Data processing method and system for environment monitoring

Abstract

The invention relates to the technical field of data processing, and discloses a data processing method and system for environmental monitoring, and the system comprises a data collection unit which is configured to determine an abnormal monitoring sample according to a comparison result; the abnormity monitoring unit is configured to determine the abnormity deviation degree of the abnormity monitoring sample according to the correlation index; the abnormity judgment unit is configured to judge whether the abnormity deviation degree is adjusted or not according to the abnormity influence factor; the exception handling unit is configured to determine an adjustment coefficient of the exception deviation degree according to a comparison result when the enhanced monitoring sample is compared with a historical normal monitoring sample, and obtain an adjustment exception deviation degree; and the abnormity early warning unit is configured to perform environment monitoring early warning according to the adjusted abnormity deviation degree. Through mutual cooperation of the data acquisition unit, the abnormity monitoring unit, the abnormity judgment unit, the abnormity processing unit and the abnormity early warning unit, comprehensive, efficient and accurate processing of environment monitoring data is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data processing method and system for environmental monitoring. Background Art

[0002] Environmental monitoring data plays a vital role in environmental protection and sustainable development. These data cover many key areas such as air quality, water quality, soil pollution, etc. They are the basis for assessing environmental conditions, formulating relevant policies and taking corresponding measures. However, traditional environmental monitoring data processing methods often face problems such as large data volume, high processing complexity, and inaccurate abnormal monitoring.

[0003] Therefore, it is necessary to design a data processing method and system for environmental monitoring to solve the problems existing in the current technology. Summary of the invention

[0004] In view of this, the present invention proposes a data processing method and system for environmental monitoring, aiming to improve the processing efficiency and accuracy of environmental monitoring data.

[0005] In one aspect, the present invention provides a data processing system for environmental monitoring, comprising: A data collection unit is configured to collect environmental monitoring data of each monitoring point, and compare the environmental monitoring data with a normal monitoring range, and determine abnormal monitoring samples according to the comparison result; an abnormality monitoring unit, configured to calculate a correlation index between the abnormality monitoring sample and a historical abnormality monitoring sample, and determine an abnormal deviation of the abnormality monitoring sample according to the correlation index; an abnormality judgment unit, configured to calculate an abnormality impact factor of a monitoring point corresponding to environmental monitoring data in an abnormal monitoring sample, and judge whether to adjust the abnormal deviation according to the abnormality impact factor; an abnormality processing unit configured to, when the abnormality judgment unit determines to adjust the abnormal deviation, perform enhancement processing on the abnormal monitoring sample and obtain an enhanced monitoring sample; compare the enhanced monitoring sample with a historical normal monitoring sample, determine an adjustment coefficient of the abnormal deviation according to the comparison result, and obtain an adjusted abnormal deviation; The abnormal warning unit is configured to perform environmental monitoring and early warning according to the adjusted abnormal deviation.

[0006] Furthermore, the environmental monitoring data is compared with the normal monitoring range, and the abnormal monitoring samples are determined according to the comparison results, including: Extracting features from the environmental monitoring data to obtain environmental monitoring feature values; Determine a normal monitoring range corresponding to each of the environmental monitoring characteristic values; Comparing each of the environmental monitoring characteristic values ​​with the corresponding normal monitoring range one by one, and determining the abnormal monitoring sample according to the comparison result; When the environmental monitoring characteristic values ​​are all within the normal monitoring range, the monitoring point corresponding to the environmental monitoring characteristic value is determined to be a normal monitoring point; When the environmental monitoring characteristic value exceeds the normal monitoring range, the monitoring point corresponding to the environmental monitoring characteristic value is determined to be an abnormal monitoring point, and the environmental monitoring data containing the environmental monitoring characteristic value is used as the abnormal monitoring sample.

[0007] Furthermore, when calculating the correlation index between the abnormal monitoring sample and the historical abnormal monitoring sample, it includes: The correlation index is obtained by the following formula:

[0008] Among them, x represents the feature vector of the anomaly monitoring sample; y represents the historical feature vector of the historical anomaly monitoring sample; n represents the dimension of the feature; xi represents the i-th feature vector in the anomaly monitoring sample; yi represents the i-th historical feature vector in the historical anomaly monitoring sample.

