary data divided into multiple second time periods (such as every hour) are transmitted between the input node and the judgment node. The purpose of this is to allow the system to repeatedly compare and analyze data at different time scales, and identify the difference between the evaporation data calculated in a shorter time period and the actual measured data; at the meteorological observation station, when the evaporation monitoring system processes the evaporation data of a day, it not only pays attention to the total evaporation of the whole day, but also specifically analyzes the evaporation data of each hour. Such analysis helps researchers understand the evaporation behavior at different times of the day, such as the obvious increase in evaporation at noon due to the increase in temperature.

[0058] By transferring data between adjacent judgment nodes, the system can continuously compare the calculated evaporation data with the evaporation data actually measured by the evaporation equipment. Each judgment node can adjust the data processing strategy based on the comparison results to reduce errors and improve data accuracy; if the judgment node in the morning finds that the actual measured evaporation is lower than the calculated data, it may be because of unexpected low temperatures or rainfall events. The node will adjust the data processing parameters for subsequent time periods to better reflect the actual environmental conditions.

[0059] By combining the analysis of long-term and short-term data, this step can provide a more comprehensive data assessment, helping the monitoring system to respond quickly when deviations are found, adjusting the data model or correcting errors; the comparison of data in subdivided time periods makes evaporation monitoring not only limited to the assessment of daily totals, but also able to accurately grasp changes in hours or even shorter time periods, providing more accurate data support for water resources management, which is extremely important in applications such as irrigation and climate analysis; this multi-scale data processing method improves the adaptability of the monitoring system to environmental changes, can effectively respond to sudden meteorological changes, and ensure the accuracy and practicality of the data.

[0060] In any of the above embodiments, the judgment threshold includes an environmental factor value that can affect the accuracy of the evaporation data. The environmental factor value can specifically be whether rainfall occurs in the first time period and a preset evaporation value for judging whether the evaporation data is within a reasonable range. The preset evaporation value can specifically be two evaporation values ​​adjusted according to actual conditions and an environmental action time value set by the scale of a time slice. The environmental action time value can be the duration of rainfall in the weather in which the evaporation equipment is located and an error detection value for judging whether the evaporation data deviates before and after correction. The error detection value can specifically be an allowable error range for the evaporation data generated by the evaporation equipment.

[0061] In this embodiment, the environmental factor values ​​cover various environmental variables that may affect the accuracy of evaporation data, such as rainfall. They are used to evaluate the direct impact of environmental changes on evaporation; during data collection, if a rainfall event is monitored, the judgment node will check the duration and intensity of the rainfall. This helps to determine whether the evaporation recorded during this period needs to be adjusted according to the rainfall to reflect the actual impact of rainfall on the evaporation process.

[0062] The preset evaporation values ​​provide a reference for the theoretical evaporation that the evaporation data should reach without significant external influences. They are adjusted based on historical data and environmental settings and are used to compare the deviation between the actual observed values ​​and the expected values; two different evaporation values ​​are set as the benchmark for daily monitoring, one for clear weather and the other for cloudy or windy conditions. This allows a more precise assessment of whether evaporation under specific weather conditions remains within the normal range.

[0063] The environmental action time value takes into account the impact of environmental conditions (such as rainfall duration) on evaporation, helping to determine the actual duration of environmental factors; if the data recorded by the evaporation equipment shows four hours of continuous rainfall on a certain day, this time value will be used to adjust the total evaporation calculation for that day to ensure that the evaporation calculation takes into account the actual impact of rainfall.

[0064] The error detection value sets the maximum error range allowed for evaporation data in the absence of obvious external interference, and is used to evaluate whether the deviation before and after data correction is within an acceptable range; if the preset error range of the evaporation monitoring system is ±5%, any data outside this range needs to be re-evaluated and corrected to ensure the accuracy of the final data.

[0065] By comparing with preset thresholds, outliers in the data can be effectively identified and corrected to ensure data quality; judgment thresholds help the automation system quickly identify data intervals that require special attention and optimize data processing and correction strategies; precise data correction and threshold comparison make evaporation data more reliably used in complex decision-making processes such as water resources management, agricultural planning and meteorological analysis.

[0066] In any of the above embodiments, the environmental factor value, the preset evaporation value, the environmental action time value and the error detection value are sequentially set along the transmission direction of the evaporation data to gradually judge and subdivide the detection conditions of the evaporation data, and characterize and identify them through continuously accumulated labels.

[0067] In the transmission direction, the later judgment threshold affects the earlier judgment threshold. By adjusting the earlier judgment threshold with the later judgment threshold, the earlier coarser range of the detection condition judgment can be guided in the subsequent more detailed detection condition judgment, so as to reduce the computing resources required for the judgment while maintaining the judgment accuracy.

[0068] In this embodiment, when each node receives data, it makes a preliminary judgment on the data according to the threshold of the current node, and the judgment result is used to adjust the data or add a new label. Each step in this process brings deeper insights into the data, making the subsequent data processing more targeted. The data is evaluated as "affected by high temperature" at the first judgment node. As the data is passed to the next node, this label will be used to guide the next stage of processing, which may be to adjust the evaporation calculation or further analyze the specific relationship between temperature and evaporation rate. Through the continuous accumulation of labels, the system can capture the specific characteristics and changes of the data at each stage. These labels are passed along with the data to provide a basis for processing for subsequent nodes. If the data continuously receives the "high temperature impact" and "rainfall" labels within a certain period of time, these labels will guide the final data processing and correction strategy to ensure that the calculation of evaporation takes into account both the actual impact of high temperature and rainfall.

