A water quality detection method based on big data comparison

Abstract

The present invention discloses a water quality detection method based on big data comparison, which relates to the technical field of water quality detection. It solves the problem that the corresponding abnormal points cannot be found quickly, and the abnormal points are re-analyzed and processed to reflect different abnormal degrees, and then the abnormal area is judged, and the misjudgment of the abnormal area due to a certain number of numerical fluctuations is not caused, and the accuracy of the abnormal area judgment is improved. The parameter data of abnormal points appearing in different areas are analyzed, wherein the analyzed parameter data include the number of times the abnormal points appear and the duration of the corresponding abnormal points appearing, and the area with abnormal data is marked as an abnormal area, and control processing is carried out in time. Subsequently, different levels of processing are carried out for different abnormal areas, wherein the abnormal area can be judged more accurately, and data misjudgment due to data fluctuations is avoided. At the same time, according to different control signals, the operator is warned.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality detection, and specifically is a water quality detection method based on big data comparison. Background Art

[0002] Water is the source of life. Human beings cannot live without water in their daily life and production activities. The quality of drinking water is closely related to human health. With the development of social economy, scientific progress and the improvement of people's living standards, people's requirements for the quality of drinking water are constantly increasing, and the drinking water quality standards are also constantly developing and improving accordingly.

[0003] The invention with patent publication number CN112320963B discloses a fish-grass balance water quality environmental protection treatment method based on big data, including the following steps: using multiple water quality detection sensors to detect various water quality index data of the water area, and uploading them to the cloud for storage; the cloud compares the received water quality index data with the preset water quality parameters. If the water quality parameters exceed the standard, the exceeded parameters are sent to the central processing unit to match with the corresponding parameter simulation adjustment scheme, and then the corresponding parameter simulation adjustment scheme is triggered to adjust the water quality. If the water quality still exceeds the standard, the image acquisition module is triggered to collect images of the water area and upload them to the cloud. The present invention improves the efficiency of interaction and judgment between the simulated water quality data and the actual processed water quality data, improves the efficiency of data judgment in various complex water quality treatment processes, and makes the parameter simulation adjustment scheme more and more fast and accurate to the actual water quality treatment situation.

[0004] In the process of water quality testing in different areas, different points in different areas are generally tested based on the numerical comparison and analysis of different areas, and corresponding maintenance personnel are dispatched to carry out management and maintenance of different areas. However, this type of management and maintenance cannot quickly find the corresponding abnormal points, and re-analyze the abnormal points to reflect the different degrees of abnormality, and then judge the abnormal areas. It will not cause misjudgment of abnormal areas due to certain numerical fluctuations, thereby improving the accuracy of abnormal area judgment. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a water quality detection method based on big data comparison, which is used to solve the problem that the corresponding abnormal points cannot be found quickly, and the abnormal points are re-analyzed to reflect different degrees of abnormality, and then the abnormal area is judged, and the misjudgment of the abnormal area due to a certain number of numerical fluctuations is not caused, thereby improving the accuracy of the abnormal area judgment.

[0006] To achieve the above object, according to an embodiment of the first aspect of the present invention, a water quality detection method based on big data comparison is proposed, comprising the following steps:

[0007] S1. Obtain water quality detection parameters of multiple detection points in the same partition from the big data cloud, and discretize several groups of water quality detection parameters according to the water quality detection parameters of different detection points, determine the detection points with a large degree of discreteness, and mark the determined detection points as abnormal points;

[0008] S2. Re-acquire the past data of the abnormal point, analyze the water quality detection parameters of the abnormal point based on the acquired past data, check whether the water quality detection parameters of the abnormal point are on an upward or downward trend, and classify different abnormal points as attention points or warning points according to different trends;

[0009] S3. Analyze the parameter data of abnormal points in different areas, where the analyzed parameter data includes the number of abnormal points and the duration of the corresponding abnormal points, limit the monitoring cycle, mark the area with abnormal data as an abnormal area, and inform the operator in time for timely control and management;

[0010] S4. The determined different point information and different abnormal area information are transmitted to the external terminal, and the external protection personnel perform different levels of control and processing according to the different point information and different abnormal area information.