[0009] Further, when determining the abnormal deviation of the abnormal monitoring sample according to the correlation index, it includes: Comparing the correlation index with a first correlation index and a second correlation index, and determining the abnormal deviation of the abnormal monitoring sample according to the comparison result; wherein the first correlation index is smaller than the second correlation index; When the correlation index is less than the first correlation index, determining the abnormal deviation of the abnormal monitoring sample to be a first abnormal deviation; When the correlation index is greater than or equal to the first correlation index and less than the second correlation index, determining the abnormal deviation of the abnormal monitoring sample to be a second abnormal deviation; When the correlation index is greater than or equal to the second correlation index, the abnormal deviation degree of the abnormal monitoring sample is determined to be a third abnormal deviation degree.

[0010] Furthermore, when calculating the abnormal impact factor of the monitoring point corresponding to the environmental monitoring data in the abnormal monitoring sample, it includes: Collecting the sensor accuracy and historical abnormality rate of the monitoring point, and performing weighted calculation on the sensor accuracy and the historical abnormality rate to determine the abnormal impact value of the monitoring point, and determining the abnormal standard value corresponding to the abnormal impact value; The abnormal impact factor is obtained by the following formula:

[0011] Among them, Im represents the abnormal impact factor; AIV represents the abnormal impact value; ASV represents the abnormal standard value.

[0012] Further, judging whether to adjust the abnormal deviation degree according to the abnormal impact factor includes: Compare the abnormal impact factor with the abnormal impact factor threshold, and determine whether to adjust the abnormal deviation according to the comparison result; When the abnormal impact factor is greater than the abnormal impact factor threshold, determining to adjust the abnormal deviation; When the abnormal impact factor is less than or equal to the abnormal impact factor threshold, it is determined that the abnormal deviation is not adjusted.

[0013] Furthermore, the enhanced monitoring sample is compared with the historical normal monitoring sample, and the adjustment coefficient of the abnormal deviation is determined according to the comparison result, and the adjusted abnormal deviation is obtained, including: When there is a historical monitoring sample that is the same as the enhanced monitoring sample in the historical normal monitoring sample, the abnormal deviation is adjusted according to the historical adjustment coefficient corresponding to the historical monitoring sample, and the product value of the historical adjustment coefficient and the abnormal deviation is used as the adjusted abnormal deviation; When there is no historical monitoring sample that is identical to the enhanced monitoring sample in the historical normal monitoring samples, the monitoring deviation values ​​of the enhanced monitoring samples and the historical normal monitoring samples are calculated one by one, and a monitoring deviation data set is constructed. The adjustment coefficient of the abnormal deviation is determined according to the monitoring deviation data set, and the adjusted abnormal deviation is obtained.

[0014] Further, determining the adjustment coefficient of the abnormal deviation according to the monitoring deviation data set and obtaining the adjusted abnormal deviation includes: Extracting a maximum monitoring deviation value from the monitoring deviation data set, comparing the maximum monitoring deviation value with a first maximum monitoring deviation value and a second maximum monitoring deviation value, and determining an adjustment coefficient of the abnormal deviation according to the comparison result; wherein the first maximum monitoring deviation value is less than the second maximum monitoring deviation value; Setting an adjustment coefficient interval, wherein the adjustment coefficient interval includes a first adjustment coefficient, a second adjustment coefficient, and a third adjustment coefficient; When the maximum monitoring deviation value is less than the first maximum monitoring deviation value, determining the adjustment coefficient of the abnormal deviation as the first adjustment coefficient, and taking the product value of the first adjustment coefficient and the abnormal deviation as the adjusted abnormal deviation; When the maximum monitoring deviation value is greater than or equal to the first maximum monitoring deviation value and less than the second maximum monitoring deviation value, determining the adjustment coefficient of the abnormal deviation as the second adjustment coefficient, and taking the product value of the second adjustment coefficient and the abnormal deviation as the adjusted abnormal deviation; When the maximum monitoring deviation value is greater than or equal to the second maximum monitoring deviation value, the adjustment coefficient of the abnormal deviation is determined to be a third adjustment coefficient, and the product value of the third adjustment coefficient and the abnormal deviation is used as the adjusted abnormal deviation.

[0015] Further, when performing environmental monitoring early warning according to the adjustment abnormal deviation, it includes: Calculating the ratio of the adjusted abnormal deviation to the adjusted abnormal deviation threshold to obtain a deviation ratio, and determining the environmental monitoring early warning level according to the deviation ratio; When the deviation ratio is greater than 0 and less than or equal to 0.3, the environmental monitoring warning level is determined to be a level one warning; When the deviation ratio is greater than 0.3 and less than or equal to 0.7, the environmental monitoring warning level is determined to be a level 2 warning; When the deviation ratio is greater than 0.7, the environmental monitoring warning level is determined to be a level three warning.

