A method and system for diagnosing abnormality of surface water automatic monitoring data

Abstract

This application discloses a method and system for diagnosing anomalies in automatic surface water monitoring data, relating to the technical field of water environment monitoring. The method comprises the following steps: S1: receiving raw data and preprocessing the raw data to obtain monitoring data; S2: inputting the monitoring data into a pre-built data prediction model, which generates predicted data; S3: receiving collected data and preprocessing the collected data to obtain actual data; and S4: inputting both the predicted data and the actual data into the anomaly diagnosis model, which generates and transmits a data diagnosis result. This application can improve the efficiency of data anomaly diagnosis and the accuracy of data anomaly diagnosis analysis.

Description

Technical Field

[0001] The present application relates to the technical field of water environment monitoring, and in particular to a method and system for diagnosing abnormalities in automatic surface water monitoring data. Background Art

[0002] Surface water automatic monitoring data is obtained by real-time or regular testing of various water quality indicators of surface water bodies (such as rivers, lakes, and reservoirs) through automated monitoring equipment and systems. This data generally includes but is not limited to the following aspects:

[0003] 1. Physical indicators: such as water temperature, turbidity and conductivity.

[0004] 2. Chemical indicators: such as dissolved oxygen, pH value, chemical oxygen demand (COD), ammonia nitrogen, total phosphorus and total nitrogen.

[0005] 3. Heavy metal content: such as lead, mercury, cadmium and chromium.

[0006] 4. Various indicators of rainfall data.

[0007] Surface water automatic monitoring data, as the basic data for evaluating water environment conditions, can timely reflect the water quality status and changing trends of surface water bodies, thus providing important basis and support for water resource protection, water pollution prevention and control, and water environment management. Therefore, the accuracy of surface water automatic monitoring data is particularly important.

[0008] However, the existing abnormality diagnosis methods for automatic surface water monitoring data have the following problems:

[0009] (1) Before performing anomaly diagnosis on surface water automatic monitoring data, the abnormal data must be identified. After identification, the identified data can be diagnosed using existing models, such as the existing supervised abnormal data classification model. However, the amount of data from automatic stations is large, identification is time-consuming and labor-intensive, and the efficiency of abnormal diagnosis is low.

[0010] (2) The existing anomaly diagnosis method for surface water automatic monitoring data cannot perform anomaly diagnosis on both single-point data and continuous sequence data, and the accuracy of data anomaly diagnosis analysis is low. Summary of the Invention

[0011] The purpose of this application is to provide a method and system for diagnosing anomalies in automatic surface water monitoring data, which can improve the efficiency of data anomaly diagnosis and the accuracy of data anomaly diagnosis analysis.

[0012] To achieve the above-mentioned purpose, the present application provides a method for diagnosing abnormalities in automatic surface water monitoring data, comprising the following steps: S1: receiving raw data, and preprocessing the raw data to obtain monitoring data; wherein the raw data includes: at least one section data, each section data includes: a section coordinate and N groups of index data, each group of index data includes: a previous time node and multiple indicators; the monitoring data includes: at least one noise reduction section data, each noise reduction section data includes: a section coordinate and N groups of noise reduction index data, each group of noise reduction index data includes: a previous time node and multiple noise reduction indicators; one section data corresponds to one noise reduction section data, and the section coordinates of the section data are the same as the section coordinates of the corresponding noise reduction section data; S2: inputting the monitoring data into a pre-constructed data prediction model, and generating predicted data by the data prediction model; wherein the predicted data includes: at least one predicted section data, each predicted section data includes: a Section coordinates and M groups of prediction index data, each group of prediction index data includes: a prediction time node and multiple prediction indicators; one denoised section data corresponds to one predicted section data, and the section coordinates of the denoised section data are the same as the section coordinates of the corresponding predicted section data; S3: receiving the collected data, and preprocessing the collected data to obtain actual data; wherein, the actual data includes: at least one actual section data, each actual section data includes: a section coordinate and M groups of actual index data, each group of actual index data includes: a subsequent time node and multiple actual indicators; one predicted section data corresponds to one actual section data, and the section coordinates of the predicted section data are the same as the section coordinates of the corresponding actual section data; S4: inputting both the predicted data and the actual data into the abnormal diagnosis model, and the abnormal diagnosis model generates a data diagnosis result and sends it; wherein, the data diagnosis result includes: at least one sub-diagnosis result, and the sub-diagnosis result is data abnormality or data normal.

[0013] As above, the sub-steps of receiving the original data and preprocessing the original data to obtain the monitoring data are as follows: S11: Read the original data. If there is only one section data in the original data, set the preprocessing sequence number of the section data to 1, and use the section data as the current section data to execute S13; if there are multiple section data in the original data, generate a preprocessing sequence number for each section data in a random order, and the preprocessing sequence number of the section data with an earlier random order is smaller than the preprocessing sequence number of the section data with a later random order, and execute S12; S12: Use the section data with the smallest preprocessing sequence number as the current section data, and execute S13; S13: Sort the N groups of indicator data in the current section data in the order from front to back of the previous time node, and generate a noise reduction sequence number for each group of indicator data. No., the denoising sequence number of a group of indicator data at the previous time node is smaller than the denoising sequence number of a group of indicator data at the previous time node, execute S14; S14: construct the original data sequence of N groups of indicator data in the order of denoising sequence number from small to large, perform denoising on the original data sequence, obtain denoised section data after completing denoising, execute S15; S15: use the total number of section data in the original data to judge the preprocessing sequence number of the current section data, if the total number of section data in the original data is greater than the preprocessing sequence number of the current section data, then remove the preprocessing sequence number of the current section data, execute S12; if the total number of section data in the original data is equal to the preprocessing sequence number of the current section data, then remove the preprocessing sequence number of the current section data, and use all denoised section data as monitoring data.

[0014] As above, wherein the monitoring data is input into a pre-built data prediction model, and the sub-steps of generating prediction data by the data prediction model are as follows: S21: Read the monitoring data. If there is one noise reduction section data in the monitoring data, the prediction task sequence number of the noise reduction section data is set to 1, and the noise reduction section data is used as the current data to be predicted, and S23 is executed; if there are multiple noise reduction section data in the monitoring data, a prediction task sequence number is generated for each noise reduction section data in a random order, and the prediction task sequence number of the noise reduction section data with an earlier random order is smaller than the prediction task sequence number of the noise reduction section data with a later random order, and S22 is executed; S22: The noise reduction section data with the smallest prediction task sequence number is used as the current data to be predicted, and S23 is executed; S23: Construct M prediction time nodes for each group of noise reduction index data according to the total number N of previous time nodes of each group of noise reduction index data of the current data to be predicted, and S24 is executed; wherein, or S24: In order from front to back of the previous time node, the N groups of noise reduction index data in the current prediction data and the M prediction time nodes of each group of noise reduction index data are input into the pre-built data prediction model in sequence, and the data prediction model generates multiple prediction indicators for each prediction time node of each group of noise reduction index data, and constitutes M groups of prediction index data, and the M groups of prediction index data and the corresponding section coordinates are used as a prediction section data, and S25 is executed, wherein a group of prediction index data includes: a prediction time node and multiple prediction indicators corresponding to the prediction time node; S25: The prediction task number of the current data to be predicted is judged by the total number of noise reduction section data in the monitoring data. If the total number of noise reduction section data in the monitoring data is greater than the prediction task number of the current data to be predicted, the prediction task number of the current data to be predicted is eliminated, and S22 is executed; if the total number of noise reduction section data in the monitoring data is equal to the prediction task number of the current data to be predicted, the prediction task number of the previous data to be predicted is eliminated, and all the prediction section data are used as the prediction data.

