Industrial park pollution source visual monitoring system and method

Abstract

The invention belongs to the technical field of data processing, and particularly relates to an industrial park pollution source visual monitoring system and method.The method comprises the steps that a water quality monitoring sensor is installed in each enterprise of an industrial park, key indexes related to water quality are monitored in real time, water quality data are transmitted to an identification module through a wireless network, and the identification module is connected with the industrial park; identifying and processing error values in the water quality data, normalizing the water quality data, classifying the water quality data, calculating a pollution score, setting a pollution level, judging whether the water quality data is abnormal or not, if the water quality data is abnormal, comparing normal ranges of key indexes, identifying standard-exceeding indexes and marking on a GIS map, and if the water quality data is not abnormal, judging whether the standard-exceeding indexes are abnormal or not. A prediction model is trained on the basis of historical data, whether abnormity occurs in the future or not is predicted, the water quality data and the geographic position are displayed on a GIS map through a visualization module, abnormal data are specifically marked, and chart analysis of the historical data is provided. According to the invention, the pollution source of the industrial park can be accurately monitored.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a visual monitoring system and method for pollution sources in industrial parks. Background Art

[0002] Traditional pollution monitoring methods often rely on fixed monitoring stations with limited distribution, making it difficult to comprehensively cover every corner of industrial parks. In addition, traditional methods do not consider the accuracy of data during data collection. For example, the Chinese patent application with the publication number CN116010507A provides a water pollution monitoring method and system based on big data. The method includes: analyzing and matching data collection methods according to water pollution factors to determine data collection ports; determining the characteristics of the target water area; conducting a correlation analysis based on the characteristics of the target water area and water pollution factors, and determining the target water pollution factors and corresponding target data collection ports based on the correlation, collecting data through the target data collection ports to construct a multi-source data collection source; performing standardized preprocessing on the multi-source data collection source to obtain the target data source; and inputting the target data source into a water pollution analysis model to obtain a water pollution analysis result. Another similar prior art is the Chinese patent application with the publication number CN112559657A, which discloses a water pollution tracing method, device, and terminal device. The method includes: based on online water quality monitoring data, obtaining the current monitoring point information where water pollution occurs and the concentration of the first exceeded pollutant corresponding to the current monitoring point; and according to the current monitoring point information, obtaining the information of each upstream monitoring point corresponding to the current monitoring point and the tracing time corresponding to each upstream monitoring point; determining the concentration of the second exceeded pollutant at each upstream monitoring point corresponding to each tracing time according to the online water quality monitoring data; and when the concentration of the second exceeded pollutant is greater than the concentration of the first exceeded pollutant, determining the upstream monitoring point corresponding to the concentration of the second exceeded pollutant as the water pollution source point. However, neither of the above two methods considers the problem that water quality data may be incorrect when collecting water quality-related data. Therefore, the present invention provides a visual monitoring system and method for pollution sources in industrial parks. Summary of the Invention

[0003] The present invention installs water quality monitoring sensors in each enterprise in the industrial park to monitor key water quality indicators in real time, transmits the water quality data to an identification module to identify incorrect values in the water quality data, and replaces them with the corresponding key indicators of the previous set of water quality data. By detecting and replacing incorrect values, the accuracy of the water quality data is improved. After normalizing the water quality data, the water quality data is classified, and the pollution score for each water quality category is calculated. The pollution level is set to determine whether the water quality data is abnormal. An abnormal model is also used to predict the future water quality condition and formulate preventive measures in advance. Finally, the water quality data is visually displayed in combination with a GIS map, and specific identifiers are used to mark abnormal data to help management personnel quickly understand the water quality condition.

[0004] To achieve the above-mentioned invention object, the present invention provides a visual monitoring method for pollution sources in industrial parks as described below. By installing water quality monitoring sensors at the pollution source discharge outlets of each enterprise in the industrial park, the method is implemented by performing the following steps:

[0005] Step S1: Real-time monitor key indicators related to water quality, combine multiple key indicators collected simultaneously into a set of water quality data, and transmit the water quality data to the recognition module in real time through a wireless network;

[0006] Step S2: Identify whether there are error values in the key indicators in the water quality data. If so, use the key indicators in the previous set of water quality data stored in the water quality database to replace the key indicators identified as error values. The recognition module stores the water quality data in the water quality database, performs normalization processing on the water quality data in the water quality database, classifies the normalized water quality data into multiple water quality categories, calculates the pollution score for each water quality category, sets the pollution level for each water quality category based on the pollution score, determines the water quality category to which the newly obtained water quality data belongs and obtains the corresponding pollution level. If the pollution level is greater than the preset pollution level, it is determined that the water quality data is abnormal;

[0007] Step S3: If the water quality data is abnormal, a comparison database is established in advance. The comparison database stores the normal ranges of the key indicators. Compare the key indicators with the corresponding normal ranges, identify the key indicators that exceed the normal ranges, obtain the geographical locations of the corresponding sensors, and send the geographical locations and the key indicators that exceed the ranges to the visualization module. If the water quality data is not abnormal, a prediction model is trained based on the water quality data in the water quality database, and whether an abnormality will occur in the future is judged based on the prediction model;

[0008] Step S4: The visualization module visually displays the real-time monitored water quality data and the corresponding geographical locations through a GIS map, marks the key indicators that exceed the ranges and the corresponding geographical locations on the GIS map with specific marks. In the case of predicting that an abnormality will occur in the future, different specific marks are also used for marking on the GIS map. The visualization module also performs chart analysis on the historical water quality data.