[0016] Compared with the prior art, the beneficial effect of the present invention is that the data processing system for environmental monitoring provided by the present invention realizes comprehensive, efficient and accurate processing of environmental monitoring data through the cooperation of the data acquisition unit, the abnormal monitoring unit, the abnormal judgment unit, the abnormal processing unit and the abnormal early warning unit. The data acquisition unit is used as the basis to ensure the accuracy and integrity of the data; the abnormal monitoring unit can quickly identify abnormal monitoring samples by calculating the correlation index, thereby improving the accuracy of abnormal monitoring; the abnormal judgment unit further adjusts the abnormal deviation according to the abnormal influencing factor, thereby enhancing the flexibility of processing; the abnormal processing unit optimizes the data processing results by enhancing the processing and adjusting the abnormal deviation; finally, the abnormal early warning unit performs environmental monitoring early warning according to the adjusted abnormal deviation, thereby providing strong support for environmental protection.

[0017] In another aspect, the present invention further provides a data processing method for environmental monitoring, comprising the following steps: S100: Collect environmental monitoring data from each monitoring point, compare the environmental monitoring data with a normal monitoring range, and determine abnormal monitoring samples based on the comparison results; S200: Calculating a correlation index between the abnormal monitoring sample and a historical abnormal monitoring sample, and determining an abnormal deviation of the abnormal monitoring sample according to the correlation index; S300: Calculating the abnormal impact factor of the monitoring point corresponding to the environmental monitoring data in the abnormal monitoring sample, and judging whether to adjust the abnormal deviation according to the abnormal impact factor; S400: When it is determined that the abnormal deviation is to be adjusted, the abnormal monitoring sample is enhanced and an enhanced monitoring sample is obtained; the enhanced monitoring sample is compared with a historical normal monitoring sample, an adjustment coefficient of the abnormal deviation is determined according to the comparison result, and an adjusted abnormal deviation is obtained; S500: Performing environmental monitoring and early warning according to the adjustment abnormal deviation.

[0018] It is understandable that the above-mentioned data processing method and system for environmental monitoring have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A structural block diagram of a data processing system for environmental monitoring provided by an embodiment of the present invention; Figure 2 A flow chart of a data processing method for environmental monitoring provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] See also Figure 1 As shown, in some embodiments of the present application, this embodiment provides a data processing system for environmental monitoring, including: A data collection unit is configured to collect environmental monitoring data of each monitoring point, and compare the environmental monitoring data with a normal monitoring range, and determine abnormal monitoring samples according to the comparison result; an abnormality monitoring unit, configured to calculate a correlation index between the abnormality monitoring sample and a historical abnormality monitoring sample, and determine an abnormal deviation of the abnormality monitoring sample according to the correlation index; an abnormality judgment unit, configured to calculate an abnormality impact factor of a monitoring point corresponding to environmental monitoring data in an abnormal monitoring sample, and judge whether to adjust the abnormal deviation according to the abnormality impact factor; an abnormality processing unit configured to, when the abnormality judgment unit determines to adjust the abnormal deviation, perform enhancement processing on the abnormal monitoring sample and obtain an enhanced monitoring sample; compare the enhanced monitoring sample with a historical normal monitoring sample, determine an adjustment coefficient of the abnormal deviation according to the comparison result, and obtain an adjusted abnormal deviation; The abnormal warning unit is configured to perform environmental monitoring and early warning according to the adjusted abnormal deviation.

[0022] In some embodiments of the present invention, one environmental monitoring data is composed of several monitoring parameters, including temperature value, humidity value, PM2.5 concentration value, sulfur dioxide concentration value, nitrogen oxide concentration value, etc. These monitoring parameters can fully reflect the environmental quality status and provide a scientific basis for environmental protection.

[0023] In some embodiments of the present invention, when the abnormal monitoring samples are enhanced, the abnormal monitoring samples are enhanced using a data enhancement algorithm, and the data enhancement algorithm includes but is not limited to data smoothing, data interpolation, data amplification, or data transformation, etc., to improve the data quality and reliability of the abnormal monitoring samples. By enhancing the abnormal monitoring samples, potential environmental problems can be more effectively discovered, providing more accurate data support for subsequent environmental monitoring warnings.