[0069] At different stages of data processing, the judgment thresholds set by previous nodes are adjusted through more refined or updated information obtained by subsequent nodes. This feedback mechanism from back to front enables the judgment in the initial stage to be optimized based on the results of subsequent analysis, thereby more accurately reflecting the actual evaporation situation and reducing unnecessary repeated calculations; in an integrated meteorological monitoring system, the initial stage may be based on preset evaporation values ​​to quickly screen out data points that may be abnormal. As data is passed to subsequent nodes, more detailed environmental analysis may indicate that some data initially marked as abnormal is actually caused by rare but reasonable meteorological events. At this point, the system can adjust the thresholds in the initial stage to avoid mislabeling such events as abnormalities in the future, reduce false alarm rates, and optimize the overall data processing process.

[0070] Furthermore, the later judgment threshold is adjusted to the earlier judgment threshold, and is calculated using the following formula:

[0071]

[0072] In the formula, is the judgment threshold of the next judgment node; is the judgment threshold of the previous judgment node; To adjust the coefficient, set or adjust it according to actual needs; is the rate of change of environmental factors E (such as temperature and humidity), that is, the first-order derivative with respect to time t; is the acceleration of change of environmental factors, that is, the absolute value of the second-order derivative with respect to time, which is used to capture the drastic changes of environmental factors; It is a natural logarithmic function, which is used to smooth the impact of environmental factors and prevent excessive fluctuations; is the relative change rate of evaporation data, where ; It is the evaporation data of the next and previous judgment nodes.

[0073] As can be seen from the above, the basic threshold adjustment Based on the threshold of the previous judgment node, it provides a starting point for subsequent adjustments; the first-order derivative of environmental factors To reflect the instantaneous change speed of environmental factors, when environmental factors change rapidly, the threshold is appropriately adjusted to improve the system's sensitivity to environmental changes; the second-order derivative of environmental factors In order to capture the acceleration of environmental factor changes, that is, the change in the rate of change, a natural logarithmic function is used for smoothing to avoid excessive adjustment of the threshold due to drastic changes; Assess the degree of change in the evaporation data itself, and when the evaporation data changes significantly, adjust the threshold to adapt to the new data trend.

[0074] Specifically, the steps of performing m corrections on the evaporation data according to the labels include:

[0075] In the current first time period, the evaporation data is calculated using the secondary data of z second time periods, where the secondary data is the evaporation data in each subdivided second time period in the first time period.

[0076] The evaporation data input by the input node and the calculated evaporation data are compared and screened. Through the final comparison and screening, the calculated data after judgment and the data measured under actual conditions can be combined to select the best one, and then the result of the selection is passed to the output node.

[0077] For the above specific description, within a larger time frame (first time period, such as a day), the time is divided into multiple smaller units (second time periods, such as every hour). The evaporation data collected in each such time unit is used to calculate the total evaporation of the entire first time period. Assume that an evaporation monitoring project aims to analyze the evaporation of water bodies in industrial areas in detail. The system is set to a first time period of 24 hours (a day), and the day is further divided into 24 one-hour second time periods. The monitoring equipment records the evaporation data once an hour, and the data of each hour is aggregated to calculate the total evaporation of the whole day.

[0078] Evaluate and compare the measured evaporation data obtained from the input nodes and the predicted evaporation data calculated based on environmental parameters. This comparison aims to identify differences and potential sources of error between the data and evaluate which set of data more accurately reflects the actual evaporation. Through a series of preset algorithms and evaluation criteria, the system is able to determine which set of data is more credible and select it for output; in a reservoir evaporation monitoring project, the monitoring system automatically records the evaporation of the water surface every hour and calculates the theoretical evaporation based on the weather forecast data of the day, such as temperature, humidity, wind speed and sunshine duration. The results of the comparative analysis show that the measured data in certain periods are significantly lower than the calculated values ​​due to sensor failure. The system issues a maintenance alarm based on this and selects the calculated data as a more reliable data source for those periods to maintain the continuity and accuracy of the overall data.

[0079] As can be seen above, by subdividing the time period and calculating the evaporation of each small section, the monitoring system can provide more sophisticated data analysis. This is particularly useful for understanding the dynamics of evaporation under specific conditions, such as analyzing the immediate impact of sunshine, temperature changes or rainfall events on evaporation; making data processing more flexible to adapt to different monitoring needs, such as being able to quickly adjust the monitoring frequency or focus on certain time periods when specific environmental events occur; evaporation data within a time period can help water resource managers better understand and predict evaporation losses in water bodies, thereby formulating more effective water resource management strategies and response measures.

[0080] This step ensures high data accuracy through comprehensive evaluation of measured and calculated data. Selecting the most appropriate data set is crucial to the accuracy of the entire monitoring process, especially when environmental conditions are complex or data collection equipment may be biased; accurate data is the basis for applications such as water resources management, environmental monitoring, and meteorological analysis. By ensuring the accuracy of the data, this step helps improve the quality of decision-making and policy formulation based on these data, making related management and operations more efficient and scientific; by effectively identifying and utilizing the most accurate data sets, this process reduces the waste of additional processing and computing resources caused by data errors. This optimization not only improves the economic efficiency of the system, but also improves its environmental adaptability and operational flexibility.