[0009] As a preferred technical solution of the present invention, identifying whether there are error values in the key indicators in the water quality data includes the following steps:

[0010] Take the water quality data as the target water quality data, obtain the historical water quality data stored in the water quality database, obtain one key index in the target water quality data as the target key index, obtain all the corresponding historical key indexes in the historical water quality data for the target key index, arrange all the historical key indexes in ascending order of numerical value. If the number of all the historical key indexes is odd, take the historical key index in the middle as the first reference value; otherwise, take the average of the two historical key indexes in the middle as the first reference value. Calculate the difference between the target key index and the first reference value as the first difference, and also calculate the differences between all the historical key indexes and the first reference value as the second differences. Calculate the second reference value of all the second differences, and calculate the standard value of the target key index using the first formula. The first formula is: S = |A - B| / C, where S is the standard value, A is the target key index, B is the first reference value, and C is the second reference value. Preset a first threshold. If the standard value is greater than the first threshold, determine the target key index as an error value.

[0011] As a preferred technical solution of the present invention, classifying the normalized water quality data into multiple water quality categories includes the following steps:

[0012] Step S21: Preset a predetermined number of water quality categories, randomly select a predetermined number of the water quality data from all the water quality data as the first water quality data, and randomly assign the first water quality data to each of the water quality categories, that is, take the first water quality data as the initial data corresponding to the water quality category.

[0013] Step S22: Select one water quality data from the remaining water quality data in the water quality database as the second water quality data, calculate the first distance between the second water quality data and all the first water quality data, obtain the minimum first distance among the multiple first distances, obtain the water quality category corresponding to the first water quality data corresponding to the minimum first distance, classify the second water quality data into the water quality category, and update the initial data of the water quality category based on the second water quality data.

[0014] Step S23: Repeat step S22 until all the water quality data have been selected.

[0015] As a preferred technical solution of the present invention, calculating the first distance between the second water quality data and all the first water quality data includes the following steps:

[0016] Use the second formula to calculate the first distance between the second water quality data and each of the first water quality data. The second formula is:

[0017]

[0018] Where D is the first distance, Hi is the i-th key index of the first water quality data, Ei is the i-th key index of the second water quality data, and m refers to the total number of key indexes in the water quality data.

[0019] As a preferred technical solution of the present invention, updating the initial data of the water quality category based on the second water quality data includes the following steps:

[0020] Updating the key indexes in the initial data based on the third formula, and the third formula is:

[0021] Hn = Ho + (D min / D max )(E - Ho),

[0022] Where Ho is a key index in the initial data before updating, E is the corresponding key index in the second water quality data, Dmin is the minimum first distance, Dmax is the maximum first distance among all the first distances, and Hn is the corresponding key index in the updated initial data.

[0023] As a preferred technical solution of the present invention, calculating the pollution score of each water quality category includes the following steps:

[0024] Assigning relevant values to different key indexes based on historical data, where the relevant values represent the importance of the key indexes to the pollution score, obtaining all the water quality data in each water quality category, calculating the corresponding average key index for each key index, and using the fourth formula to calculate the pollution score of each water quality category. The fourth formula is:

[0025]

[0026] Where Gi is the average key index corresponding to the i-th key index, and Wi is the relevant value corresponding to the i-th key index.

[0027] As a preferred technical solution of the present invention, training and generating a prediction model based on the water quality data in the water quality database includes the following steps:

[0028] Step S31: Using a number of the water quality data as the first learning data, training and generating a first model, collecting other data related to water pollution, obtaining a combination of a number of the water quality data and the other data except the first learning data as the second learning data, and training and generating a second model;

[0029] Step S32: Obtain the latest collected water quality data as the third water quality data, input the third water quality data into the first model to obtain the first prediction data, calculate the first difference between the third water quality data and the first prediction data, obtain the other data corresponding to the third water quality data, input the third water quality data and the other data into the second model to obtain the second prediction data, and calculate the second difference between the second prediction data and the third water quality data;

[0030] Step S33: If the second difference is less than the first difference, calculate the difference between the first difference and the second difference as the third difference, compare the third difference with a preset second threshold. If the third difference is greater than the second threshold, combine the second model and the first model to generate a third model as the prediction model;

[0031] Step S34: If the second difference is greater than or equal to the first difference, continue to collect new other data related to water quality, use the new other data and several pieces of water quality data as new learning data to train the second model to generate a new second model, and return to Step S32 until the second difference is less than the first difference.