[0024] It can be understood that the data processing system for environmental monitoring provided by this embodiment realizes comprehensive, efficient and accurate processing of environmental monitoring data through the cooperation of the data acquisition unit, the abnormal monitoring unit, the abnormal judgment unit, the abnormal processing unit and the abnormal warning unit. The data acquisition unit is used as the basis to ensure the accuracy and integrity of the data; the abnormal monitoring unit can quickly identify abnormal monitoring samples by calculating the correlation index, thereby improving the accuracy of abnormal monitoring; the abnormal judgment unit further adjusts the abnormal deviation according to the abnormal influencing factor, thereby enhancing the flexibility of processing; the abnormal processing unit optimizes the data processing results by enhancing the processing and adjusting the abnormal deviation; finally, the abnormal warning unit performs environmental monitoring warning according to the adjusted abnormal deviation, thereby providing strong support for environmental protection.

[0025] Specifically, the environmental monitoring data is compared with the normal monitoring range, and the abnormal monitoring samples are determined according to the comparison results, including: Extracting features from the environmental monitoring data to obtain environmental monitoring feature values; Determine a normal monitoring range corresponding to each of the environmental monitoring characteristic values; Comparing each of the environmental monitoring characteristic values ​​with the corresponding normal monitoring range one by one, and determining the abnormal monitoring sample according to the comparison result; When the environmental monitoring characteristic values ​​are all within the normal monitoring range, the monitoring point corresponding to the environmental monitoring characteristic value is determined to be a normal monitoring point; When the environmental monitoring characteristic value exceeds the normal monitoring range, the monitoring point corresponding to the environmental monitoring characteristic value is determined to be an abnormal monitoring point, and the environmental monitoring data containing the environmental monitoring characteristic value is used as the abnormal monitoring sample.

[0026] In some embodiments of the present invention, the normal monitoring range is a reasonable interval set based on historical monitoring data and scientific evaluation. This interval can reflect the normal fluctuation range of environmental quality and is used to compare with environmental monitoring data to determine whether there is an abnormal situation.

[0027] In some embodiments of the present invention, the environmental monitoring characteristic values ​​include temperature characteristic values, humidity characteristic values, PM2.5 concentration characteristic values, sulfur dioxide concentration characteristic values, and nitrogen oxide concentration characteristic values.

[0028] It can be understood that by comparing the environmental monitoring characteristic values ​​with the corresponding normal monitoring ranges and then determining the abnormal monitoring samples, this not only improves the efficiency of environmental monitoring, but also ensures the accuracy of the monitoring results.

[0029] Specifically, when calculating the correlation index between the abnormal monitoring sample and the historical abnormal monitoring sample, it includes: The correlation index is obtained by the following formula:

[0030] Among them, x represents the feature vector of the anomaly monitoring sample; y represents the historical feature vector of the historical anomaly monitoring sample; n represents the dimension of the feature; xi represents the i-th feature vector in the anomaly monitoring sample; yi represents the i-th historical feature vector in the historical anomaly monitoring sample.

[0031] It can be understood that the correlation index calculated by the above formula can quantify the similarity between the abnormal monitoring sample and the historical abnormal monitoring sample. When the correlation index is high, it indicates that the current abnormal monitoring sample has a high similarity with some abnormal events in history, which means that the same or similar environmental problems have reappeared. This similarity analysis helps to quickly identify the root cause of the problem and provide a basis for taking targeted treatment measures.

[0032] Specifically, when determining the abnormal deviation of the abnormal monitoring sample according to the correlation index, it includes: Comparing the correlation index with a first correlation index and a second correlation index, and determining the abnormal deviation of the abnormal monitoring sample according to the comparison result; wherein the first correlation index is smaller than the second correlation index; When the correlation index is less than the first correlation index, determining the abnormal deviation of the abnormal monitoring sample to be a first abnormal deviation; When the correlation index is greater than or equal to the first correlation index and less than the second correlation index, determining the abnormal deviation of the abnormal monitoring sample to be a second abnormal deviation; When the correlation index is greater than or equal to the second correlation index, the abnormal deviation degree of the abnormal monitoring sample is determined to be a third abnormal deviation degree.

[0033] It is understandable that by setting different correlation index thresholds (first correlation index, second correlation index), the abnormal deviation of abnormal monitoring samples is divided into different levels. This hierarchical processing method helps to more finely assess the severity of environmental problems and provide a reference for formulating corresponding levels of response measures. Specifically, the first abnormal deviation indicates a low degree of abnormality; the second abnormal deviation indicates a medium degree of abnormality; and the third abnormal deviation indicates a high degree of abnormality.