[0081] Specifically, the step of calculating the evaporation data using the secondary data of the z second time periods includes:

[0082] Identify all tags of the current evaporation data, configure the calculation formula according to the tags, and the calculation formula can be adaptively adjusted according to different detailed detection conditions to adapt to the evaporation data generated under different detection conditions.

[0083] In the first time period, the evaporation data is calculated using the secondary data of z second time periods using the currently configured calculation formula. The evaporation data in the first time period is calculated using the secondary data of a more refined time period. This can avoid the overall detection error caused by a detection error in a smaller time period under a large range of time units.

[0084] In response to the above specific description, the evaporation data received from the input node is analyzed to identify various tags attached to the data, such as "high temperature", "rainfall" or "strong wind". Each tag represents a specific environmental condition during the data collection period. The system dynamically configures or selects appropriate calculation formulas based on these tags to ensure that the calculation of evaporation reflects these specific conditions. If the evaporation monitoring station collects data during a heat wave, the system automatically identifies the "high temperature" tag and selects an adjusted formula to calculate evaporation, which takes into account the increased effect of high temperature on the evaporation rate. In the data processing flow, the system further adjusts the calculation formula based on the refined detection conditions (such as specific weather changes during the time period). This ensures that the calculation formula is flexible and adaptable, and can accurately adapt to and reflect data collected from different environments and conditions. In a climate region with a variety of changes, evaporation monitoring may need to consider sudden weather changes, such as afternoon thunderstorms. The system identifies relevant environmental tags, such as "afternoon rain", and adjusts the formula to accommodate the impact of such short-term but intense weather events on evaporation.

[0085] The first time period (e.g., one day) is divided into multiple second time periods (e.g., every hour), and the calculation formula configured for each small time period is used to calculate the evaporation data of these time periods respectively. Such a configuration ensures that the calculation model can adapt to the environmental variables and conditions unique to each time period. In the evaporation monitoring of water bodies in a temperate climate zone, the temperature changes significantly at night and during the day. The system configures different calculation formulas according to different environmental conditions (such as temperature and sunshine) during the day and night. In this way, the evaporation at night and every hour during the day can be calculated separately, and the total evaporation for the whole day can be estimated more accurately. By subdividing the data of a large time unit (such as a whole day) into small time units (such as every hour), the evaporation of each small unit is calculated, and the total evaporation is summarized to obtain the overall evaporation. This method effectively avoids the accuracy of the evaporation data of the overall time period due to data anomalies or errors in a small time period. A short equipment failure occurred at a monitoring site, affecting the data recording for two hours in the afternoon. Because the system adopts the data calculation method of subdividing time periods, the abnormal data of this time period is isolated and corrected, avoiding its negative impact on the calculation of evaporation for the whole day.

[0086] As can be seen from the above, through this tag-based dynamic calculation formula adjustment, the system can process and analyze evaporation data more accurately, ensuring that the output data reflects the actual situation as much as possible; as environmental conditions change, the system can be flexibly adjusted, which is of great significance for responding to climate change and environmental fluctuations; accurate evaporation data is essential for water resource management, agricultural planning and environmental monitoring. By adaptively adjusting the calculation formula, the monitoring system provides reliable data support to help managers make better decisions.

[0087] By calculating and accumulating evaporation for each sub-time period, this method improves the overall accuracy and reliability of monitoring data, especially under changing environmental conditions; the calculation of sub-time periods allows the system to quickly identify and correct local data anomalies, thereby protecting the integrity and accuracy of the overall data; accurate evaporation data is the key to water resource management and environmental monitoring. By ensuring high data accuracy, the system provides reliable support to help managers and decision makers make more scientific decisions.

[0088] Specifically, the steps of comparing and screening the evaporation data input by the input node and the calculated evaporation data include:

[0089] According to the error range of the evaporation equipment for obtaining the evaporation data, or according to the actual monitoring process effect, adaptive adjustment is performed, and the current judgment node sets the value of the judgment threshold.

[0090] The absolute value of the difference between the input evaporation data and the calculated evaporation data is calculated so that only the difference in the values ​​is discussed here.

[0091] It is determined whether the absolute value of the difference is less than the judgment threshold. If so, it is proved that the actually measured data is more accurate and the input evaporation data is retained. If not, it is proved that the data corrected by calculation is more accurate and the calculated evaporation data is retained.

[0092] In view of the above specific description, the technical specifications of the evaporation equipment, especially its error range, are analyzed, and the current effect of the monitoring process is evaluated. Based on this information, the thresholds of the judgment nodes are appropriately adjusted to ensure the accuracy and reliability of data analysis. This adaptive adjustment helps to deal with the impact of inherent errors in the equipment and optimize the monitoring process to adapt to changes in actual operations. In a water evaporation monitoring project, the evaporation pan used has an error range of ±5%. The system administrator sets the judgment threshold based on this error range to ensure that the evaporation calculation reflects the actual situation even in the case of the maximum error. At the same time, if the monitoring data shows that the results of consecutive days are significantly different from the expected model, the threshold will be fine-tuned based on these feedbacks to adapt to possible environmental changes or equipment performance changes. At each judgment node, the threshold is set based on the analysis results of the above error range and monitoring effect. These thresholds determine when the data is considered abnormal and requires further analysis or correction. Correct threshold setting is the basis for ensuring data quality and effective operation of the system. At an environmental monitoring station, the threshold of the judgment node was originally set to trigger an alarm when the evaporation of the day differed from the previous day by more than 10%. However, after evaluating the effectiveness of the actual monitoring process, it was found that this threshold was too sensitive on days with high humidity, resulting in frequent false alarms. Therefore, the threshold was adjusted and the difference threshold under high humidity conditions was set to 15% to reduce the false alarm rate.