[0032] As a preferred technical solution of the present invention, judging whether an anomaly will occur in the future based on the prediction model includes the following steps:

[0033] Obtain predicted water quality data based on the prediction model, judge the water quality category of the predicted water quality data, obtain the corresponding pollution score based on the water quality category, obtain the corresponding pollution level based on the pollution score. If the pollution level is greater than the preset pollution level, judge that an abnormal situation will occur in the future.

[0034] The present invention also provides an industrial park pollution source visualization monitoring system, including the following modules:

[0035] A monitoring module, used to monitor key indicators related to water quality in real time, combine multiple key indicators collected simultaneously into a set of water quality data, and transmit it to the identification module in real time through a wireless network;

[0036] An identification module is used to identify whether there are error values in the key indicators in the water quality data. If so, the key indicators in the previous set of water quality data stored in the water quality database are used to replace the key indicators identified as error values. The identification module stores the water quality data in the water quality database, performs normalization processing on the water quality data in the water quality database, classifies the normalized water quality data into multiple water quality categories, calculates the pollution scores of each water quality category, sets pollution levels for each water quality category based on the pollution scores, determines the water quality category to which the newly acquired water quality data belongs and obtains the corresponding pollution level, and determines that the water quality data is abnormal if the pollution level is greater than the preset pollution level;

[0037] A prediction module, if the water quality data is abnormal, pre - establishes a comparison database. The comparison database stores the normal ranges of each key indicator, compares the key indicators with the corresponding normal ranges, identifies the key indicators that exceed the normal ranges, obtains the geographical location of the corresponding sensor, and sends the geographical location and the key indicators that exceed the range to the visualization module. If the water quality data is not abnormal, a prediction model is trained based on the water quality data in the water quality database, and it is determined whether an abnormality will occur in the future based on the prediction model;

[0038] A visualization module is used to visually display the real - time monitored water quality data and the corresponding geographical locations through a GIS map, identify the key indicators that exceed the range and the corresponding geographical locations with specific marks on the GIS map, and also use different specific marks for identification on the GIS map in the case of predicting that an abnormality will occur in the future. The visualization module also performs chart analysis on historical water quality data.

[0039] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0040] In the present invention, first, water quality monitoring sensors are installed in the industrial park to monitor water quality data in real time. The collected water quality data is also identified to identify the key indicators with errors, and the key indicators collected last time are used for replacement, improving the accuracy of the data and providing data support for subsequent analysis and prediction. In the case where it is determined that the water quality data is abnormal, the water quality data is further analyzed by comparison. By comparing with the normal ranges of each key indicator stored in the comparison database, the key indicators exceeding the normal range are obtained, and the installation location of the sensor corresponding to the key indicator, that is, the geographical location, is also obtained. The key indicators and the geographical location are sent to the visualization module. In the case where the water quality data is normal, a prediction model is also used to predict future abnormal conditions, and the abnormal conditions and the corresponding geographical locations are displayed through the visualization module. The historical water quality data is also analyzed in the form of charts to help the manager intuitively understand the water quality data and water quality trends. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flowchart of the steps of a method for visual monitoring of pollution sources in an industrial park according to the present invention;

[0042] Figure 2 is a structural composition diagram of a system for visual monitoring of pollution sources in an industrial park according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] It can be understood that the terms "first", "second", etc. used in the present application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the present application, the first xx script may be referred to as the second xx script, and similarly, the second xx script may be referred to as the first xx script.

[0045] The present invention provides a method for visual monitoring of pollution sources in an industrial park as Figure 1 shown, which is realized by installing water quality monitoring sensors at the pollution source discharge outlets of each enterprise in the industrial park and performing the following steps:

[0046] Step S1: Real-time monitor the key indicators related to water quality, combine multiple key indicators collected simultaneously into a set of water quality data, and transmit it to the identification module in real time through a wireless network.

[0047] Specifically, in order to visually monitor the pollution sources in the industrial park in real time, water quality monitoring sensors are installed at the pollution discharge outlets of each enterprise in the industrial park. The number of sensors can be multiple, which are used to monitor in real time the key indicators related to water quality. The key indicators include physical parameters such as turbidity, temperature, and flow rate, chemical parameters such as chemical oxygen demand, dissolved oxygen, suspended solids, and pH value, metal ions such as the contents of heavy metals like lead, mercury, and chromium, nutrient salt components such as nitrogen content and phosphorus content, and microorganisms such as total bacteria count and specific pathogens. For example, if different water quality data are collected from multiple sensors within the same time period, these different water quality data are combined into a set of water quality data and transmitted to the recognition module in real time through a wireless network, and the recognition module processes and analyzes these water quality data.

[0048] Step S2: Identify whether there are error values in the key indicators of the water quality data. If so, use the key indicators in the previous set of water quality data stored in the water quality database to replace the key indicators identified as error values. The recognition module stores the water quality data in the water quality database, performs normalization processing on the water quality data in the water quality database, classifies the normalized water quality data into multiple water quality categories, calculates the pollution score for each water quality category, sets the pollution level for each water quality category based on the pollution score, determines the water quality category to which the newly obtained water quality data belongs and obtains the corresponding pollution level, and determines that the water quality data is abnormal if the pollution level is greater than the preset pollution level.