[0034] Specifically, when calculating the abnormal impact factor of the monitoring point corresponding to the environmental monitoring data in the abnormal monitoring sample, it includes: Collecting the sensor accuracy and historical abnormality rate of the monitoring point, and performing weighted calculation on the sensor accuracy and the historical abnormality rate to determine the abnormal impact value of the monitoring point, and determining the abnormal standard value corresponding to the abnormal impact value; The abnormal impact factor is obtained by the following formula:

[0035] Among them, Im represents the abnormal impact factor; AIV represents the abnormal impact value; ASV represents the abnormal standard value.

[0036] In some embodiments of the present invention, the historical anomaly rate refers to the frequency of anomalies at a specific monitoring point in the past period of time. This frequency reflects the stability and reliability of the environmental quality of the monitoring point. The higher the historical anomaly rate, the more likely the monitoring point was to have anomalies in the past.

[0037] It is understandable that the abnormal impact factor Im reflects the possibility and severity of abnormal data at the monitoring point. By comprehensively considering the sensor accuracy and historical abnormality rate, the system can more accurately evaluate the data reliability of the monitoring point, thereby providing strong support for subsequent processing. The higher the sensor accuracy, the more accurate the data and the lower the possibility of abnormality; while the higher the historical abnormality rate, it means that the monitoring point has frequently experienced abnormalities in the past, so the risk of current data abnormality will also increase accordingly.

[0038] Specifically, judging whether to adjust the abnormal deviation according to the abnormal impact factor includes: Compare the abnormal impact factor with the abnormal impact factor threshold, and determine whether to adjust the abnormal deviation according to the comparison result; When the abnormal impact factor is greater than the abnormal impact factor threshold, determining to adjust the abnormal deviation; When the abnormal impact factor is less than or equal to the abnormal impact factor threshold, it is determined that the abnormal deviation is not adjusted.

[0039] It is understandable that by setting the threshold of the abnormal impact factor, the system can intelligently determine whether the abnormal deviation needs to be further adjusted. When the abnormal impact factor exceeds the threshold, it means that the data anomaly of the current monitoring point may be more serious or there are special circumstances, so the abnormal deviation needs to be carefully adjusted to ensure the accuracy and pertinence of the early warning information. This judgment mechanism based on abnormal impact factors enhances the system's ability to handle complex environmental monitoring situations and improves the flexibility and adaptability of data processing.

[0040] Specifically, the enhanced monitoring sample is compared with the historical normal monitoring sample, and the adjustment coefficient of the abnormal deviation is determined according to the comparison result, and the adjusted abnormal deviation is obtained, including: When there is a historical monitoring sample that is the same as the enhanced monitoring sample in the historical normal monitoring sample, the abnormal deviation is adjusted according to the historical adjustment coefficient corresponding to the historical monitoring sample, and the product value of the historical adjustment coefficient and the abnormal deviation is used as the adjusted abnormal deviation; When there is no historical monitoring sample that is identical to the enhanced monitoring sample in the historical normal monitoring samples, the monitoring deviation values ​​of the enhanced monitoring samples and the historical normal monitoring samples are calculated one by one, and a monitoring deviation data set is constructed. The adjustment coefficient of the abnormal deviation is determined according to the monitoring deviation data set, and the adjusted abnormal deviation is obtained.

[0041] In some embodiments of the present invention, historical normal monitoring samples refer to environmental monitoring data samples that have been verified and confirmed to be in a normal state over a period of time in the past. These samples represent the fluctuation range of environmental quality under normal conditions and provide a benchmark for comparison of current monitoring data.

[0042] It is understandable that when the enhanced monitoring samples are compared with the historical normal monitoring samples, the system can identify abnormal changes in the monitoring data and intelligently determine the adjustment coefficient of the abnormal deviation based on historical experience and data characteristics. This comparison and adjustment method based on historical data not only improves the accuracy and reliability of data processing, but also helps to quickly identify abnormal changes in environmental quality and provide a scientific basis for environmental protection and decision-making.