[0011] Preferably, in step S1, the specific method of determining the abnormal points of the partition is:

[0012] S11. Mark the water quality test parameters of different test points in different zones as JC k-i , where k represents different partitions, i represents different detection points, and the designated partition is determined according to the corresponding mark k, and several groups of water quality detection parameters JC of the designated partition are k-i Perform mean processing to obtain the corresponding mean parameter X1 to be compared;

[0013] S12, when JC k-i When <X1, the corresponding detection point is marked as a normal point, otherwise, the corresponding detection point is marked as an abnormal point.

[0014] Preferably, in step S2, the specific method of analyzing the water quality detection parameters of the abnormal point is:

[0015] S21. Obtain different abnormal points and extract the water quality detection parameters JC belonging to the abnormal points k-i , determine the limited time, obtain the water quality detection parameters 30 days before the abnormal point at this limited time, and mark the water quality detection parameters of different time periods as JC k-i-t, where t = 1, 2, ..., n, and the specific value of n is 30. The water quality detection parameters at this moment are expressed as JC k-i-30 , where t represents different days, that is, different time periods;

[0016] S22, use Get the trend parameter QS corresponding to the abnormal point k-i , the obtained trend parameter QS k-i Compare with the preset parameter Y2, where the preset parameter Y2 is set by the operator based on experience. k-i When <Y2, the corresponding abnormal point is marked as a concern point, otherwise, the corresponding abnormal point is marked as a warning point;

[0017] S23. The generated point water quality detection parameters of the focus points and warning points and the corresponding point information are transmitted to the external display terminal. External personnel make different response measures according to the parameter information displayed on the display terminal to control and purify the designated water quality area.

[0018] Preferably, in step S3, the specific method of analyzing the parameter data of abnormal points in different regions is:

[0019] S31, extract the water quality detection parameter JC corresponding to the abnormal point k-i-t , where t represents different time periods, and the monitoring period is limited to T, where T is 720h;

[0020] S32, water quality detection parameter JC k-i-t Compare with the preset parameter Y3, where the preset parameter Y3 is drawn up by external operators based on experience. k-i-t When <Y3, no processing is performed, otherwise, a corresponding abnormal signal is generated;

[0021] S33. The duration of the abnormal signal is marked as SC k-i-t , where the duration is in h, and the number of occurrences of abnormal signals is marked as CS k-i ,;

[0022] S34, use Get the corresponding comparison parameter BD k-i , the comparison parameter BD k-i Compare with the preset parameter Y4, when BD k-i When <Y4, no processing is performed, otherwise, the corresponding area is marked as an abnormal area, and the abnormal area mark is transmitted to the external display terminal.

[0023] Preferably, in step S4, the specific manner of performing different levels of control on different areas is:

[0024] S41, pre-check whether the area is an abnormal area, if it is an abnormal area, directly execute step S42, if it is not an abnormal area, directly execute step S43;

[0025] S42, checking the ratio parameter between the focus point and the warning point in the abnormal area, marking the ratio parameter as BL1, comparing the ratio parameter BL1 with the preset parameter Y5, when BL1 < Y5, generating a Class A control signal, otherwise, generating a Class B control signal;

[0026] S43, obtaining a ratio parameter between the focus point and the warning point in the normal area, and marking the corresponding ratio parameter as BL2, and comparing the ratio parameter BL2 with the preset parameter Y6, wherein the preset parameters Y5 and Y6 are both formulated by external personnel based on experience, and when BL<Y6, a Class C control signal is generated, otherwise, a Class D control signal is generated;