[0015] As above, the sub-steps of receiving the collected data and preprocessing the collected data to obtain the actual data are as follows: S31: Read the collected data. If there is only one collected section data in the collected data, set the preprocessing sequence number of the collected section data to 1, and use the collected section data as the current collected section data, and execute S33; If there are multiple collected section data in the collected data, generate a preprocessing sequence number for each collected section data in a random order, and the preprocessing sequence number of the collected section data with an earlier random order is smaller than the preprocessing sequence number of the collected section data with a later random order, and execute S32; S32: Use the collected section data with the smallest preprocessing sequence number as the current collected section data, and execute S33; S33: Sort the M groups of collected index data in the current collected section data in order from front to back at the later time node, and generate a noise reduction sequence number for each group of collected index data No., the denoising sequence number of a group of acquisition index data before the subsequent time node is less than the denoising sequence number of a group of acquisition index data after the subsequent time node, execute S34; S34: construct the original acquisition data sequence according to the denoising sequence number from small to large, perform denoising on the original acquisition data sequence, obtain the actual section data after completing the denoising, execute S35; S35: use the total number of acquisition section data in the acquisition data to judge the preprocessing sequence number of the current acquisition section data, if the total number of acquisition section data in the acquisition data is greater than the preprocessing sequence number of the current acquisition section data, then remove the preprocessing sequence number of the current acquisition section data, and execute S32; if the total number of acquisition section data in the acquisition data is equal to the preprocessing sequence number of the current acquisition section data, then remove the preprocessing sequence number of the current acquisition section data, and use all the actual section data as the actual data.

[0016] As above, both the predicted data and the actual data are input into the abnormal diagnosis model, and the sub-steps of generating the data diagnosis result by the abnormal diagnosis model are as follows: S41: Read the predicted data and the actual data. If there is one predicted section data in the predicted data and one actual section data in the actual data, then the predicted section data and the actual section data are used as the current diagnosis data, and the sequence number of the current diagnosis data to be diagnosed is set to 1, and S43 is executed; if there are multiple predicted section data in the predicted data and multiple actual section data in the actual data, then the predicted section data and the actual section data with the same section coordinates are used as a group of data to be diagnosed, and each group of data to be diagnosed is randomly assigned a sequence number. A diagnosis sequence number is generated according to the random sequence, and the diagnosis sequence number of the group of diagnosis data in the front of the random sequence is less than the diagnosis sequence number of the group of diagnosis data in the back of the random sequence, and S42 is executed; S42: the group of diagnosis data with the smallest diagnosis sequence number is used as the current diagnosis data, and S43 is executed; S43: the M groups of prediction index data and the M groups of actual index data in the current diagnosis data are grouped, and the prediction index data and the actual index data with the same prediction time node and the same subsequent time node are used as a group of analysis data, and an analysis sequence number is generated for each group of analysis data in the order from the front to the back of the subsequent time node, and the analysis sequence number of the group of analysis data in the front of the subsequent time node is less than At the analysis sequence number of a group of analysis data at the subsequent time node, execute S44; S44: perform error trend analysis on the M group of analysis data by the first analysis model in the abnormal diagnosis model to obtain a first diagnosis result, wherein the first diagnosis result is: data abnormal or data normal; if the first diagnosis result is that the data is normal, execute S45; if the first diagnosis result is that the data is abnormal, execute S46; S45: analyze the M group of analysis data by the second analysis model in the abnormal diagnosis model to obtain a second diagnosis result, wherein the second diagnosis result is: data abnormal or data normal; S46: generate a sub-diagnosis result according to the first diagnosis result or the second diagnosis result, execute S47; wherein, if the first diagnostic result is data abnormality, the sub-diagnostic result is data abnormality; if the second diagnostic result is data normal, the sub-diagnostic result is data normal; if the second diagnostic result is data abnormality, the sub-diagnostic result is data abnormality; S47: use the total number of current diagnostic data to judge the sequence number of the current diagnostic data to be diagnosed. If the total number of current diagnostic data is greater than the sequence number of the current diagnostic data to be diagnosed, the sequence number of the current diagnostic data to be diagnosed is eliminated, and S42 is executed; if the total number of current diagnostic data is equal to the sequence number of the current diagnostic data to be diagnosed, the sequence number of the current diagnostic data to be diagnosed is eliminated, and all sub-diagnostic results are used as data diagnostic results.

[0017] As above, the first analysis model in the abnormal diagnosis model performs error trend analysis on the M group analysis data, and the sub-steps for obtaining the first diagnosis result are as follows: S441: Obtain the error sequence of each prediction indicator in the M group analysis data by the first analysis model in the abnormal diagnosis model, wherein the error sequence in, The kth prediction indicator in the analysis data with analysis number 1 and the kth actual index The prediction error between For the kth prediction indicator in the analysis data with analysis number 2 and the kth actual index The prediction error between For the kth prediction indicator in the analysis data with analysis number M and the kth actual index The prediction error value between ; k∈[1,K], K is the total number of prediction indicators; S442: the first analysis model in the abnormal diagnosis model performs error trend analysis on the error sequence of each prediction indicator respectively to obtain K first sub-results. If the trend of the error sequence is increasing, the first sub-result is abnormal; if the trend of the error sequence is not increasing, the first sub-result is normal; S443: the K first sub-results are analyzed and a first diagnosis result is generated. If the K first sub-results are all normal, the first diagnosis result is that the data is normal; if there is at least one abnormality among the K first sub-results, the first diagnosis result is that the data is abnormal.

[0018] As above, the total number of prediction indicators K=9; the 9 prediction indicators are: rainfall, rainfall intensity, rainfall duration, rainfall frequency, rainfall distribution uniformity, maximum hourly rainfall, maximum daily rainfall, recurrence period and previous rainfall.