[0043] Specifically, determining the adjustment coefficient of the abnormal deviation according to the monitoring deviation data set and obtaining the adjusted abnormal deviation includes: Extracting a maximum monitoring deviation value from the monitoring deviation data set, comparing the maximum monitoring deviation value with a first maximum monitoring deviation value and a second maximum monitoring deviation value, and determining an adjustment coefficient of the abnormal deviation according to the comparison result; wherein the first maximum monitoring deviation value is less than the second maximum monitoring deviation value; Setting an adjustment coefficient interval, wherein the adjustment coefficient interval includes a first adjustment coefficient, a second adjustment coefficient, and a third adjustment coefficient; When the maximum monitoring deviation value is less than the first maximum monitoring deviation value, determining the adjustment coefficient of the abnormal deviation as the first adjustment coefficient, and taking the product value of the first adjustment coefficient and the abnormal deviation as the adjusted abnormal deviation; When the maximum monitoring deviation value is greater than or equal to the first maximum monitoring deviation value and less than the second maximum monitoring deviation value, determining the adjustment coefficient of the abnormal deviation as the second adjustment coefficient, and taking the product value of the second adjustment coefficient and the abnormal deviation as the adjusted abnormal deviation; When the maximum monitoring deviation value is greater than or equal to the second maximum monitoring deviation value, the adjustment coefficient of the abnormal deviation is determined to be a third adjustment coefficient, and the product value of the third adjustment coefficient and the abnormal deviation is used as the adjusted abnormal deviation.

[0044] In some embodiments of the present invention, the first adjustment coefficient>the second adjustment coefficient>the third adjustment coefficient, which means that as the monitoring deviation increases, the adjustment coefficient gradually decreases, thereby achieving fine adjustment of the abnormal deviation. This adjustment strategy takes into account both the reference value of historical data and the actual situation of current monitoring data, making the adjustment of abnormal deviation more scientific and reasonable.

[0045] It is understandable that through careful analysis of monitoring deviations and reasonable setting of adjustment coefficients, the system can more accurately reflect the degree of abnormal environmental quality and provide more reliable data support for environmental protection and decision-making. This comprehensive processing mechanism based on historical experience and real-time monitoring data not only improves the efficiency and accuracy of environmental monitoring, but also helps to discover and solve environmental problems in a timely manner.

[0046] Specifically, when performing environmental monitoring early warning according to the adjustment of abnormal deviation, it includes: Calculating the ratio of the adjusted abnormal deviation to the adjusted abnormal deviation threshold to obtain a deviation ratio, and determining the environmental monitoring early warning level according to the deviation ratio; When the deviation ratio is greater than 0 and less than or equal to 0.3, the environmental monitoring warning level is determined to be a level one warning; When the deviation ratio is greater than 0.3 and less than or equal to 0.7, the environmental monitoring warning level is determined to be a level 2 warning; When the deviation ratio is greater than 0.7, the environmental monitoring warning level is determined to be a level three warning.

[0047] It is understandable that by comparing the adjusted abnormal deviation with the preset threshold, the system can quantify the degree of abnormality of environmental quality into different warning levels. The first-level warning means that the degree of abnormality of environmental quality is relatively mild and may not have a significant impact on the ecological environment and human health; the second-level warning means that the degree of abnormality of environmental quality is moderate, which requires the attention of relevant departments and certain preventive measures; and the third-level warning indicates that the degree of abnormality of environmental quality is serious, which may pose a serious threat to the environment and public health, and immediate action is needed to intervene. This hierarchical warning mechanism helps relevant departments to formulate scientific and reasonable response measures based on the actual situation of environmental quality, thereby effectively protecting the safety of the ecological environment and public health.

[0048] See also Figure 2 As shown, in some embodiments of the present application, this embodiment provides a data processing method for environmental monitoring, comprising the following steps: S100: Collect environmental monitoring data from each monitoring point, compare the environmental monitoring data with a normal monitoring range, and determine abnormal monitoring samples based on the comparison results; S200: Calculating a correlation index between the abnormal monitoring sample and a historical abnormal monitoring sample, and determining an abnormal deviation of the abnormal monitoring sample according to the correlation index; S300: Calculating the abnormal impact factor of the monitoring point corresponding to the environmental monitoring data in the abnormal monitoring sample, and judging whether to adjust the abnormal deviation according to the abnormal impact factor; S400: When it is determined that the abnormal deviation is to be adjusted, the abnormal monitoring sample is enhanced and an enhanced monitoring sample is obtained; the enhanced monitoring sample is compared with a historical normal monitoring sample, an adjustment coefficient of the abnormal deviation is determined according to the comparison result, and an adjusted abnormal deviation is obtained; S500: Performing environmental monitoring and early warning according to the adjustment abnormal deviation.

[0049] It can be understood that through the above steps, a comprehensive analysis and processing of environmental monitoring data is achieved. From data collection to the determination of abnormal monitoring samples, to the calculation and adjustment of abnormal deviation, and finally to the issuance of environmental monitoring warnings, the entire process forms a closed loop, ensuring the accuracy and timeliness of environmental monitoring. This data processing method not only improves the efficiency of environmental monitoring, but also helps to discover and solve environmental problems in a timely manner, providing strong support for environmental protection and decision-making.