[0093] Calculate the absolute value of the difference between the actual measured evaporation data and the evaporation data calculated by the model or algorithm. By taking the absolute value, the focus is on the size of the difference, regardless of the direction of the difference (positive or negative). Suppose the actual evaporation data entered is 15 mm and the calculated evaporation data is 12 mm. The difference is 15 mm minus 12 mm, which is 3 mm. Taking its absolute value, the size of the difference is 3 mm. Regardless of which value is larger, the absolute value of the difference always reflects the numerical difference between the two. By focusing only on the absolute value of the difference, this statement is intended to simplify the analysis process and focus the discussion on the size of the numerical difference, regardless of whether the difference is positive or negative. This helps to unify the assessment of the degree of difference between the actual data and the calculated data. Regardless of whether the calculated value is 3 mm lower or 3 mm higher than the actual value, the absolute value of the difference is 3 mm. In this way, analysts can directly judge whether the data is within the acceptable error range based on the size of the difference, without considering the direction of the difference.

[0094] First, the absolute value of the difference between the actual measured evaporation data and the calculated corrected evaporation data is calculated, and the calculated absolute value of the difference is compared with the pre-set judgment threshold to determine whether the difference between the actual measured data and the calculated corrected data is within an acceptable error range; for example, the actual measured data: 15 mm, the calculated corrected data: 14.7 mm, the absolute value of the difference: |15 - 14.7| = 0.3 mm, the judgment threshold: 0.5 mm, the comparison result: 0.3 mm < 0.5 mm, the absolute value of the difference is less than the judgment threshold. If the absolute value of the difference is less than the judgment threshold, it means that the difference between the actual measured data and the calculated corrected data is small, and the actual measured data is considered to be more accurate. In this case, the system chooses to retain the actual measured evaporation data as the final result for subsequent analysis and decision-making. The absolute value of the difference is 0.3 mm, which is less than the judgment threshold of 0.5 mm. The actual measured evaporation data of 15 mm is retained, and the accuracy of the actual measured data meets the requirements and has high credibility. If the absolute value of the difference is greater than or equal to the judgment threshold, it means that the actual measured data may have errors or inaccuracies, and the calculated and corrected data is considered to be more reliable. In this case, the system will discard the actual measured data and retain the calculated and corrected evaporation data as the final result. An example is the actual measured data: 15 mm, calculated and corrected data: 13 mm, absolute value of the difference: |15 - 13| = 2 mm, judgment threshold: 0.5 mm, comparison result: 2 mm ≥ 0.5 mm, the absolute value of the difference is greater than the judgment threshold, and the calculated and corrected evaporation data of 13 mm is retained. The actual measured data has a large deviation, which may be affected by equipment failure or environmental interference.

[0095] As can be seen from the above, by adaptively adjusting the threshold, the system can more accurately reflect the actual evaporation situation, while quickly responding to changes in equipment performance and environmental conditions; accurate threshold setting reduces unnecessary data processing and resource waste, ensuring the economic efficiency of monitoring activities. In addition, it also provides reliable data support to help managers and decision makers make data-based decisions; this adaptive adjustment based on the equipment error range and monitoring effect enables the system to flexibly respond to various challenges and ensure that a high level of operational performance and data accuracy can be maintained in different environments and conditions.

[0096] Objectively evaluate the degree of difference between the actual measured data and the calculated model. By calculating the absolute value of the difference, the system can focus on the size of the numerical difference and determine whether the difference exceeds the preset error threshold, thereby deciding whether further corrections to the data or adjustments to the model are needed. This method improves the efficiency and accuracy of data analysis and provides a reliable basis for subsequent decision-making and optimization.

[0097] By comparing actual measured data and calculated corrected data and evaluating based on judgment thresholds, the system can select more accurate data and improve data quality; it can identify and exclude abnormal data to prevent erroneous information from affecting subsequent analysis and decision-making processes; through automated judgment and selection mechanisms, it reduces human intervention and improves the efficiency and accuracy of data processing; accurate evaporation data is crucial for fields such as water resources management, agricultural irrigation and weather forecasting. This step ensures the reliability of the data and provides a solid foundation for related decisions.

[0098] S104, until the evaporation data reaches the output node, the evaporation data is output as a correction result in the output node in units of the first time period, so that the final output data is unique and the redundancy of the database is avoided.