[0019] As above, the second analysis model in the abnormal diagnosis model analyzes the Mth group of analysis data, and the sub-steps for obtaining the second diagnosis result are as follows: S451: using the cross-sectional coordinates of the Mth group of analysis data as the center of the sphere and a preset area radius as the radius to construct a spherical area, and use the spherical area as the target sample area; S452: traversing the sample database according to the target sample area, and using the sample data with overlapping parts between the area range and the target sample area as target sample data, wherein each target sample data includes: input data, output data and area range; input data includes: multiple input sample indicators; output data includes: multiple sample standard indicators; multiple input sample indicators correspond to multiple sample standard indicators in a one-to-one manner; multiple sample standard indicators correspond to multiple prediction indicators in a one-to-one manner; S453: inputting the input data of the target sample data into the data prediction model, and generating prediction sample data by the data prediction model, each prediction sample data includes: Multiple prediction sample indicators; S454: Use the output data of the target sample data to analyze the prediction sample data to obtain the benchmark error of each prediction indicator; S455: Use the benchmark error of each prediction indicator to judge the current error value of each prediction indicator in the M-th group of analysis data, and generate K second sub-results. If the current error value of the k-th prediction indicator is less than or equal to the benchmark error, the second sub-result of the k-th prediction indicator is normal; if the current error value of the k-th prediction indicator is greater than the benchmark error, the second sub-result of the k-th prediction indicator is abnormal; wherein, the current error value of the k-th prediction indicator = the k-th prediction indicator in the M-th group of analysis data - the k-th actual indicator in the M-th group of analysis data; S456: Analyze the K second sub-results and generate a second diagnosis result. If the K second sub-results are all normal, the second diagnosis result is normal data; if there is at least one abnormality in the K second sub-results, the second diagnosis result is abnormal data.

[0020] As above, the benchmark errors of different types of prediction indicators are different, and the expression of the benchmark error of the k-th prediction indicator is: Among them, Bwc k is the benchmark error of the k-th prediction indicator; is the kth prediction sample index in the output data of the vth prediction sample data; is the kth sample standard index in the output data of the vth target sample data, v∈[1,V], V is the total number of target sample data; Δbzc k is the standard deviation of the k-th prediction indicator.

[0021] The present application also provides an abnormality diagnosis system for automatic surface water monitoring data, including: multiple monitoring nodes and a data abnormality diagnosis center; wherein the monitoring nodes are used to send original data; send collected data; and receive data diagnosis results; and the data abnormality diagnosis center is used to execute the above-mentioned abnormality diagnosis method for automatic surface water monitoring data.

[0022] The beneficial effects achieved by this application are as follows:

[0023] (1) The anomaly diagnosis method and system for automatic surface water monitoring data of the present application can directly perform data anomaly diagnosis on the original data without pre-marking the abnormal data, which can effectively improve the efficiency of anomaly diagnosis on a large amount of original data.

[0024] (2) The abnormality diagnosis method and system of surface water automatic monitoring data of the present application can effectively improve the accuracy of data abnormality diagnosis and analysis.

[0025] (3) The abnormality diagnosis method and system of the surface water automatic monitoring data of the present application can perform abnormality diagnosis on single-point data and continuous sequence data.

[0026] (4) The abnormality diagnosis method and system of the surface water automatic monitoring data of the present application can fully consider the correlation between the upstream and downstream section data, and at the same time judge whether the data of multiple sections are abnormal.

[0027] (5) The abnormality diagnosis method and system of the surface water automatic monitoring data of the present application can automatically identify the original data with abnormalities. The timely and accurate detection of abnormal data can provide support for the operation and maintenance of the automatic station and the early detection of pollution incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0029] Figure 1 A schematic structural diagram of an embodiment of an abnormality diagnosis system for automatic surface water monitoring data;

[0030] Figure 2 The present invention is a flowchart of an embodiment of a method for diagnosing abnormalities in automatic surface water monitoring data. DETAILED DESCRIPTION

[0031] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] like Figure 1 As shown, the present application provides an abnormality diagnosis system for automatic surface water monitoring data, including: multiple monitoring nodes 110 and a data abnormality diagnosis center 120.

[0033] The monitoring node 110 is used to send original data, send collected data, and receive data diagnosis results.

[0034] The data anomaly diagnosis center 120 is used to execute the following anomaly diagnosis method for automatic surface water monitoring data.

[0035] Furthermore, the monitoring node 110 is an automatic station, but is not limited to an automatic station. The present application preferably uses an automatic station.

[0036] Furthermore, the data anomaly diagnosis center 120 includes: a pre-processing unit, a prediction unit, an anomaly diagnosis unit and a storage unit.

[0037] Among them, the preprocessing unit is used to receive original data and preprocess the original data to obtain monitoring data; it is used to receive collected data and preprocess the collected data to obtain actual data.

[0038] Prediction unit: used to input monitoring data into a pre-built data prediction model, and the data prediction model generates prediction data.

[0039] Abnormal diagnosis unit: used to input both predicted data and actual data into the abnormal diagnosis model, and the abnormal diagnosis model generates data diagnosis results and sends them.

[0040] Storage unit: used to store a sample database, the sample database includes: multiple sample data, each sample data includes: input data, output data and area range; the area range of each sample data is different.

[0041] like Figure 2 As shown, the present application provides a method for diagnosing abnormalities in surface water automatic monitoring data, comprising the following steps:

[0042] S1: Receive raw data and pre-process the raw data to obtain monitoring data; wherein, the raw data includes: at least one section data, each section data includes: a section coordinate and N groups of index data, each group of index data includes: a previous time node and multiple indicators; the monitoring data includes: at least one noise reduction section data, each noise reduction section data includes: a section coordinate and N groups of noise reduction index data, each group of noise reduction index data includes: a previous time node and multiple noise reduction indicators; one section data corresponds to one noise reduction section data, and the section coordinates of the section data are the same as the section coordinates of the corresponding noise reduction section data.

[0043] Specifically, the previous time node is the time node before the time node at which the monitoring node currently sends the original data. The total number of previous time nodes in each section data set is consistent with the number of groups of indicator data in that section data set. The total number of previous time nodes in each noise reduction section data set is consistent with the number of groups of noise reduction indicator data in that noise reduction section data set.

[0044] Among them, a variety of indicators are used to represent the rainfall data in the data collection area at the previous time node.

[0045] The specific number of types of multiple indicators is set according to actual conditions, and this application preferably uses 9 indicators.

[0046] The specific contents of the various indicators are set according to actual conditions. The various indicators include at least: rainfall, rainfall intensity, rainfall duration, rainfall frequency, rainfall distribution uniformity, maximum hourly rainfall, maximum daily rainfall, recurrence period and previous rainfall.

[0047] Among them, rainfall refers to the total amount of rain that falls within a certain period of time.

[0048] Rainfall intensity is the amount of rainfall per unit time.

[0049] Rainfall duration is the time that rainfall lasts.

[0050] Rainfall frequency is the number of times a specific amount of rainfall occurs within a certain period of time.