[0027] S44. The generated different control signals are transmitted to an external terminal, and external personnel perform different levels of control processing on different areas according to the signals displayed by the display terminal.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: a plurality of groups of water quality detection parameters are processed discretely from the big data cloud, detection points with a large degree of discreteness are determined, and the determined detection points are marked as abnormal points, and the past data of the abnormal points are acquired again, and the water quality detection parameters of the abnormal points are analyzed according to the acquired past data to check whether the water quality detection parameters of the abnormal points are in an upward trend or a downward trend, and different abnormal points are divided into attention points or warning points according to different trends. By adopting this point analysis method, different abnormal points can be found quickly, and the information of different abnormal points can be transmitted to the external operator terminal, so as to process such information in time;

[0029] Analyze the parameter data of abnormal points in different areas, including the number of abnormal points and the duration of the corresponding abnormal points. Mark the areas with abnormal data as abnormal areas and carry out management and control in time. Subsequently, different levels of processing are carried out for different abnormal areas, which can make the judgment of abnormal areas more accurate and avoid data misjudgment due to data fluctuations. At the same time, operators are warned according to different management and control signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 , the present application provides a water quality detection method based on big data comparison, comprising the following steps:

[0033] S1. Obtain water quality detection parameters of multiple detection points in the same partition from the big data cloud, and discretize several groups of water quality detection parameters according to the water quality detection parameters of different detection points, determine the detection points with a large degree of discreteness, and mark the determined detection points as abnormal points (specifically, the abnormal points are determined by using a discrete processing method, because multiple groups of abnormal points are in the same partition, and the water purification capacity and discharge parameters of the same partition are in a dynamic equilibrium state, wherein the water quality detection parameters are detected and obtained by sensors set in the designated area), wherein the specific method for determining the abnormal points of this partition is:

[0034] S11. Mark the water quality test parameters of different test points in different zones as JC k-i , where k represents different partitions, i represents different detection points, and the designated partition is determined according to the corresponding mark k, and several groups of water quality detection parameters JC of the designated partition are k-i Perform mean processing to obtain the corresponding mean parameter X1 to be compared;

[0035] S12, when JC k-i When <X1, the corresponding detection point is marked as a normal point, otherwise, the corresponding detection point is marked as an abnormal point;

[0036] S2. The past data of the abnormal point is obtained again. Based on the obtained past data, the water quality detection parameters of the abnormal point are analyzed to check whether the water quality detection parameters of the abnormal point are on an upward or downward trend. Different abnormal points are divided into focus points or warning points according to different trends. The specific method of analysis is as follows:

[0037] S21. Obtain different abnormal points and extract the water quality detection parameters JC belonging to the abnormal points k-i , determine the limited time, obtain the water quality detection parameters 30 days before the abnormal point at this limited time, among which there is only one set of water quality detection parameters for each day, and the water quality detection parameters of the day are obtained by averaging multiple sets of different water quality detection parameters, and the water quality detection parameters of different time periods are marked as JC k-i-t, where t = 1, 2, ..., n, and the specific value of n is 30. The water quality detection parameters at this moment are expressed as JC k-i-30 , where t represents different days, that is, different time periods;

[0038] S22, use Get the trend parameter QS corresponding to the abnormal point k-i , the obtained trend parameter QS k-i Compare with the preset parameter Y2, where the preset parameter Y2 is set by the operator based on experience. k-i When <Y2, the corresponding abnormal point is marked as a concern point, otherwise, the corresponding abnormal point is marked as a warning point;

[0039] S23, transmitting the generated point water quality detection parameters of the focus point and the warning point and the corresponding point information to the external display terminal, and the external personnel make different response measures according to the parameter information displayed by the display terminal to control and purify the designated water quality area;

[0040] S3. Analyze the parameter data of abnormal points in different areas, where the analyzed parameter data includes the number of abnormal points and the duration of the corresponding abnormal points. Define the monitoring cycle, mark the area with abnormal data as an abnormal area, and inform the operator in time to carry out control and processing in time. The specific method of analysis is:

[0041] S31, extract the water quality detection parameter JC corresponding to the abnormal point k-i-t , where t represents different time periods, and the monitoring period is limited to T, where T is 720h;