[0049] Specifically, errors may occur during the process of collecting and transmitting data. Analyzing the incorrect water quality data will directly affect the analysis result and lead to inaccurate analysis. In order to effectively identify and remove the error values, improve the data quality, and provide more accurate data for subsequent analysis and prediction, it is necessary to identify whether there are error values in the water quality data. The specific identification method will be explained in detail later. If so, use the key indicators in the previous set of water quality data stored before to replace the error values. Then, store the water quality data without errors in the water quality database for convenient subsequent access. Different key indicators correspond to different numerical values. In order to analyze the water quality data more quickly, normalization processing is also performed on the water quality data. Based on the normalized water quality data, the water quality data is divided into multiple water quality categories, and the pollution score for each water quality category is also calculated. The pollution level is set for each water quality category based on the pollution score. For example, it is set to 6 levels, namely levels 1 - 6. The larger the level number, the greater the pollution degree. Then, when newly obtained water quality data is available, the pollution level of the water quality data can be obtained based on the water quality category of the water quality data by judgment. If the pollution level is greater than the preset pollution level, it is determined that the water quality data is abnormal. For example, there may be excessive emissions.

[0050] Step S3: If the water quality data is abnormal, a comparison database is pre-established. The comparison database stores the normal ranges of each key indicator. Compare the key indicators with the corresponding normal ranges to identify the key indicators that exceed the normal ranges. Obtain the geographical location of the corresponding sensor, and send the geographical location and the key indicators that exceed the range to the visualization module. If the water quality data is not abnormal, a prediction model is trained and generated based on the water quality data in the water quality database, and whether an abnormality will occur in the future is judged based on the prediction model.

[0051] Specifically, in the case where it is determined that the water quality data is abnormal, further comparative analysis is performed on the water quality data. By comparing with the normal ranges of each key indicator stored in the comparison database, the key indicators that exceed the normal ranges are obtained, and the installation location of the sensor corresponding to the key indicator, that is, the geographical location, is also obtained. The key indicators and the geographical location are sent to the visualization module. If it is determined that the water quality data is not abnormal, a prediction model is trained and generated based on the water quality data stored in the water quality database. The specific process of training the prediction model will be explained in detail later. Whether an abnormality will be sent is judged based on the prediction model. If so, relevant managers can be notified to take measures. Through the above method, abnormal water quality data can be analyzed quickly and accurately, and the future water quality data can be predicted using the prediction model.

[0052] Step S4: The visualization module visually displays the real-time monitored water quality data and the corresponding geographical location through a GIS map, and uses specific marks to identify the key indicators that exceed the range and the corresponding geographical location on the GIS map. In the case where it is predicted that an abnormality will occur in the future, different specific marks are also used for identification on the GIS map. The visualization module also performs chart analysis on the historical water quality data.

[0053] Specifically, to help managers understand the situation more intuitively, the visualization module visually displays the real-time monitored water quality data and the corresponding geographical location through a GIS map, and uses specific marks to identify the key indicators that exceed the range and the corresponding geographical location, such as using a prominent red five-pointed star for identification. In the case where it is predicted that an abnormality will occur in the future, different specific marks, such as using a green triangle for identification, are also used on the GIS map to help managers quickly locate the pollution source location and the key indicators that exceed the standard. The visualization module also performs chart analysis on the historical water quality data to help managers understand the pollution trend.

[0054] Furthermore, to identify whether there are error values in the key indicators of the water quality data, the following steps are included:

[0055] Take the water quality data as the target water quality data, obtain the historical water quality data stored in the water quality database, obtain one key indicator in the target water quality data as the target key indicator, obtain all the corresponding historical key indicators in the historical water quality data for the target key indicator, arrange all the historical key indicators in ascending order of numerical value. If the number of all historical key indicators is odd, take the historical key indicator in the middle as the first reference value; otherwise, take the average of the two historical key indicators in the middle as the first reference value. Calculate the difference between the target key indicator and the first reference value as the first difference, and also calculate the differences between all historical key indicators and the first reference value as the second differences. Calculate the second reference value of all the second differences, and calculate the standard value of the target key indicator using the first formula. The first formula is: S = |A - B| / C, where S is the standard value, A is the target key indicator, B is the first reference value, and C is the second reference value. Preset the first threshold. If the standard value is greater than the first threshold, determine the target key indicator as an error value.

[0056] Specifically, to identify the error values of the key indicators in the water quality data, obtain the historical water quality data in the water quality database. To improve the accuracy of identification, analyze each key indicator separately. For a target key indicator in the target water quality data, obtain all the corresponding historical key indicators in the historical water quality data, arrange all the historical key indicators in ascending order of the numerical value of the key indicator. If the number of all historical key indicators is odd, obtain the historical key indicator in the middle as the first reference value; otherwise, take the average of the two historical key indicators in the middle as the first reference value. Calculate the difference between the target key indicator and the first reference value as the first difference, and also calculate the differences between all historical key indicators and the first reference value as the second differences. Also calculate the second reference value of all the second differences, that is, the median of all the second differences. The specific calculation method is the same as the method for calculating the first reference value. Use the above first formula to calculate the standard value corresponding to the target key indicator. Determine the first threshold based on historical experience or statistical methods. If the standard value is greater than the first threshold, then determine the target key indicator as an error value; otherwise, the target key indicator is a correct value. Use the above method to determine whether other target key indicators in the target water quality data are error values. The above method takes the median of all historical key indicators as the first reference value because the median has better resistance to error values and is not as easily affected by extreme values as the mean and standard deviation. Therefore, using the median as the reference value to identify error values can improve the stability of error value identification.