[0051] Rainfall distribution uniformity is used to describe the degree of spatial balance of rainfall distribution.

[0052] The maximum hourly rainfall and maximum daily rainfall refer to the maximum rainfall in one hour and one day respectively.

[0053] The recurrence period refers to the average interval between the recurrence of rainfall of a certain magnitude over a long period of time.

[0054] The previous rainfall is the cumulative rainfall in the period before this rainfall.

[0055] The multiple indicators may also include: physical indicators, chemical indicators and heavy metal content.

[0056] The section coordinates are the coordinates of the location where the section data was collected. One section data corresponds to one section coordinate.

[0057] Furthermore, the sub-steps of receiving and preprocessing the raw data to obtain monitoring data are as follows:

[0058] S11: Read the original data. If there is only one section data in the original data, set the preprocessing sequence number of the section data to 1, and use the section data as the current section data, and execute S13; if there are multiple section data in the original data, generate a preprocessing sequence number for each section data in a random order, and the preprocessing sequence number of the section data with an earlier random order is smaller than the preprocessing sequence number of the section data with a later random order, and execute S12.

[0059] Specifically, if there is only one cross-sectional data in the original data, it means that the original data is single-point data.

[0060] If there are multiple cross-section data in the original data, it means that the original data is multi-point data. Multi-point data includes: multiple single-point data with continuous time series (i.e., continuous sequence data) or multiple single-point data with discontinuous time series.

[0061] S12: Use the section data with the smallest pre-processing sequence number as the current section data and execute S13.

[0062] S13: Sort the N groups of indicator data in the current section data in order from front to back of the previous time node, and generate a noise reduction sequence number for each group of indicator data. The noise reduction sequence number of the group of indicator data at the front of the previous time node is smaller than the noise reduction sequence number of the group of indicator data at the back of the previous time node, and execute S14.

[0063] Furthermore, the time interval between two adjacent prior time nodes may be equal or unequal. In the present application, it is preferred that the time interval between two adjacent prior time nodes is equal.

[0064] The specific value of the time interval between two adjacent prior time nodes is set according to actual conditions, and can be a second, a minute, a hour, a day, a week, a month, a quarter or a year. In this application, the time interval between two adjacent prior time nodes is preferably 1 hour.

[0065] S14: N groups of index data are arranged in ascending order of noise reduction numbers to form an original data sequence, and noise reduction processing is performed on the original data sequence. After the noise reduction processing is completed, noise-reduced cross-sectional data is obtained, and S15 is executed.

[0066] Specifically, the raw data collected by the monitoring nodes has noise, so it is necessary to perform noise reduction processing on the raw data to improve the accuracy of the monitoring data.

[0067] The noise reduction of the original data sequence can be achieved by using existing noise reduction processing methods, such as mean filtering noise reduction method, median filtering noise reduction method and Gaussian filtering noise reduction method.

[0068] S15: Use the total number of section data in the original data to judge the preprocessing number of the current section data. If the total number of section data in the original data is greater than the preprocessing number of the current section data, the preprocessing number of the current section data is eliminated and S12 is executed; if the total number of section data in the original data is equal to the preprocessing number of the current section data, the preprocessing number of the current section data is eliminated and all the noise-reduced section data are used as monitoring data.

[0069] Specifically, one noise reduction section data corresponds to one section data, the N previous time nodes of the section data correspond one to one with the N previous time nodes of the corresponding noise reduction section data, and the section coordinates of the section data are the same as the section coordinates of the corresponding noise reduction section data.

[0070] Furthermore, the specific number of groups of indicator data is set according to actual conditions, and the specific number of groups of indicator data may be: N∈[2, 16], N∈[3, 24] or N∈[12, 24].

[0071] S2: Input the monitoring data into a pre-built data prediction model, and generate prediction data by the data prediction model; wherein, the prediction data includes: at least one prediction section data, each prediction section data includes: a section coordinate and M groups of prediction index data, each group of prediction index data includes: a prediction time node and multiple prediction indicators; one noise reduction section data corresponds to one prediction section data, and the section coordinates of the noise reduction section data are the same as the section coordinates of the corresponding prediction section data.

[0072] Specifically, the specific value of the time interval between two adjacent prediction time nodes is set according to actual conditions, which can be a second, a minute, a hour, a day, a week, a month, a quarter or a year. In this application, the time interval between two adjacent prediction time nodes is preferably 1 hour.

[0073] Furthermore, the monitoring data is input into a pre-built data prediction model, and the sub-steps of generating prediction data from the data prediction model are as follows:

[0074] S21: Read the monitoring data. If there is only one noise reduction section data in the monitoring data, set the prediction task number of the noise reduction section data to 1, and use the noise reduction section data as the current data to be predicted, and execute S23; if there are multiple noise reduction section data in the monitoring data, generate a prediction task number for each noise reduction section data in a random order, and the prediction task number of the noise reduction section data with an earlier random order is smaller than the prediction task number of the noise reduction section data with a later random order, and execute S22.

[0075] S22: Use the noise reduction section data with the smallest prediction task number as the current data to be predicted, and execute S23.

[0076] S23: construct M prediction time nodes for each group of noise reduction index data according to the total number N of previous time nodes of each group of noise reduction index data to be predicted, and execute S24; wherein, or

[0077] Specifically, or But not limited to or

[0078] Among them, the specific number of prediction time nodes is set according to actual conditions, and the preferred number in this application is: M∈[1,8].

[0079] A set of noise reduction index data corresponds to M sets of prediction index data, and a set of prediction index data corresponds to a prediction time node and multiple prediction indicators.

[0080] S24: In order from front to back of the previous time node, the N groups of noise reduction index data in the current prediction data and the M prediction time nodes of each group of noise reduction index data are input into the pre-built data prediction model in sequence, and the data prediction model generates multiple prediction indicators for each prediction time node of each group of noise reduction index data, and constitutes M groups of prediction index data, and the M groups of prediction index data and the corresponding section coordinates are used as a prediction section data, and S25 is executed, wherein a group of prediction index data includes: a prediction time node and multiple prediction indicators corresponding to the prediction time node.

[0081] Furthermore, the data prediction model is a pre-trained neural network model.

[0082] S25: Use the total number of denoised section data in the monitoring data to judge the prediction task number of the current data to be predicted. If the total number of denoised section data in the monitoring data is greater than the prediction task number of the current data to be predicted, the prediction task number of the current data to be predicted is eliminated and S22 is executed; if the total number of denoised section data in the monitoring data is equal to the prediction task number of the current data to be predicted, the prediction task number of the previous data to be predicted is eliminated, and all the prediction section data are used as prediction data.

[0083] Specifically, one piece of denoised section data corresponds to one piece of predicted section data, and the section coordinates of the denoised section data are the same as the section coordinates of the corresponding predicted section data.