[0099] Here, the evaporation data finally reaches the output node after being processed and corrected by the input node and multiple judgment nodes. This means that all necessary analysis, correction and verification steps have been completed. In a hydrological monitoring system, the evaporation data is gradually improved from the initial collection through a series of judgment nodes, such as rainfall impact, high temperature correction, etc. When all these steps are completed, the data is transmitted to the output node for final output and storage. The output node summarizes and outputs the evaporation data according to the pre-set first time period (for example, one day). The output data is the final evaporation data after all corrections and verifications, representing the most accurate evaporation in that time period. If the first time period is set to one day, the output node will output the corrected evaporation data of that day at the end of each day. For example, the corrected evaporation of a certain day is determined to be 10 mm, and the output node publishes this value as the final result of that day. By only outputting the unique evaporation data within a time period at the output node, duplicate data is avoided, ensuring that there is only one corresponding evaporation data record for each time period in the database, reducing data storage redundancy and improving database efficiency and reliability; in database design, date is set as the primary key (or unique index), and only one evaporation data record is allowed to be stored every day. If there is a new correction result, the previous data will be overwritten to ensure that there is only one latest and accurate data record under the same date.

[0100] From the above, it can be seen that by uniformly outputting the corrected evaporation data at the output node, it is ensured that the data has been comprehensively analyzed and corrected and has high credibility; the uniqueness of the data is guaranteed, the storage waste and management complexity caused by duplicate data are avoided, and the query efficiency of the database and the simplicity of data management are improved; accurate and unique evaporation data are provided, which provides reliable data support for fields such as water resources management, agricultural irrigation and meteorological research, and facilitates relevant departments and personnel to quickly obtain and apply data.

[0101] In any of the above embodiments, the correction result output by the output node is the evaporation data before correction or the evaporation data after correction, so that the correction result can not only be selected, but also correct data must be output to avoid blanks.

[0102] In this embodiment, the output node has the function of selecting to output the evaporation data before correction or the evaporation data after correction according to the previous comparison and judgment results, so as to ensure the accuracy of the data: by selecting more accurate data, the system improves the quality of the output data and meets the needs of practical applications; Scenario 1: During the data processing, if the difference between the actual measured evaporation data and the calculated corrected data is small (for example, the absolute value of the difference is less than the set judgment threshold), the system determines that the actual measured data is more accurate. Therefore, the output node will output the evaporation data before correction. For example, the actual measured data is 10 mm, and the corrected data is 9.8 mm. The difference is only 0.2 mm, which is less than the threshold of 0.5 mm. The system chooses to output the actual measured data of 10 mm; Scenario 2: If the difference is large (for example, the absolute value of the difference is greater than the judgment threshold), the system believes that the corrected data is more reliable. At this time, the output node will output the corrected evaporation data. For example, the actual measured data is 12 mm, and the corrected data is 9 mm. The difference is 3 mm, which is greater than the threshold of 0.5 mm. The system chooses to output the corrected data of 9 mm.

[0103] Regardless of whether the data before or after correction is selected, the system ensures that there is data available for output at the output node to avoid data missing and improve system reliability: by ensuring that there is always data output, the system avoids analysis interruptions or decision-making difficulties caused by data gaps; in some cases, both the actual measured data and the corrected data may be abnormal, such as sensor failure or extreme weather. Even so, the system will select the data closest to the true value for output according to the preset rules, or use the historical average or default value to ensure that the output node is not blank. For example, if the actual measured data is missing and the corrected data cannot be calculated, the system may output the historical average evaporation for that time period as a substitute.

[0104] By flexibly selecting and outputting the most suitable evaporation data at the output node, the system ensures data continuity and avoids the impact of data interruption on subsequent analysis and decision-making; it allows the system to provide reliable data output in the face of various data anomalies or environmental changes, improving the robustness and adaptability of the system; accurate and continuous evaporation data is essential for water resource management, agricultural irrigation planning, and environmental monitoring. This step ensures that relevant departments and personnel can always obtain the necessary data support and make scientific and effective decisions; by avoiding data blanks, the system reduces uncertainty in data processing and storage, simplifies database management, and improves the efficiency of data query and utilization.

[0105] The present invention provides an evaporation data monitoring and correction method that integrates multiple detection conditions. During the operation of traditional automatic evaporation equipment, it is easy to be affected by equipment failures (such as water replenishment, overflow) and meteorological factors (such as rainfall, strong wind), resulting in a decrease in the accuracy of evaporation data and an inability to reflect the actual evaporation situation in real time. n judgment nodes are set between the input node and the output node, and these nodes are connected in one direction to form a monitoring process for transmitting evaporation data step by step. Each judgment node is responsible for making specific judgments and corrections to the evaporation data. Through multi-level judgment nodes, the data is gradually refined and analyzed and corrected during the transmission process, which improves the accuracy of data processing, effectively identifies and processes abnormal data, and ensures the quality of the final output data.

[0106] Different environmental factors and equipment conditions have different effects on evaporation data. Traditional methods are difficult to identify and distinguish these effects in a timely and accurate manner, resulting in inaccurate data correction. A specific judgment threshold is set at each judgment node to detect the detection conditions (such as rainfall, high temperature, equipment failure, etc.) in which the evaporation data is located. When the evaporation data reaches the judgment node, it is analyzed according to the judgment threshold and the data is labeled accordingly; through the label system, it is clear which factors affect the evaporation data, which is convenient for subsequent targeted corrections. For different working conditions, the system can flexibly adjust the correction strategy, improving the adaptability to complex environments.