[0057] Further, classify the normalized water quality data into multiple water quality categories, including the following steps:

[0058] Step S21: Predetermine a predetermined number of water quality categories. Randomly select a predetermined number of water quality data from all the water quality data as the first water quality data, and randomly assign the first water quality data to each water quality category, that is, use the first water quality data as the initial data for the corresponding water quality category.

[0059] Step S22: Select one water quality data from the remaining water quality data in the water quality database as the second water quality data. Calculate the first distance between the second water quality data and all the first water quality data, obtain the minimum first distance among the multiple first distances, obtain the water quality category corresponding to the first water quality data corresponding to the minimum first distance, classify the second water quality data into the water quality category, and update the initial data of the water quality category based on the second water quality data.

[0060] Step S23: Repeat Step S22 until all the water quality data have been selected.

[0061] Specifically, in order to quickly and accurately classify the water quality data, a predetermined number of water quality categories are preset. For example, 10 categories are preset in advance. Randomly select 10 water quality data from the water quality data as the first water quality data, and use the first water quality data as the initial data for each water quality category respectively. Then select one water quality data from the remaining water quality data as the second water quality data, and calculate the first distance between the second water quality data and each first water quality data to obtain multiple first distances. The specific method for calculating the first distance will be introduced later. The first distance represents the similarity between two water quality data. The smaller the first distance, the greater the similarity between the two water quality data and the more likely they are to be classified into the same category. Therefore, classify the second water quality data into the water quality category corresponding to the first water quality data corresponding to the latest first distance. Then, also update the initial data of the corresponding water quality category based on the second water quality data to make the two water quality data closer, which is convenient for more accurate classification of the remaining other water quality data later. The specific method for updating the initial data will be explained in detail later. Repeat the above Step S22 until all the water quality data have been classified. Through the above method, the water quality data can be quickly and accurately classified.

[0062] Furthermore, calculating the first distance between the second water quality data and all the first water quality data includes the following steps:

[0063] Use the second formula to calculate the first distance between the second water quality data and each first water quality data. The second formula is:

[0064]

[0065] where D is the first distance, Hi is the i-th key index of the first water quality data, Ei is the i-th key index of the second water quality data, and m refers to the total number of key indexes in the water quality data.

[0066] Specifically, by calculating the first distance between the second water quality data and each first water quality data using the above formula, the first water quality data that is closer to the second water quality is determined, and the second water quality data is classified into the corresponding water quality category, which can help us quickly classify the water quality data.

[0067] Furthermore, based on the second water quality data, the initial data of the water quality category is updated, including the following steps:

[0068] Update the key indicators in the initial data based on the third formula, and the third formula is:

[0069] Hn = Ho + (D min / D max )(E - Ho),

[0070] where Ho is a key indicator in the initial data before update, E is the corresponding key indicator in the second water quality data, Dmin is the minimum first distance, Dmax is the maximum first distance among all first distances, and Hn is the corresponding key indicator in the updated initial data.

[0071] Specifically, since the initial data is randomly selected, each time a water quality data is added to the data in the same category, the initial data is updated using the above formula, so that the initial data better matches the newly added water quality data. By the above method, as the data in the same category increases, the neighborhood in the same category of data is gradually reduced, making the water quality data in the same category more and more similar, and finally completing the classification of the water quality data.

[0072] Further, calculate the pollution score of each water quality category, including the following steps:

[0073] Allocate relevant values to different key indicators based on historical data. The relevant values represent the importance of the key indicators to the pollution score. Obtain all the water quality data in each water quality category, calculate the corresponding average key indicator for each key indicator, and use the fourth formula to calculate the pollution score of each water quality category. The fourth formula is:

[0074]

[0075] where Gi refers to the average key indicator corresponding to the i-th key indicator, and Wi refers to the relevant value corresponding to the i-th key indicator.

[0076] Specifically, since the water quality data of each water quality category are very similar, the pollution score is calculated for each category, and the pollution score of each water quality category is used to represent the pollution score of each water quality data in each water quality category. First, relevant values are assigned to different key indicators based on historical data, and the relevant values represent the importance of the key indicators for calculating the pollution score, that is, the relevant values represent the importance of the key indicators for evaluating the pollution degree. Then, all water quality data of each water quality category are obtained, the corresponding average key indicator is calculated for each key indicator, and the pollution score of each water quality category is calculated using the above formula. Through the above method, the pollution score of each water quality category can be calculated quickly and accurately.