[0084] S3: Receive collected data and pre-process the collected data to obtain actual data; wherein, the actual data includes: at least one actual section data, each actual section data includes: a section coordinate and M groups of actual indicator data, each group of actual indicator data includes: a subsequent time node and multiple actual indicators; one predicted section data corresponds to one actual section data, and the section coordinates of the predicted section data are the same as the section coordinates of the corresponding actual section data.

[0085] Furthermore, the sub-steps of receiving the collected data and pre-processing the collected data to obtain the actual data are as follows:

[0086] S31: Read the collected data. If there is only one collected section data in the collected data, set the preprocessing sequence number of the collected section data to 1, and use the collected section data as the current collected section data, and execute S33; if there are multiple collected section data in the collected data, generate a preprocessing sequence number for each collected section data in a random order, and the preprocessing sequence number of the collected section data with an earlier random order is smaller than the preprocessing sequence number of the collected section data with a later random order, and execute S32.

[0087] Specifically, the collected data includes: at least one collected section data, each collected section data includes: a section coordinate and M groups of collected index data, and each group of collected index data includes: a subsequent time node and multiple indicators.

[0088] One collected section data corresponds to one predicted section data, the subsequent time node of the collected section data is consistent with the predicted time node of the predicted section data, and the section coordinates of the collected section data are consistent with the section coordinates of the predicted section data.

[0089] S32: The collected section data with the smallest pre-processing sequence number is used as the current collected section data, and S33 is executed.

[0090] S33: Sort the M groups of acquisition index data in the current acquisition section data in order from front to back at the subsequent time node, and generate a noise reduction sequence number for each group of acquisition index data. The noise reduction sequence number of the group of acquisition index data in front of the subsequent time node is smaller than the noise reduction sequence number of the group of acquisition index data in the back of the subsequent time node, and execute S34.

[0091] Furthermore, the time interval between two adjacent subsequent time nodes may be equal or unequal. In the present application, it is preferred that the time interval between two adjacent subsequent time nodes is equal.

[0092] Specifically, the specific value of the time interval between two adjacent subsequent time nodes is set according to actual conditions, and can be a second, a minute, a hour, a day, a week, a month, a quarter or a year. In this application, the time interval between two adjacent subsequent time nodes is preferably 1 hour.

[0093] S34: The M groups of collected index data are arranged in ascending order of the noise reduction sequence number to form an original collected data sequence, and the original collected data sequence is subjected to noise reduction processing. After the noise reduction processing is completed, the actual cross-section data is obtained, and S35 is executed.

[0094] Specifically, the noise reduction processing of the original collected data sequence can be achieved by adopting existing noise reduction processing methods, such as: mean filter noise reduction method, median filter noise reduction method and Gaussian filter noise reduction method.

[0095] S35: Use the total number of collected section data in the collected data to judge the preprocessing number of the current collected section data. If the total number of collected section data in the collected data is greater than the preprocessing number of the current collected section data, the preprocessing number of the current collected section data is eliminated and S32 is executed; if the total number of collected section data in the collected data is equal to the preprocessing number of the current collected section data, the preprocessing number of the current collected section data is eliminated and all actual section data are used as actual data.

[0096] Specifically, one predicted section data corresponds to one actual section data, the M predicted time nodes of the predicted section data correspond one-to-one to the M subsequent time nodes of the corresponding actual section data, and the section coordinates of the predicted section data are the same as the section coordinates of the corresponding actual section data.

[0097] S4: Input the predicted data and actual data into the abnormality diagnosis model, and the abnormality diagnosis model generates a data diagnosis result and sends it; wherein the data diagnosis result includes: at least one sub-diagnosis result, and the sub-diagnosis result is data abnormality or data normal.

[0098] Furthermore, both the predicted data and the actual data are input into the anomaly diagnosis model, and the sub-steps of generating the data diagnosis results by the anomaly diagnosis model are as follows:

[0099] S41: Read the predicted data and actual data. If there is one predicted section data in the predicted data and one actual section data in the actual data, then use the predicted section data and the actual section data as the current diagnosis data, and set the sequence number of the current diagnosis data to be diagnosed to 1, and execute S43; if there are multiple predicted section data in the predicted data and multiple actual section data in the actual data, then use the predicted section data and the actual section data with the same section coordinates as a group of data to be diagnosed, and randomly generate a sequence number to be diagnosed for each group of data to be diagnosed, and the sequence number to be diagnosed of the group of data to be diagnosed in the front of the random order is smaller than the sequence number to be diagnosed of the group of data to be diagnosed in the back of the random order, and execute S42.

[0100] S42: The group of to-be-diagnosed data with the smallest to-be-diagnosed sequence number is used as the current diagnosis data, and S43 is executed.

[0101] S43: Group the M groups of predicted indicator data and M groups of actual indicator data in the current diagnostic data, take the predicted indicator data and actual indicator data with the same predicted time node and the same subsequent time node as a group of analysis data, and generate an analysis sequence number for each group of analysis data in the order from front to back of the subsequent time node. The analysis sequence number of the group of analysis data with the previous subsequent time node is smaller than the analysis sequence number of the group of analysis data with the next subsequent time node, and execute S44.

[0102] S44: Perform error trend analysis on the M groups of analysis data using the first analysis model in the abnormal diagnosis model to obtain a first diagnosis result, wherein the first diagnosis result is: data abnormality or data normal; if the first diagnosis result is that the data is normal, execute S45; if the first diagnosis result is that the data is abnormal, execute S46.

[0103] Furthermore, the first analysis model in the abnormality diagnosis model performs error trend analysis on the M groups of analysis data to obtain the first diagnosis result in the following sub-steps:

[0104] S441: Obtain the error sequence of each prediction indicator in the M groups of analysis data from the first analysis model in the abnormality diagnosis model, wherein the error sequence in, The kth prediction indicator in the analysis data with analysis number 1 and the kth actual index The prediction error between For the kth prediction indicator in the analysis data with analysis number 2 and the kth actual index The prediction error between For the kth prediction indicator in the analysis data with analysis number M and the kth actual index The prediction error value between them; k∈[1,K], K is the total number of prediction indicators.

[0105] Furthermore, the total number of types K of prediction indicators is set according to actual conditions, and this application preferably has 9 types.

[0106] Furthermore, the nine prediction indicators are: rainfall amount, rainfall intensity, rainfall duration, rainfall frequency, rainfall distribution uniformity, maximum hourly rainfall, maximum daily rainfall, return period and antecedent rainfall.

[0107] S442: The first analysis model in the abnormality diagnosis model performs error trend analysis on the error sequence of each prediction indicator to obtain K first sub-results. If the trend of the error sequence is increasing, the first sub-result is abnormal; if the trend of the error sequence is not increasing, the first sub-result is normal.