[0050] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0051] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A data processing system for environmental monitoring, characterized in that: include: A data collection unit is configured to collect environmental monitoring data of each monitoring point, and compare the environmental monitoring data with a normal monitoring range, and determine abnormal monitoring samples according to the comparison result; an abnormality monitoring unit, configured to calculate a correlation index between the abnormality monitoring sample and a historical abnormality monitoring sample, and determine an abnormal deviation of the abnormality monitoring sample according to the correlation index; an abnormality judgment unit, configured to calculate an abnormality impact factor of a monitoring point corresponding to environmental monitoring data in an abnormal monitoring sample, and judge whether to adjust the abnormal deviation according to the abnormality impact factor; an abnormality processing unit configured to, when the abnormality judgment unit determines to adjust the abnormal deviation, perform enhancement processing on the abnormal monitoring sample and obtain an enhanced monitoring sample; compare the enhanced monitoring sample with a historical normal monitoring sample, determine an adjustment coefficient of the abnormal deviation according to the comparison result, and obtain an adjusted abnormal deviation; The abnormal warning unit is configured to perform environmental monitoring and early warning according to the adjusted abnormal deviation.

2. The data processing system for environmental monitoring according to claim 1, characterized in that: Comparing the environmental monitoring data with the normal monitoring range and determining abnormal monitoring samples based on the comparison results include: Extracting features from the environmental monitoring data to obtain environmental monitoring feature values; Determine a normal monitoring range corresponding to each of the environmental monitoring characteristic values; Comparing each of the environmental monitoring characteristic values ​​with the corresponding normal monitoring range one by one, and determining the abnormal monitoring sample according to the comparison result; When the environmental monitoring characteristic values ​​are all within the normal monitoring range, the monitoring point corresponding to the environmental monitoring characteristic value is determined to be a normal monitoring point; When the environmental monitoring characteristic value exceeds the normal monitoring range, the monitoring point corresponding to the environmental monitoring characteristic value is determined to be an abnormal monitoring point, and the environmental monitoring data containing the environmental monitoring characteristic value is used as the abnormal monitoring sample.

3. The data processing system for environmental monitoring according to claim 2, characterized in that: When calculating the correlation index between the abnormal monitoring sample and the historical abnormal monitoring sample, it includes: The correlation index is obtained by the following formula: ； Among them, x represents the feature vector of the anomaly monitoring sample; y represents the historical feature vector of the historical anomaly monitoring sample; n represents the dimension of the feature; xi represents the i-th feature vector in the anomaly monitoring sample; yi represents the i-th historical feature vector in the historical anomaly monitoring sample.

4. The data processing system for environmental monitoring according to claim 3, characterized in that: When determining the abnormal deviation of the abnormal monitoring sample according to the correlation index, it includes: Comparing the correlation index with a first correlation index and a second correlation index, and determining the abnormal deviation of the abnormal monitoring sample according to the comparison result; wherein the first correlation index is smaller than the second correlation index; When the correlation index is less than the first correlation index, determining the abnormal deviation of the abnormal monitoring sample as a first abnormal deviation; When the correlation index is greater than or equal to the first correlation index and less than the second correlation index, determining the abnormal deviation of the abnormal monitoring sample to be a second abnormal deviation; When the correlation index is greater than or equal to the second correlation index, the abnormal deviation degree of the abnormal monitoring sample is determined to be a third abnormal deviation degree.

5. The data processing system for environmental monitoring according to claim 1, characterized in that: When calculating the abnormal impact factor of the monitoring point corresponding to the environmental monitoring data in the abnormal monitoring sample, it includes: Collecting the sensor accuracy and historical abnormality rate of the monitoring point, and performing weighted calculation on the sensor accuracy and the historical abnormality rate to determine the abnormal impact value of the monitoring point, and determining the abnormal standard value corresponding to the abnormal impact value; The abnormal impact factor is obtained by the following formula: ； Among them, Im represents the abnormal impact factor; AIV represents the abnormal impact value; ASV represents the abnormal standard value.

6. The data processing system for environmental monitoring according to claim 5, characterized in that: When judging whether to adjust the abnormal deviation according to the abnormal impact factor, it includes: Compare the abnormal impact factor with the abnormal impact factor threshold, and determine whether to adjust the abnormal deviation according to the comparison result; When the abnormal impact factor is greater than the abnormal impact factor threshold, determining to adjust the abnormal deviation; When the abnormal impact factor is less than or equal to the abnormal impact factor threshold, it is determined that the abnormal deviation is not adjusted.