[0077] Furthermore, a prediction model is trained and generated based on the water quality data in the water quality database, including the following steps:

[0078] Step S31: Use a number of water quality data as the first learning data, and generate a first model through training. Collect other data related to water pollution, and obtain a combination of a number of water quality data and other data except the first learning data as the second learning data, and generate a second model through training;

[0079] Step S32: Obtain the latest collected water quality data as the third water quality data, input the third water quality data into the first model to obtain the first prediction data, calculate the first difference between the third water quality data and the first prediction data, obtain the other data corresponding to the third water quality data, input the third water quality data and the other data into the second model to obtain the second prediction data, and calculate the second difference between the second prediction data and the third water quality data;

[0080] Step S33: If the second difference is less than the first difference, calculate the difference between the first difference and the second difference as the third difference, compare the third difference with a preset second threshold. If the third difference is greater than the second threshold, combine the second model and the first model to generate a third model as the prediction model;

[0081] Step S34: If the second difference is greater than or equal to the first difference, continue to collect new other data related to water quality, use the new other data and a number of water quality data as new learning data to train the second model to generate a new second model, and return to Step S32 until the second difference is less than the first difference.

[0082] Specifically, in order to predict the water quality status, a prediction model needs to be trained. In order to quickly train a prediction model with good prediction performance and high prediction accuracy and reduce the cost of training the model, first obtain a number of water quality data as the first learning data, train the first learning data to generate a first model, and also obtain other data related to water pollution, such as meteorological data. Obtain the combination of water quality data and other data other than the first learning data to generate second learning data, train the second learning data to generate a second model. Then obtain the latest water quality data, that is, the most recently collected water quality data as the third water quality data. Input the third water quality data into the first model to output the first prediction data. Input the third water quality data and the corresponding other data into the second model to output the second prediction data. Calculate the first difference between the first prediction data and the latest water quality data. Calculate the second difference between the second prediction data and the latest water quality data. If the second difference is less than the first difference, it means that the prediction accuracy of the second model is higher. Then calculate the difference between the first difference and the second difference as the third difference, and compare the third difference with the preset second threshold. If the third difference is greater than the second threshold, it means that the prediction accuracy of the second model is much higher than that of the first model, and it also means that other data has a good improvement on the prediction result. Therefore, combine the second model and the first model to generate a third model as the final prediction model, so that the prediction model has a high prediction accuracy. If the second difference is greater than or equal to the first difference, it means that compared with the first model, the second model does not improve the prediction accuracy, and it also means that other data has no improvement on the prediction result. Therefore, continue to obtain new other data related to water pollution, such as industrial production data. Combine the new other data and the water quality data as new learning data to train the second model to generate a new second model. Then return to step S32 until the second difference is less than the first difference, that is, the new second model has an obvious prediction accuracy compared with the first model. Combine the second model and the first model to generate the final prediction model. Through the above method, continuously use new other data to improve the prediction accuracy of the prediction model, so as to achieve the purpose of improving the prediction accuracy of the prediction model.

[0083] Further, it is characterized in that judging whether an abnormality will occur in the future based on the prediction model includes the following steps:

[0084] Obtain predicted water quality data based on the prediction model, judge the water quality category of the predicted water quality data, obtain the corresponding pollution score based on the water quality category, obtain the corresponding pollution level based on the pollution score. If the pollution level is greater than the preset pollution level, judge that an abnormal situation will occur in the future.

[0085] Specifically, future predicted water quality data is obtained through a prediction model. The predicted water quality data is analyzed to determine the water quality category of the predicted water quality data, and the pollution score corresponding to the water quality category is obtained. Based on the pollution score, the corresponding pollution level is obtained. If the pollution level is greater than the preset pollution level, it indicates that the future water quality data is abnormal, and one or several key indicators may be abnormal. The prediction result is promptly sent to the visualization module for display, helping the administrator discover abnormal situations in advance so as to take preventive measures.

[0086] According to another aspect of the embodiments of the present invention, as shown in Figure 2 also provided is an industrial park pollution source visualization monitoring system, including a monitoring module, an identification module, a prediction module, and a visualization module, which are used to implement an industrial park pollution source visualization monitoring method described above. The specific functions of each module are as follows:

[0087] The monitoring module is used to monitor in real time the key indicators related to water quality, combine a plurality of key indicators collected simultaneously into a set of water quality data, and transmit the water quality data to the identification module in real time through a wireless network;

[0088] The identification module is used to identify whether there are error values in the key indicators of the water quality data. If so, the key indicators in the previous set of water quality data stored in the water quality database are used to replace the key indicators identified as error values. The identification module stores the water quality data in the water quality database, performs normalization processing on the water quality data in the water quality database, classifies the normalized water quality data into multiple water quality categories, calculates the pollution score for each water quality category, sets the pollution level for each water quality category based on the pollution score, determines the water quality category to which the newly obtained water quality data belongs and obtains the corresponding pollution level, and determines that the water quality data is abnormal if the pollution level is greater than the preset pollution level;

[0089] The prediction module, if the water quality data is abnormal, pre - establishes a comparison database. The comparison database stores the normal ranges of each key indicator, compares the key indicators with the corresponding normal ranges, identifies the key indicators that exceed the normal ranges, obtains the geographical location of the corresponding sensor, and sends the geographical location and the key indicators that exceed the range to the visualization module. If the water quality data is not abnormal, a prediction model is trained based on the water quality data in the water quality database, and it is determined whether an abnormality will occur in the future based on the prediction model;

[0090] The visualization module is used to visually display the real - time monitored water quality data and the corresponding geographical locations through a GIS map, identify the key indicators that exceed the range and the corresponding geographical locations with specific marks on the GIS map, and also use different specific marks to identify them on the GIS map in the case of predicting that an abnormality will occur in the future. The visualization module also performs chart analysis on the historical water quality data.