[0108] S443: Analyze the K first sub-results and generate a first diagnosis result. If all the K first sub-results are normal, the first diagnosis result is that the data is normal; if at least one of the K first sub-results is abnormal, the first diagnosis result is that the data is abnormal.

[0109] S45: Analyze the Mth group of analysis data using the second analysis model in the abnormality diagnosis model to obtain a second diagnosis result, wherein the second diagnosis result is: the data is abnormal or the data is normal.

[0110] Furthermore, the second analysis model in the abnormality diagnosis model analyzes the M-th group of analysis data to obtain the second diagnosis result in the following sub-steps:

[0111] S451: constructing a spherical region with the cross-section coordinates of the Mth group of analysis data as the sphere center and a preset region radius as the radius, and using the spherical region as the target sample region.

[0112] S452: Traverse the sample database according to the target sample area, and use the sample data with overlapping areas with the target sample area as target sample data, wherein each target sample data includes: input data, output data and area range; input data includes: multiple input sample indicators; output data includes: multiple sample standard indicators; multiple input sample indicators correspond one-to-one with multiple sample standard indicators; multiple sample standard indicators correspond one-to-one with multiple prediction indicators.

[0113] Specifically, the sample data having overlapping portions between the region range and the target sample region are all taken as target sample data, which can fully consider the correlation between the upstream and downstream section data.

[0114] S453: Inputting the input data of the target sample data into the data prediction model, and generating predicted sample data by the data prediction model, each predicted sample data includes: a plurality of predicted sample indicators.

[0115] S454: Analyze the predicted sample data using the output data of the target sample data to obtain a benchmark error for each prediction indicator.

[0116] Furthermore, as an embodiment, different types of prediction indicators have different benchmark errors, wherein the benchmark error of the k-th prediction indicator is expressed as follows:

[0117]

[0118] Among them, Bwc k is the benchmark error of the k-th prediction indicator; is the kth prediction sample index in the output data of the vth prediction sample data; is the kth sample standard index in the output data of the vth target sample data, v∈[1,V], V is the total number of target sample data; Δbzc k is the standard deviation of the k-th prediction indicator.

[0119] Specifically, based on different benchmark errors, the accuracy of abnormal diagnosis of surface water automatic monitoring data can be improved. The standard deviation of each prediction indicator is set according to the characteristics of the indicator and the actual situation.

[0120] Furthermore, as another embodiment, the baseline errors of different types of prediction indicators are the same, and a pre-built autoencoder model based on a deep spatiotemporal graph convolutional neural network is used to statistically predict the error distribution in the Mth hour in the future, and the error greater than the mean + 3 times the standard deviation is used as the baseline error for the Mth hour, and S455 is executed.

[0121] Specifically, an autoencoder model based on a deep spatiotemporal graph convolutional neural network is established. The autoencoder model includes a deep spatiotemporal graph convolutional neural network model and a simple convolutional neural network. The deep spatiotemporal graph convolutional neural network model serves as the encoder model, and the simple convolutional neural network serves as the decoder model. After the autoencoder model is trained, the error distribution for the next M hours is statistically predicted. The error greater than the mean + 3 standard deviations is used as the baseline error for the M hour, and S455 is executed.

[0122] S455: Use the benchmark error of each prediction indicator to judge the current error value of each prediction indicator in the Mth group of analysis data, and generate K second sub-results. If the current error value of the kth prediction indicator is less than or equal to the benchmark error, the second sub-result of the kth prediction indicator is normal; if the current error value of the kth prediction indicator is greater than the benchmark error, the second sub-result of the kth prediction indicator is abnormal.

[0123] Furthermore, the current error value of the k-th prediction indicator=the k-th prediction indicator in the M-th group of analysis data-the k-th actual indicator in the M-th group of analysis data.

[0124] S456: Analyze the K second sub-results and generate a second diagnosis result. If all the K second sub-results are normal, the second diagnosis result is that the data is normal; if at least one of the K second sub-results is abnormal, the second diagnosis result is that the data is abnormal.

[0125] S46: Generate a sub-diagnostic result based on the first diagnostic result or the second diagnostic result, and execute S47; wherein, if the first diagnostic result is data abnormality, the sub-diagnostic result is data abnormality; if the second diagnostic result is data normal, the sub-diagnostic result is data normal; if the second diagnostic result is data abnormality, the sub-diagnostic result is data abnormality.

[0126] S47: Use the total number of current diagnostic data to judge the serial number of the current diagnostic data to be diagnosed. If the total number of current diagnostic data is greater than the serial number of the current diagnostic data to be diagnosed, then the serial number of the current diagnostic data to be diagnosed is eliminated and S42 is executed; if the total number of current diagnostic data is equal to the serial number of the current diagnostic data to be diagnosed, then the serial number of the current diagnostic data to be diagnosed is eliminated and all sub-diagnostic results are used as data diagnosis results.

[0127] Specifically, after obtaining the data diagnosis results, the data anomaly diagnosis center sends the data diagnosis results to the monitoring node.

[0128] For normal data, the error between the predicted data generated by the data prediction model and the original data is within the normal range. However, for abnormal data, the error between the predicted data generated by the data prediction model and the original data deviates from the normal range. Therefore, monitoring data with a sub-diagnosis result of data anomaly is considered abnormal data.

[0129] The beneficial effects achieved by this application are as follows:

[0130] (1) The anomaly diagnosis method and system for automatic surface water monitoring data of the present application can directly perform data anomaly diagnosis on the original data without pre-marking the abnormal data, which can effectively improve the efficiency of anomaly diagnosis on a large amount of original data.

[0131] (2) The abnormality diagnosis method and system of surface water automatic monitoring data of the present application can effectively improve the accuracy of data abnormality diagnosis and analysis.

[0132] (3) The abnormality diagnosis method and system of the surface water automatic monitoring data of the present application can perform abnormality diagnosis on single-point data and continuous sequence data.

[0133] (4) The abnormality diagnosis method and system of the surface water automatic monitoring data of the present application can fully consider the correlation between the upstream and downstream section data, and at the same time judge whether the data of multiple sections are abnormal.

[0134] (5) The abnormality diagnosis method and system of the surface water automatic monitoring data of the present application can automatically identify the original data with abnormalities. The timely and accurate detection of abnormal data can provide support for the operation and maintenance of the automatic station and the early detection of pollution incidents.

[0135] Although preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the underlying inventive concepts. Therefore, the scope of protection of this application is intended to include the preferred embodiments and all changes and modifications that fall within the scope of this application. Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if such changes and modifications of this application fall within the scope of protection of this application and its equivalents, then this application is intended to include such changes and modifications.