[0107] In traditional methods, data correction is often one-off, and it is difficult to fully correct evaporation data affected by multiple factors. Evaporation data is input into the system in units of the first time period (for example, one day). During the data transmission process, m corrections (m ≥ 1) are performed on the evaporation data according to the labels that have been marked, and each correction is adjusted for specific detection conditions; through multiple rounds of corrections, the errors caused by various factors are gradually eliminated to ensure the accuracy of the data. The first time period can be further subdivided to improve the ability to capture changes in data in different time periods.

[0108] The data processing process is complex. If there is no clear output strategy, it may lead to data redundancy or omission, affecting the validity of the data. At the output node, the evaporation data after m corrections is output in the first time period as the final correction result. The output data can be the original data before correction or the corrected data, depending on which one is more accurate; ensure that there is only one final evaporation data in each time period to avoid data redundancy. No matter how many times the data is corrected, accurate data can be output in the end to avoid data gaps and ensure the continuity of subsequent analysis and decision-making.

[0109] Another embodiment of the first aspect of the present invention proposes a monitoring process based on the evaporation data monitoring and correction method set by integrating multiple detection conditions. In some embodiments of the present invention, Figure 2 As shown, the monitoring process includes the following steps:

[0110] S401, inputting evaporation data in units of a first time period at an input node, and copying the evaporation data; setting the time slice to one hour, cutting the first time period by the time slice and distributing the copied evaporation data.

[0111] S402, at the first judgment node, judge whether the detection condition is a rainy day, and the judgment threshold is a rainfall of 2.0 mm; if not, execute S403, and if so, execute S409.

[0112] S403, at the second judgment node, it is judged that the detection condition is that the detection equipment is affected and an abnormal value is generated, the first threshold is the lower limit and the specific value is -0.2mm, the second threshold is the upper limit and the specific value is adjusted according to the actual use, and it is judged whether the evaporation data in all the second time period is between the first threshold and the second threshold. If so, there is no abnormal hour and S404 is executed, otherwise there is an abnormal hour and S406 is executed.

[0113] S404, calculating the evaporation value in the first time period, specifically: daily evaporation calculation = liquid level at 8 o'clock yesterday - liquid level at 8 o'clock today + yesterday's rainfall - yesterday's overflow.

[0114] S405, at the third judgment node, determine the difference between the calculated daily evaporation data and the input evaporation data, and determine whether the absolute value of the difference is less than 0.2 mm. If so, select the input evaporation data; otherwise, select the calculated daily evaporation data; execute S413.

[0115] S406, at the fourth judgment node, determine whether the total duration of abnormal hours is greater than 12 hours, if not, execute S407, if yes, execute S408.

[0116] S407, at the fifth judgment node, determine the relationship between the evaporation data in the second time period and the first threshold and the second threshold; when the evaporation data in the second time period is not less than the second threshold, the daily evaporation calculation = abnormal period evaporation + normal period evaporation, normal period evaporation calculation = normal period starting liquid level - normal period ending liquid level + rainfall - overflow, abnormal period evaporation calculation = abnormal period hours * normal period evaporation intensity; when the evaporation data in the second time period is not greater than the first threshold, normal period evaporation calculation = normal period starting liquid level - normal period ending liquid level + rainfall - overflow, abnormal period evaporation calculation = 0; return to S405.

[0117] S408, interpolate with reference to the evaporation at the current station or the evaporation of the previous day; return to S405.

[0118] S409, at the sixth judgment node, it is judged that the detection condition is that the detection equipment is affected and an abnormal value is generated, the first threshold is the lower limit and the specific value is -0.2mm, the second threshold is the upper limit and the specific value is adjusted according to the actual use, and it is judged whether the evaporation data in all the second time period is between the first threshold and the second threshold. If so, there is no abnormal hour and S410 is executed, otherwise there is an abnormal hour and S412 is executed.

[0119] S410, calculate the evaporation value in the first time period, specifically: daily evaporation calculation = accumulated value of evaporation in rainless period hour + accumulated value of evaporation in rainy period hour, accumulated value of evaporation in rainless period hour = starting liquid level in rainless period - ending liquid level in rainless period, accumulated value of evaporation in rainy period hour = initial liquid level in hour - final liquid level in hour + rainfall - overflow; return to S405.

[0120] S411, at the seventh judgment node, the difference between the calculated daily evaporation data and the input evaporation data is judged, and whether the absolute value of the difference is less than 0.2 mm. If so, the input evaporation data is selected, otherwise the calculated daily evaporation data is selected; return to S405.

[0121] S412, determine whether the total duration of abnormal hours is greater than 12 hours, if not, execute S407, if yes, execute S408.

[0122] S413, outputting the evaporation data at the output node.

[0123] The monitoring process provided by the present invention, first, in step S401, the system inputs evaporation data in units of a first time period (e.g., one day) at the input node, and copies it. By setting the time slice to one hour, the first time period is cut into multiple smaller time periods, and the copied evaporation data is distributed to these time periods. This refinement makes data analysis more accurate, can capture subtle changes in evaporation within a day, and improves the flexibility and accuracy of data processing.