7. The data processing system for environmental monitoring according to claim 1, characterized in that: The enhanced monitoring sample is compared with the historical normal monitoring sample, and the adjustment coefficient of the abnormal deviation is determined according to the comparison result, and the adjusted abnormal deviation is obtained, including: When there is a historical monitoring sample that is the same as the enhanced monitoring sample in the historical normal monitoring sample, the abnormal deviation is adjusted according to the historical adjustment coefficient corresponding to the historical monitoring sample, and the product value of the historical adjustment coefficient and the abnormal deviation is used as the adjusted abnormal deviation; When there is no historical monitoring sample that is identical to the enhanced monitoring sample in the historical normal monitoring samples, the monitoring deviation values ​​of the enhanced monitoring samples and the historical normal monitoring samples are calculated one by one, and a monitoring deviation data set is constructed. The adjustment coefficient of the abnormal deviation is determined according to the monitoring deviation data set, and the adjusted abnormal deviation is obtained.

8. The data processing system for environmental monitoring according to claim 7, characterized in that: Determining the adjustment coefficient of the abnormal deviation according to the monitoring deviation data set and obtaining the adjusted abnormal deviation includes: Extracting a maximum monitoring deviation value from the monitoring deviation data set, comparing the maximum monitoring deviation value with a first maximum monitoring deviation value and a second maximum monitoring deviation value, and determining an adjustment coefficient of the abnormal deviation according to the comparison result; wherein the first maximum monitoring deviation value is less than the second maximum monitoring deviation value; Setting an adjustment coefficient interval, wherein the adjustment coefficient interval includes a first adjustment coefficient, a second adjustment coefficient, and a third adjustment coefficient; When the maximum monitoring deviation value is less than the first maximum monitoring deviation value, determining the adjustment coefficient of the abnormal deviation as the first adjustment coefficient, and taking the product value of the first adjustment coefficient and the abnormal deviation as the adjusted abnormal deviation; When the maximum monitoring deviation value is greater than or equal to the first maximum monitoring deviation value and less than the second maximum monitoring deviation value, determining the adjustment coefficient of the abnormal deviation as the second adjustment coefficient, and taking the product value of the second adjustment coefficient and the abnormal deviation as the adjusted abnormal deviation; When the maximum monitoring deviation value is greater than or equal to the second maximum monitoring deviation value, the adjustment coefficient of the abnormal deviation is determined to be a third adjustment coefficient, and the product value of the third adjustment coefficient and the abnormal deviation is used as the adjusted abnormal deviation.

9. The data processing system for environmental monitoring according to claim 8, characterized in that: When the environmental monitoring early warning is carried out according to the adjustment abnormal deviation, it includes: Calculating the ratio of the adjusted abnormal deviation to the adjusted abnormal deviation threshold to obtain a deviation ratio, and determining the environmental monitoring early warning level according to the deviation ratio; When the deviation ratio is greater than 0 and less than or equal to 0.3, the environmental monitoring warning level is determined to be a level one warning; When the deviation ratio is greater than 0.3 and less than or equal to 0.7, the environmental monitoring warning level is determined to be a level 2 warning; When the deviation ratio is greater than 0.7, the environmental monitoring warning level is determined to be a level three warning.

10. A data processing method for environmental monitoring, applied to a data processing system for environmental monitoring as claimed in any one of claims 1 to 9, characterized in that: include: Collect environmental monitoring data from each monitoring point, compare the environmental monitoring data with the normal monitoring range, and determine abnormal monitoring samples based on the comparison results; Calculating a correlation index between the abnormal monitoring sample and a historical abnormal monitoring sample, and determining an abnormal deviation of the abnormal monitoring sample according to the correlation index; Calculate the abnormal impact factor of the monitoring point corresponding to the environmental monitoring data in the abnormal monitoring sample, and determine whether to adjust the abnormal deviation according to the abnormal impact factor; When it is determined that the abnormal deviation is to be adjusted, the abnormal monitoring sample is enhanced and an enhanced monitoring sample is obtained; the enhanced monitoring sample is compared with a historical normal monitoring sample, and an adjustment coefficient of the abnormal deviation is determined according to the comparison result, and an adjusted abnormal deviation is obtained; Environmental monitoring and early warning are performed based on the adjusted abnormal deviation.

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