Claims

1. A method for diagnosing abnormalities in surface water automatic monitoring data, characterized in that: The steps include: S1: Receive raw data and pre-process the raw data to obtain monitoring data; wherein the raw data includes: at least one section data, each section data includes: a section coordinate and N groups of index data, each group of index data includes: a previous time node and multiple indicators; the monitoring data includes: at least one noise reduction section data, each noise reduction section data includes: a section coordinate and N groups of noise reduction index data, each group of noise reduction index data includes: a previous time node and multiple noise reduction indicators; one section data corresponds to one noise reduction section data, and the section coordinates of the section data are the same as the section coordinates of the corresponding noise reduction section data; S2: Input the monitoring data into a pre-built data prediction model, and generate prediction data by the data prediction model; wherein the prediction data includes: at least one prediction section data, each prediction section data includes: a section coordinate and M groups of prediction index data, each group of prediction index data includes: a prediction time node and multiple prediction indicators; one noise reduction section data corresponds to one prediction section data, and the section coordinates of the noise reduction section data are the same as the section coordinates of the corresponding prediction section data; S3: receiving collected data and preprocessing the collected data to obtain actual data; wherein the actual data includes: at least one actual section data, each actual section data includes: a section coordinate and M groups of actual indicator data, each group of actual indicator data includes: a subsequent time node and multiple actual indicators; one predicted section data corresponds to one actual section data, and the section coordinates of the predicted section data are the same as the section coordinates of the corresponding actual section data; S4: Inputting both the predicted data and the actual data into the anomaly diagnosis model, generating a data diagnosis result by the anomaly diagnosis model, and sending the result; wherein the data diagnosis result includes: at least one sub-diagnosis result, and the sub-diagnosis result is data anomaly or data normal; The sub-steps of inputting both the predicted data and the actual data into the anomaly diagnosis model and generating the data diagnosis results from the anomaly diagnosis model are as follows: S41: Read the predicted data and the actual data. If there is one predicted section data in the predicted data and one actual section data in the actual data, use the predicted section data and the actual section data as the current diagnosis data, set the sequence number of the current diagnosis data to be diagnosed to 1, and execute S43. If there are multiple predicted section data in the predicted data and multiple actual section data in the actual data, use the predicted section data and the actual section data with the same section coordinates as a group of data to be diagnosed, and randomly generate a sequence number for each group of data to be diagnosed, and the sequence number for the group of data to be diagnosed that is in the first random order is smaller than the sequence number for the group of data to be diagnosed that is in the last random order, and execute S42. S42: taking the group of to-be-diagnosed data with the smallest to-be-diagnosed sequence number as the current diagnosis data, and executing S43; S43: Grouping the M groups of predicted indicator data and the M groups of actual indicator data in the current diagnostic data, taking the predicted indicator data and the actual indicator data with the same predicted time node and the same subsequent time node as a group of analysis data, and generating an analysis sequence number for each group of analysis data in order from the front to the back of the subsequent time node, wherein the analysis sequence number of the group of analysis data with the earlier subsequent time node is smaller than the analysis sequence number of the group of analysis data with the later subsequent time node, and executing S44; S44: performing error trend analysis on the M sets of analysis data using the first analysis model in the abnormality diagnosis model to obtain a first diagnosis result, wherein the first diagnosis result is: data abnormality or data normal; if the first diagnosis result is that the data is normal, executing S45; if the first diagnosis result is that the data is abnormal, executing S46; S45: Analyze the Mth group of analysis data using the second analysis model in the abnormality diagnosis model to obtain a second diagnosis result, wherein the second diagnosis result is: the data is abnormal or the data is normal; S46: Generate a sub-diagnosis result based on the first diagnosis result or the second diagnosis result, and execute S47; wherein, if the first diagnosis result is data abnormality, the sub-diagnosis result is data abnormality; if the second diagnosis result is data normal, the sub-diagnosis result is data normal; if the second diagnosis result is data abnormality, the sub-diagnosis result is data abnormality; S47: Use the total number of current diagnostic data to judge the serial number of the current diagnostic data to be diagnosed. If the total number of current diagnostic data is greater than the serial number of the current diagnostic data to be diagnosed, then the serial number of the current diagnostic data to be diagnosed is eliminated and S42 is executed; if the total number of current diagnostic data is equal to the serial number of the current diagnostic data to be diagnosed, then the serial number of the current diagnostic data to be diagnosed is eliminated and all sub-diagnostic results are used as data diagnosis results.

2. The abnormality diagnosis method of surface water automatic monitoring data according to claim 1 is characterized in that: The sub-steps of receiving and preprocessing raw data to obtain monitoring data are as follows: S11: Read the original data. If there is only one cross-sectional data in the original data, set the pre-processing sequence number of the cross-sectional data to 1, and use the cross-sectional data as the current cross-sectional data, and execute S13. If there are multiple cross-sectional data in the original data, generate a pre-processing sequence number for each cross-sectional data in a random order. The pre-processing sequence number of the cross-sectional data with an earlier random order is smaller than the pre-processing sequence number of the cross-sectional data with a later random order, and execute S12. S12: Use the section data with the smallest pre-processing sequence number as the current section data and execute S13; S13: Sort the N groups of indicator data in the current section data in order from the previous time node, and generate a noise reduction sequence number for each group of indicator data. The noise reduction sequence number of the group of indicator data at the previous time node is smaller than the noise reduction sequence number of the group of indicator data at the previous time node, and execute S14; S14: N groups of index data are arranged in ascending order of noise reduction numbers to form an original data sequence, and noise reduction processing is performed on the original data sequence. After the noise reduction processing is completed, noise-reduced cross-sectional data is obtained, and S15 is executed; S15: using the total number of cross-sectional data in the original data to judge the pre-processing sequence number of the current cross-sectional data, if the total number of cross-sectional data in the original data is greater than the pre-processing sequence number of the current cross-sectional data, the pre-processing sequence number of the current cross-sectional data is discarded and S12 is executed; If the total number of section data in the original data is equal to the pre-processing sequence number of the current section data, the pre-processing sequence number of the current section data is removed, and all the noise-reduced section data are used as monitoring data.