[0042] S32, water quality detection parameter JC k-i-t Compare with the preset parameter Y3, where the preset parameter Y3 is drawn up by external operators based on experience. k-i-t When <Y3, no processing is performed, otherwise, a corresponding abnormal signal is generated;

[0043] S33. The duration of the abnormal signal is marked as SC k-i-t The duration is obtained in the following way: when an abnormal signal appears at an abnormal point, the corresponding sensor is used to monitor the data of the abnormal point in real time until the parameter of the abnormal point is lower than the preset parameter Y3, and then the corresponding duration is obtained. This duration is the duration, where the time unit of the duration is h, and the number of times the abnormal signal exists is marked as CS k-i ,;

[0044] S34, use Get the corresponding comparison parameter BD k-i, the comparison parameter BD k-i Compare with the preset parameter Y4, when BD k-i When the value is less than Y4, no processing is performed. Otherwise, the corresponding area is marked as an abnormal area, and the abnormal area mark is transmitted to the external display terminal for external personnel to check and control in time;

[0045] S4. The determined different point information and different abnormal area information are transmitted to the external terminal. The external protection personnel perform different levels of control processing according to the different point information and different abnormal area information. The specific methods of the control processing are as follows:

[0046] S41, pre-check whether the area is an abnormal area, if it is an abnormal area, directly execute step S42, if it is not an abnormal area, directly execute step S43;

[0047] S42, check the ratio parameter between the focus points and the warning points in the abnormal area, mark the ratio parameter as BL1, compare the ratio parameter BL1 with the preset parameter Y5 (the comparison method is the ratio of the number of focus points to the number of warning points), when BL1 < Y5, generate a Class A control signal, otherwise, generate a Class B control signal;

[0048] S43, obtaining a ratio parameter between the focus point and the warning point in the normal area, and marking the corresponding ratio parameter as BL2, and comparing the ratio parameter BL2 with the preset parameter Y6, wherein the preset parameters Y5 and Y6 are both formulated by external personnel based on experience, and when BL<Y6, a Class C control signal is generated, otherwise, a Class D control signal is generated;

[0049] S44. The generated different control signals are transmitted to an external terminal, and external personnel perform different levels of control processing on different areas according to the signals displayed by the display terminal.

[0050] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0051] The working principle of the present invention is as follows: water quality detection parameters belonging to multiple detection points in the same partition are obtained in advance from the big data cloud, and several groups of water quality detection parameters are discretized according to the water quality detection parameters of different detection points, and the detection points with a large degree of discreteness are determined, and the determined detection points are marked as abnormal points, and the past data of the abnormal points are obtained again. According to the acquired past data, the water quality detection parameters of the abnormal points are analyzed to check whether the water quality detection parameters of the abnormal points are in an upward trend or a downward trend. Different abnormal points are divided into attention points or warning points according to different trends. By adopting this point analysis method, different abnormal points can be quickly found, and the information of different abnormal points is transmitted to the external operator terminal, so as to process such information in time;

[0052] Analyze the parameter data of abnormal points in different areas, including the number of abnormal points and the duration of the corresponding abnormal points. Limit the monitoring period, mark the areas with abnormal data as abnormal areas, and inform the operators in time to carry out management and control. Subsequently, different levels of processing are carried out for different abnormal areas, which can make the judgment of abnormal areas more accurate and avoid data misjudgment due to data fluctuations. At the same time, according to different management and control signals, the operators are warned.