[0091] In summary, for a visual monitoring system and method for pollution sources in an industrial park according to the present invention, the method includes installing water quality monitoring sensors in each enterprise in the industrial park to monitor key indicators related to water quality in real time, transmitting water quality data to an identification module through a wireless network, identifying error values in the water quality data, and replacing them with corresponding key indicators in the previous set of water quality data, performing normalization processing on the water quality data, classifying the water quality data, calculating a pollution score, setting a pollution level, determining whether the water quality data is abnormal, if the water quality data is abnormal, comparing the normal range of the key indicators, identifying the exceeded indicators, and marking them on a GIS map, if there is no abnormality, training a prediction model based on historical data to predict whether an abnormality will occur in the future, a visualization module displaying the water quality data and geographical locations on a GIS map, specifically marking the abnormal data, and providing a chart analysis of the historical data. The present invention can accurately monitor the pollution sources in the industrial park.

[0092] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages, and these sub-steps or stages do not necessarily have to be executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages does not necessarily have to be sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0093] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The above program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0094] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0095] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent of the present invention should be subject to the appended claims.

[0096] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for visually monitoring pollution sources in industrial parks, wherein water quality monitoring sensors are installed at the pollution source outlets of each enterprise in the industrial park, characterized in that: The steps include: Step S1, real-time monitoring of key indicators related to water quality, combining multiple key indicators collected simultaneously into a set of water quality data, and transmitting the data to the identification module in real time via a wireless network; Step S2, identifying whether the key indicator in the water quality data has an erroneous value, and if so, using the key indicator in the previous set of water quality data stored in the water quality database to replace the key indicator identified as an erroneous value, the identification module stores the water quality data in the water quality database, normalizes the water quality data in the water quality database, classifies the normalized water quality data into multiple water quality categories, calculates the pollution score of each water quality category, sets a pollution level for each water quality category based on the pollution score, determines the water quality category to which the newly acquired water quality data belongs and obtains the corresponding pollution level, and determines that the water quality data is abnormal if the pollution level is greater than the preset pollution level; Step S3: if the water quality data is abnormal, a comparison database is pre-established, the comparison database stores the normal range of each key indicator, the key indicator is compared with the corresponding normal range, the key indicator that exceeds the normal range is identified, the geographical location of the corresponding sensor is obtained, the geographical location and the key indicator that exceeds the range are sent to the visualization module, if the water quality data is not abnormal, a prediction model is generated based on the water quality data in the water quality database, and whether an abnormality will occur in the future is determined based on the prediction model; Step S4, the visualization module visualizes the real-time monitored water quality data and the corresponding geographical locations through the GIS map, and uses specific markers to mark the key indicators and corresponding geographical locations that are out of range on the GIS map. When anomalies are predicted to occur in the future, different specific markers are also used to mark them on the GIS map. The visualization module also performs graphical analysis on historical water quality data.

2. The method according to claim 1, characterized in that Identifying whether the key indicator in the water quality data has an erroneous value comprises the following steps: The water quality data is used as the target water quality data, the historical water quality data stored in the water quality database is obtained, a key indicator in the target water quality data is obtained as the target key indicator, all the historical key indicators corresponding to the historical water quality data are obtained for the target key indicator, all the historical key indicators are arranged in order of numerical value, if the number of all the historical key indicators is an odd number, the historical key indicator in the middle is used as the first reference value, otherwise, the average value of the two historical key indicators in the middle is used as the first reference value, the difference between the target key indicator and the first reference value is calculated as the first difference, the difference between all the historical key indicators and the first reference value is also calculated as the second difference, the second reference value of all the second difference values ​​is calculated, and the standard value of the target key indicator is calculated using a first formula, the first formula is: S=|AB| / C, wherein S is the standard value, A is the target key indicator, B is the first reference value, and C is the second reference value. A first threshold is preset, and if the standard value is greater than the first threshold, the target key indicator is determined to be an error value.

3. The method according to claim 1, characterized in that Classifying the normalized water quality data into multiple water quality categories includes the following steps: Step S21, presetting a predetermined number of water quality categories, randomly selecting a predetermined number of the water quality data from all the water quality data as first water quality data, and randomly assigning the first water quality data to each of the water quality categories, that is, using the first water quality data as initial data corresponding to the water quality category; Step S22: selecting one water quality data from the remaining water quality data in the water quality database as the second water quality data, calculating the first distance between the second water quality data and all the first water quality data, obtaining the minimum first distance among the multiple first distances, obtaining the water quality category corresponding to the first water quality data corresponding to the minimum first distance, classifying the second water quality data into the water quality category, and updating the initial data of the water quality category based on the second water quality data; Step S23, repeating step S22 until all the water quality data have been selected.