3. The abnormality diagnosis method of surface water automatic monitoring data according to claim 2 is characterized in that: Input the monitoring data into the pre-built data prediction model. The sub-steps of generating prediction data from the data prediction model are as follows: S21: Read the monitoring data. If there is only one noise reduction section data in the monitoring data, set the prediction task sequence number of the noise reduction section data to 1, and use the noise reduction section data as the current data to be predicted, and execute S23; if there are multiple noise reduction section data in the monitoring data, generate a prediction task sequence number for each noise reduction section data in a random order, and the prediction task sequence number of the noise reduction section data with an earlier random order is smaller than the prediction task sequence number of the noise reduction section data with a later random order, and execute S22; S22: taking the noise reduction section data with the smallest prediction task number as the current data to be predicted, and executing S23; S23: construct M prediction time nodes for each set of noise reduction index data according to the total number N of previous time nodes of each set of noise reduction index data to be predicted, and execute S24; wherein, or ; S24: In order from the previous time node to the next, the N groups of noise reduction index data in the current prediction data and the M prediction time nodes of each group of noise reduction index data are input into a pre-built data prediction model. The data prediction model generates multiple prediction indicators for each prediction time node of each group of noise reduction index data, and forms M groups of prediction index data. The M groups of prediction index data and the corresponding section coordinates are used as one prediction section data, and S25 is executed, wherein a group of prediction index data includes: a prediction time node and multiple prediction indicators corresponding to the prediction time node; S25: Use the total number of denoised section data in the monitoring data to judge the prediction task number of the current data to be predicted. If the total number of denoised section data in the monitoring data is greater than the prediction task number of the current data to be predicted, the prediction task number of the previous data to be predicted is eliminated and S22 is executed; if the total number of denoised section data in the monitoring data is equal to the prediction task number of the current data to be predicted, the prediction task number of the current data to be predicted is eliminated, and all the prediction section data are used as prediction data.

4. The abnormality diagnosis method of surface water automatic monitoring data according to claim 3 is characterized in that: The sub-steps of receiving collected data and preprocessing it to obtain actual data are as follows: S31: Read the collected data. If there is only one collected section data in the collected data, set the pre-processing sequence number of the collected section data to 1, and use the collected section data as the current collected section data, and execute S33; If there are multiple acquisition section data in the acquisition data, a preprocessing sequence number is generated for each acquisition section data in a random order, the preprocessing sequence number of the acquisition section data in the earlier random order is smaller than the preprocessing sequence number of the acquisition section data in the later random order, and S32 is executed; S32: taking the acquisition section data with the smallest pre-processing sequence number as the current acquisition section data, and executing S33; S33: sorting the M groups of acquisition index data in the current acquisition section data in order from the front to the back of the subsequent time node, and generating a noise reduction sequence number for each group of acquisition index data, wherein the noise reduction sequence number of the group of acquisition index data at the front of the subsequent time node is smaller than the noise reduction sequence number of the group of acquisition index data at the back of the subsequent time node, and executing S34; S34: The M groups of collected index data are arranged in ascending order of the noise reduction sequence number to form an original collected data sequence, and the original collected data sequence is subjected to noise reduction processing. After the noise reduction processing is completed, the actual cross-sectional data is obtained, and S35 is executed; S35: using the total number of collected section data in the collected data to determine the preprocessing sequence number of the current collected section data, if the total number of collected section data in the collected data is greater than the preprocessing sequence number of the current collected section data, then discarding the preprocessing sequence number of the current collected section data and executing S32; If the total number of collected section data in the collected data is equal to the pre-processing sequence number of the current collected section data, the pre-processing sequence number of the current collected section data is removed, and all actual section data are used as actual data.

5. The abnormality diagnosis method of surface water automatic monitoring data according to claim 1 is characterized in that: The first analysis model in the abnormality diagnosis model performs error trend analysis on the M groups of analysis data to obtain the first diagnosis result in the following sub-steps: S441: Obtain the error sequence of each prediction indicator in the M groups of analysis data from the first analysis model in the abnormality diagnosis model, wherein the error sequence ,in, , The analysis data with the analysis number 1 is Predictive indicators With the Actual indicators The prediction error between , For the analysis data with sequence number 2 Predictive indicators With the Actual indicators The prediction error between , For the analysis data with the sequence number M Predictive indicators With the Actual indicators The prediction error between , is the total number of types of predictors; S442: The first analysis model in the abnormal diagnosis model performs error trend analysis on the error sequence of each prediction indicator to obtain If the error sequence has an increasing trend, the first sub-result is abnormal; if the error sequence has an increasing trend, the first sub-result is normal. S443: Yes The first sub-result is analyzed and the first diagnosis result is generated. If all the first sub-results are normal, the first diagnosis result is that the data is normal; if If there is at least one abnormality in the first sub-results, the first diagnosis result is a data abnormality.

6. The abnormality diagnosis method for surface water automatic monitoring data according to claim 5 is characterized in that: The total number of predictor types The nine prediction indicators are: rainfall amount, rainfall intensity, rainfall duration, rainfall frequency, rainfall distribution uniformity, maximum hourly rainfall, maximum daily rainfall, recurrence period and previous rainfall.

7. The abnormality diagnosis method of surface water automatic monitoring data according to claim 5 is characterized in that: The sub-steps of analyzing the M-th group of analysis data using the second analysis model in the abnormality diagnosis model to obtain the second diagnosis result are as follows: S451: constructing a spherical area with the cross-section coordinates of the Mth group of analysis data as the sphere center and a preset area radius as the radius, and using the spherical area as the target sample area; S452: Traverse the sample database according to the target sample area, and use the sample data whose area range overlaps with the target sample area as the target sample data, wherein each target sample data includes: input data, output data, and area range; the input data includes: multiple input sample indicators; the output data includes: multiple sample standard indicators; the multiple input sample indicators correspond one-to-one with the multiple sample standard indicators; the multiple sample standard indicators correspond one-to-one with the multiple prediction indicators; S453: Inputting the input data of the target sample data into the data prediction model, and generating predicted sample data by the data prediction model, each predicted sample data including: a plurality of predicted sample indicators; S454: Analyze the predicted sample data using the output data of the target sample data to obtain a benchmark error for each prediction indicator; S455: Use the benchmark error of each prediction indicator to judge the current error value of each prediction indicator in the Mth group of analysis data, and generate The second child result, if If the current error value of the prediction indicator is less than or equal to the benchmark error, then The second child result of this predictive indicator is normal; if the If the current error value of the prediction indicator is greater than the benchmark error, then The second sub-result of the predictive indicator is abnormal; The current error value of the prediction indicator = the Predictive indicators - the first in the Mth group of analysis data Actual indicators; S456: Yes The second sub-result is analyzed and a second diagnosis result is generated. If all the second sub-results are normal, the second diagnosis result is that the data is normal; if If there is at least one abnormality in the second sub-results, the second diagnosis result is a data abnormality.

8. The abnormality diagnosis method for surface water automatic monitoring data according to claim 7 is characterized in that: Different types of forecast indicators have different benchmark errors. The expression of the benchmark error of the prediction indicator is: ； in, For the The baseline error of the prediction indicator; For the The output data of the predicted sample data Prediction sample indicators; For the The output data of the target sample data Sample standard indicators, , is the total number of target sample data; For the The standard deviation of the predictor.

9. A surface water automatic monitoring data abnormality diagnosis system, characterized in that: include: Multiple monitoring nodes and data anomaly diagnosis centers; Among them, the monitoring node is used to send raw data; send collected data; receive data diagnosis results; Data anomaly diagnosis center: used to execute the anomaly diagnosis method of surface water automatic monitoring data as described in any one of claims 1-8.

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