[0124] Next, in steps S402 to S405, the system sets multiple judgment nodes and thresholds. For example, S402 determines whether it is a rainy day (the threshold is 2.0 mm rainfall). If it is not a rainy day, it continues to determine whether the equipment is affected and abnormal values ​​are generated. In S403, by setting upper and lower thresholds, it is determined whether the evaporation data is within the normal range. If the data is normal, the system calculates the daily evaporation according to a specific formula in S404, and compares the difference between the calculated value and the input value in S405, and selects more accurate data output. These steps ensure the accuracy of the data, can identify and correct abnormal data in a timely manner, and avoid the impact of erroneous data on the analysis results.

[0125] For abnormal data, the system adopts different processing strategies in steps S406 to S408. If the total duration of abnormal hours is less than 12 hours, the system calculates the evaporation of the abnormal period and the normal period respectively in S407 and makes corrections; if the abnormal hours exceed 12 hours, the system interpolates the evaporation data of the adjacent stations or the evaporation of the previous day in S408. This flexible processing method ensures the integrity and continuity of the data and avoids the impact of missing or abnormal data on the overall analysis.

[0126] In the case of rainy days, the system performs similar processing through steps S409 to S412 to ensure that the evaporation data is reliable even under complex meteorological conditions. Finally, the system outputs the corrected evaporation data at the output node in step S413 to ensure the uniqueness and accuracy of the data.

[0127] In summary, this technical solution improves the accuracy and reliability of evaporation data by refining the time scale, setting multiple judgment nodes and thresholds, flexibly handling abnormal data, and selecting the optimal data output. The system can adapt to various complex environments and equipment conditions, ensure the integrity and continuity of data, provide solid data support for subsequent water resources management, agricultural irrigation decision-making and meteorological research, and improve the efficiency and quality of overall data monitoring and processing.

[0128] Specifically, the types of tags include: whether it is a rainy day, whether the evaporation data in the second time period is abnormal, and whether the abnormal time is greater than 12 hours.

[0129] The embodiment of the second aspect of the present invention provides a system for implementing the evaporation data monitoring and correction method in any of the above embodiments, such as Figure 3 As shown, the system 2 includes:

[0130] The monitoring process setting module 201 is used to construct a monitoring process, set n judgment nodes between the input node and the output node, and then connect the judgment nodes.

[0131] The threshold and label updating module 202 is used to set different judgment thresholds at each judgment node, and to judge the detection conditions under which the evaporation data is generated by the judgment thresholds, and to label the evaporation data by the detection conditions.

[0132] The evaporation data transmission module 203 is used to transmit the evaporation data between the judgment nodes and perform m corrections on the evaporation data according to the labels.

[0133] The correction and output module 204 is used to obtain the evaporation data at the output node and output it as a correction result.

[0134] The system 2 provided by the present invention, the monitoring process setting module is responsible for establishing the process framework of the entire evaporation data monitoring and correction, including input nodes, output nodes and intermediate judgment nodes, and n judgment nodes are set: n judgment nodes are inserted between the input nodes and the output nodes, and these nodes are used to perform multi-level judgment and correction on the evaporation data. In a unidirectional connection manner, each judgment node is connected in sequence to form a structure for data transmission step by step; in an actual application, the system may set up 3 judgment nodes, which are used to detect rainfall, temperature anomalies and equipment failures respectively. The monitoring process setting module provides an infrastructure for the operation of the entire system, ensures that data can be transmitted between nodes according to a predetermined path, and provides an operating platform and channel for the threshold and label update module, the evaporation data transmission module, and the correction and output module.

[0135] The threshold and label update module sets a specific judgment threshold on each judgment node to identify the detection condition of the evaporation data. According to the set judgment threshold, the current evaporation data is analyzed to determine the environment or equipment condition (such as rainfall, high temperature, equipment abnormality, etc.) in which it is located. According to the detection condition, the corresponding label is added to the evaporation data to facilitate subsequent correction processing; the rainfall threshold is set to 0.5 mm. When the rainfall in the time period corresponding to the evaporation data exceeds the threshold, the "rainfall impact" label is marked. The temperature threshold is set to 35°C. When the temperature exceeds this value, the "high temperature impact" label is marked. Within the framework set by the monitoring process, the threshold and label update module runs on each judgment node to ensure that each node has a corresponding judgment threshold and label mechanism. The label information will be transmitted along with the evaporation data to guide subsequent correction operations.

[0136] The evaporation data transmission module is responsible for transmitting evaporation data between each judgment node to ensure the smoothness of data flow. m corrections: According to the label carried by the evaporation data, m corrections (m≥1) are performed on the evaporation data during the transmission process. Each correction is for a specific detection condition. After each correction, the value of the evaporation data and possible labels are updated to prepare for the next judgment and correction. The first correction: According to the "rainfall impact" label, the evaporation data error caused by rainfall is corrected. The second correction: According to the "high temperature impact" label, the evaporation data is adjusted to consider the impact of high temperature on evaporation. According to the label information, decide how to correct the evaporation data. After completing all corrections, the final evaporation data is passed to the correction and output module.

[0137] The correction and output module receives the evaporation data that has been corrected for multiple times at the output node, and outputs the final evaporation data as the correction result for subsequent analysis, storage or application, and outputs the corrected evaporation data, for example, the corrected evaporation amount on a certain day is 8 mm; receives the final data from the evaporation data transmission module; as the last step of data output, ensures that the output data is unique and accurate to avoid data redundancy or omission.