[0053] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A water quality detection method based on big data comparison, It is characterized in that The following steps are involved: S1. Obtain water quality detection parameters of multiple detection points in the same partition from the big data cloud, and discretize several groups of water quality detection parameters according to the water quality detection parameters of different detection points, determine the detection points with a large degree of discreteness, and mark the determined detection points as abnormal points; S2. Re-acquire the past data of the abnormal point, analyze the water quality detection parameters of the abnormal point based on the acquired past data, check whether the water quality detection parameters of the abnormal point are on an upward or downward trend, and classify different abnormal points as attention points or warning points according to different trends; S3. Analyze the parameter data of abnormal points in different areas, where the analyzed parameter data includes the number of abnormal points and the duration of the corresponding abnormal points. Define the monitoring cycle, mark the area with abnormal data as an abnormal area, and inform the operator in time to carry out control and management in time. The specific method is as follows: S31, extract the water quality detection parameter JC corresponding to the abnormal point k-i-t , where t represents different time periods, and the monitoring period is limited to T, where T is 720h; S32, water quality detection parameter JC k-i-t Compare with the preset parameter Y3, where the preset parameter Y3 is drawn up by external operators based on experience. k-i-t When <Y3, no processing is performed, otherwise, a corresponding abnormal signal is generated; S33. The duration of the abnormal signal is marked as SC k-i-t , where the duration is in h, and the number of occurrences of abnormal signals is marked as CS k-i ; S34, use Get the corresponding comparison parameter BD k-i , the comparison parameter BD k-i Compare with the preset parameter Y4, when BD k-i When <Y4, no processing is performed, otherwise, the corresponding area is marked as an abnormal area, and the abnormal area mark is transmitted to the external display terminal; S4. The determined different point information and different abnormal area information are transmitted to the external terminal, and the external protection personnel perform different levels of control and processing according to the different point information and different abnormal area information.

2. A water quality detection method based on big data comparison according to claim 1, It is characterized in that In step S1, the specific method for determining the abnormal points of the partition is: S11. Mark the water quality test parameters of different test points in different zones as JC k-i , where k represents different partitions, i represents different detection points, and the designated partition is determined according to the corresponding mark k, and several groups of water quality detection parameters JC of the designated partition are k-i Perform mean processing to obtain the corresponding mean parameter X1 to be compared; S12, when JC k-i When <X1, the corresponding detection point is marked as a normal point, otherwise, the corresponding detection point is marked as an abnormal point.

3. A water quality detection method based on big data comparison according to claim 2, It is characterized in that In step S2, the specific method of analyzing the water quality detection parameters of the abnormal point is: S21. Obtain different abnormal points and extract the water quality detection parameters JC belonging to the abnormal points k-i , determine the limited time, obtain the water quality detection parameters 30 days before the abnormal point at this limited time, and mark the water quality detection parameters of different time periods as JC k-i-t , where t=1, 2, ..., n, and the specific value of n is 30. The water quality detection parameters at this moment are expressed as JC k-i-30 , where t represents different days, that is, different time periods; S22, use Get the trend parameter QS corresponding to the abnormal point k-i , the trend parameter QS obtained k-i Compare with the preset parameter Y2, where the preset parameter Y2 is set by the operator based on experience. k-i When <Y2, the corresponding abnormal point is marked as a concern point, otherwise, the corresponding abnormal point is marked as a warning point; S23. The generated point water quality detection parameters of the focus points and warning points and the corresponding point information are transmitted to the external display terminal. The external personnel make different response measures according to the parameter information displayed by the display terminal to control and purify the designated water quality area.

4. A water quality detection method based on big data comparison according to claim 1, It is characterized in that In step S4, the specific method of performing different levels of control on different areas is as follows: S41, pre-check whether the area is an abnormal area, if it is an abnormal area, directly execute step S42, if it is not an abnormal area, directly execute step S43; S42, checking the ratio parameter between the focus point and the warning point in the abnormal area, marking the ratio parameter as BL1, comparing the ratio parameter BL1 with the preset parameter Y5, when BL1 < Y5, generating a Class A control signal, otherwise, generating a Class B control signal; S43, obtaining a ratio parameter between the focus point and the warning point in the normal area, and marking the corresponding ratio parameter as BL2, and comparing the ratio parameter BL2 with the preset parameter Y6, wherein the preset parameters Y5 and Y6 are both formulated by external personnel based on experience, and when BL2 < Y6, a Class C control signal is generated, otherwise, a Class D control signal is generated; S44. The generated different control signals are transmitted to an external terminal, and external personnel perform different levels of control processing on different areas according to the signals displayed by the display terminal.

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