4. The method according to claim 3, characterized in that Calculating a first distance between the second water quality data and all the first water quality data comprises the following steps: A second formula is used to calculate a first distance between the second water quality data and each of the first water quality data, wherein the second formula is: Among them, D is the first distance, Hi is the i-th key indicator of the first water quality data, Ei is the i-th key indicator of the second water quality data, and m refers to the total number of key indicators in the water quality data.

5. The method according to claim 3, characterized in that: Updating the initial data of the water quality category based on the second water quality data comprises the following steps: The key indicator in the initial data is updated based on a third formula, and the third formula is: H n =H o +(D min / D max )(E-Ho), Among them, Ho is one of the key indicators in the initial data before updating, E is the corresponding key indicator in the second water quality data, Dmin is the minimum first distance, Dmax is the maximum first distance among all the first distances, and Hn is the corresponding key indicator of the initial data after updating.

6. The method according to claim 1, characterized in that Calculating the pollution score for each water quality category involves the following steps: Based on historical data, different key indicators are assigned relevant values, which represent the importance of the key indicators to the pollution score. All water quality data in each water quality category are obtained, and the corresponding average key indicator is calculated for each key indicator. The pollution score of each water quality category is calculated using the fourth formula, which is: Among them, Gi refers to the average key indicator corresponding to the i-th key indicator, and Wi refers to the relevant value corresponding to the i-th key indicator.

7. The method according to claim 1, characterized in that The prediction model is generated based on the water quality data training in the water quality database, including the following steps: Step S31: using some of the water quality data as first learning data, generating a first model through training, collecting other data related to water pollution, obtaining a combination of some of the water quality data other than the first learning data and the other data as second learning data, and generating a second model through training; Step S32, obtaining the latest collected water quality data as the third water quality data, inputting the third water quality data into the first model to obtain first prediction data, calculating a first difference between the third water quality data and the first prediction data, obtaining the other data corresponding to the third water quality data, inputting the third water quality data and the other data into the second model to obtain second prediction data, and calculating a second difference between the second prediction data and the third water quality data; Step S33: if the second difference is less than the first difference, calculate the difference between the first difference and the second difference as a third difference, compare the third difference with a preset second threshold, and if the third difference is greater than the second threshold, combine the second model with the first model to generate a third model as the prediction model; Step S34: If the second difference is greater than or equal to the first difference, continue to collect new other data related to water quality, use the new other data and some of the water quality data as new learning data to train the second model to generate a new second model, and return to step S32 until the second difference is less than the first difference.

8. The method according to claim 1, characterized in that Judging whether an abnormality will occur in the future based on the prediction model includes the following steps: Based on the prediction model, predicted water quality data is obtained, the water quality category of the predicted water quality data is determined, a corresponding pollution score is obtained based on the water quality category, and a corresponding pollution level is obtained based on the pollution score. If the pollution level is greater than a preset pollution level, it is determined that an abnormal situation will occur in the future.

9. A visual monitoring system for pollution sources in industrial parks, used to implement a visual monitoring method for pollution sources in industrial parks as described in any one of claims 1 to 8, characterized in that: Includes the following modules: The monitoring module is used to monitor key indicators related to water quality in real time, combine multiple key indicators collected simultaneously into a set of water quality data, and transmit it to the identification module in real time through the wireless network; an identification module, for identifying whether the key indicator in the water quality data has an erroneous value, and if so, using the key indicator in the previous set of water quality data stored in the water quality database to replace the key indicator identified as an erroneous value, the identification module stores the water quality data in the water quality database, normalizes the water quality data in the water quality database, classifies the normalized water quality data into multiple water quality categories, calculates a pollution score for each water quality category, sets a pollution level for each water quality category based on the pollution score, determines the water quality category to which the newly acquired water quality data belongs and obtains the corresponding pollution level, and determines that the water quality data is abnormal if the pollution level is greater than a preset pollution level; The prediction module, if the water quality data is abnormal, pre-establishes a comparison database, the comparison database stores the normal range of each key indicator, compares the key indicator with the corresponding normal range, identifies the key indicator that exceeds the normal range, obtains the geographical location of the corresponding sensor, sends the geographical location and the key indicator that exceeds the range to the visualization module, and if the water quality data is not abnormal, generates a prediction model based on the water quality data in the water quality database, and determines whether an abnormality will occur in the future based on the prediction model; The visualization module is used to visualize the real-time monitored water quality data and the corresponding geographical locations through the GIS map. The key indicators and corresponding geographical locations that are out of range are marked with specific markers on the GIS map. When abnormalities are predicted to occur in the future, different specific markers are also used to mark them on the GIS map. The visualization module also performs graphical analysis on historical water quality data.

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