[0138] Embodiments of the third aspect of the present invention provide electronic equipment. In some embodiments of the present invention, such as Figure 4 As shown, an electronic device is provided, which includes: electronic devices such as desktop computers, notebooks, handheld computers, and cloud servers. The electronic device 3 may include but is not limited to a processor 301 and a memory 302. Those skilled in the art will understand that Figure 4 The electronic device 3 is merely an example and does not limit the electronic device 3 , and may include more or less components than those shown in the figure, or different components.

[0139] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0140] The memory 302 may be an internal storage unit of the electronic device 3, for example, a hard disk or memory of the electronic device 3. The memory 302 may also be an external storage device of the electronic device 3, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 3. The memory 302 may also include both an internal storage unit of the electronic device 3 and an external storage device. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0141] The embodiment of the fourth aspect of the present invention proposes a computer-readable storage medium. In some embodiments of the present invention, a computer-readable storage medium is provided, and when the computer-readable storage medium is executed by the processor 301, the steps of the above method are implemented. Therefore, the computer-readable storage medium provided by the fourth aspect of the present invention has all the technical effects of the above steps, which will not be repeated here.

[0142] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0143] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0144] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0145] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0146] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should all be included in the protection scope of the present disclosure.

Claims

1. A method for monitoring and correcting evaporation data integrating multiple detection conditions, characterized in that: The steps include: Constructing a monitoring process including an input node and an output node, wherein the input node and the output node are connected by setting n judgment nodes; the judgment nodes are unidirectionally connected to each other to transmit the evaporation data step by step; At each judgment node, a judgment threshold that can influence each other is set considering the environmental factors changing over time, so that when the evaporation data reaches the judgment node, the detection condition under which the evaporation data is generated is judged by the judgment threshold, and the evaporation data is labeled by the detection condition; the later judgment threshold affects the earlier judgment threshold through the following formula: In the formula, is the judgment threshold of the next judgment node; is the judgment threshold of the previous judgment node; To adjust the coefficient, set or adjust it according to actual needs; is the rate of change of the environmental factor E, that is, the first-order derivative with respect to time t; is the acceleration of change of environmental factors, that is, the absolute value of the second-order derivative with respect to time, which is used to capture the drastic changes of environmental factors; It is a natural logarithmic function, which is used to smooth the impact of environmental factors and prevent excessive fluctuations; is the relative change rate of evaporation data, where ; It is the evaporation data of the next and previous judgment nodes; The evaporation data acquired in units of a first time period is inputted into the input node; during the evaporation data being transmitted between the judgment nodes, m corrections are performed on the evaporation data according to the label; Until the evaporation data reaches the output node, the evaporation data is output as a correction result at the output node in units of the first time period.

2. The evaporation data monitoring and correction method according to claim 1, characterized in that: After the evaporation data is transmitted from the input node, the evaporation data monitoring and correction method further includes: Setting a time slice of a scale, dividing the first time period into z second time periods by the time slice; The evaporated data is copied and distributed according to the second time periods to obtain distributed secondary data in each of the second time periods.

3. The evaporation data monitoring and correction method according to claim 2, characterized in that: In each of the first time periods, the evaporation data in units of the first time period and the secondary data in z second time periods are transmitted between the input node and the determination node and between adjacent determination nodes.

4. The evaporation data monitoring and correction method according to claim 2, characterized in that: The judgment threshold includes an environmental factor value that can affect the accuracy of the evaporation data, a preset evaporation value for judging whether the evaporation data is within a reasonable range, an environmental action time value set by the scale of the time slice, and an error detection value for judging whether the evaporation data deviates before and after correction.

5. The evaporation data monitoring and correction method according to claim 4, characterized in that: The environmental factor value, the preset evaporation value, the environmental action time value and the error detection value are sequentially arranged along the transmission direction of the evaporation data; Along the transmission direction, the later judgment threshold affects the earlier judgment threshold.

6. The evaporation data monitoring and correction method according to claim 2, characterized in that: The step of performing m corrections on the evaporation data according to the label comprises: In the current first time period, calculating the evaporation data by using the secondary data of z second time periods; The evaporation data inputted by the input node and the calculated evaporation data are compared and screened, and transmitted to the output node according to the screening result.

7. The evaporation data monitoring and correction method according to claim 6, characterized in that: The m is an integer not less than zero.

8. The evaporation data monitoring and correction method according to claim 6, characterized in that: The step of calculating the evaporation data using the secondary data of z second time periods comprises: Identify all the tags of the current evaporation data, and configure a calculation formula according to the tags; In the first time period, the evaporation data is calculated using the secondary data of z second time periods using the calculation formula currently configured.

9. The evaporation data monitoring and correction method according to claim 6, characterized in that: The step of comparing and screening the evaporation data input by the input node and the calculated evaporation data comprises: According to the error range of the evaporation equipment for acquiring the evaporation data, setting the value of the judgment threshold at the current judgment node; Calculate the absolute value of the difference between the input evaporation data and the calculated evaporation data; It is determined whether the absolute value of the difference is less than the determination threshold, if so, the input evaporation data is retained, otherwise, the calculated evaporation data is retained.

10. The evaporation data monitoring and correction method according to any one of claims 1 to 9, characterized in that: The correction result output by the output node is the evaporation data before correction or the evaporation data after correction.

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