A smart water data evaluation system and method based on industrial Internet

Abstract

The present invention discloses a smart water affairs data evaluation system and method based on industrial Internet, which belongs to the field of data processing technology. The system and method are used to solve the technical problems of poor local water affairs monitoring data processing and analysis effects and poor abnormal data mining and analysis effects in existing solutions. Data processing and analysis are performed on all target detection data curves in the water affairs target detection data set obtained through monitoring, and data processing and calculation are performed on the target detection points obtained through analysis to obtain the corresponding active detection status degree. According to the active detection status degree, data evaluation is performed on the abnormal impact status corresponding to the target detection point at the moment, and a digital risk prompt of the risk level of the abnormal impact status at the stage is performed. Data calculation and analysis of the continuous impact of the corresponding abnormal fluctuation are performed on the risk level of the stage abnormal impact status obtained through the detection and analysis of the target detection point, and targeted digital visualization risk prompts are implemented for the continuous impact of the abnormal fluctuation of the target detection point according to the analysis results.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a smart water data evaluation system and method based on industrial Internet. Background Art

[0002] Smart water management refers to the use of advanced information technology to conduct comprehensive testing, real-time response and intelligent management of water systems, in order to enhance the efficiency of water resource utilization, improve water supply quality, optimize the operation of water supply and drainage systems, and other goals; water management data usually refers to data related to water resource management, water supply, drainage and water quality testing.

[0003] The existing water affairs data assessment scheme has certain defects in implementation. It cannot implement targeted data analysis of local abnormal impact status and stage abnormal impact status of water affairs detection data in different locations, and cannot implement targeted digital visualization risk tracing processing for the continuous impact of abnormal fluctuations, resulting in poor processing and analysis of local water affairs monitoring data and poor abnormal data mining and analysis. Summary of the invention

[0004] The purpose of the present invention is to provide a smart water data evaluation system and method based on the industrial Internet, which is used to solve the technical problems of poor processing and analysis of local water monitoring data and poor abnormal data mining and analysis in existing solutions.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A smart water data evaluation system based on industrial Internet, including:

[0007] The detection data processing and evaluation module is used to perform data processing and analysis on all target detection data curves in the water affairs target detection data set obtained through monitoring, and to perform data processing and calculation on the target detection points obtained through analysis to obtain the corresponding active detection status, and to perform data evaluation on the abnormal impact status corresponding to the target detection point at the moment according to the active detection status, and to provide a digital risk prompt of the risk level of the abnormal impact status at the stage;

[0008] Among them, according to the abnormal continuous label obtained by analysis, all the subsequent detection points with water resource transmission relationship between the target detection point and it are counted and marked as passive impact detection points, the total number of passive impact detection points N is counted and the active detection state Zj corresponding to the target detection point is calculated by the formula Zj=N×S / t; where S is the area enclosed by the target detection data curve greater than the warning value within the evaluation period and the warning value, and t is the duration corresponding to the evaluation period; the active detection state is input into the abnormal impact identification model for data analysis to obtain the corresponding stage abnormal impact state risk level and prompt;

[0009] The detection data traceability assessment module is used to calculate and analyze the data corresponding to the continuous impact of abnormal fluctuations on the risk level of the stage abnormal impact status obtained through the detection and analysis of the target detection point, and implement targeted digital visualization risk prompts for the continuous impact of abnormal fluctuations on the target detection point based on the analysis results;

[0010] Among them, the continuous impact degree Cy corresponding to the target detection point in the continuous impact verification period is calculated by the formula Cy=[S′-S0]; where S′ is the evaluation area enclosed by the target detection data curve and the warning value in the continuous impact verification period, and S0 is the standard area; the sign of the continuous impact degree is obtained and the continuous impact type analysis is performed to obtain a positive impact type label or a negative impact type label; the numerical value of the continuous impact degree is input into the pre-built continuous impact assessment model to evaluate the degree of continuous impact and output the continuous impact mark and the corresponding continuous impact risk level.

[0011] Preferably, it also includes a water conservancy data detection setting module, which is used to obtain corresponding main detection points and secondary detection points according to water resource transmission routes at different locations, and implement target detection item detection and data statistics on the main detection points and secondary detection points to obtain a water conservancy target detection data set composed of a number of marked main detection points and secondary detection points and associated target detection data curves.

[0012] Preferably, all transfer sites on the way from the origin to the user end of the water resources are obtained, and all the transfer sites are marked as the first main detection point, the second main detection point, ..., the Nth main detection point in the order of the water resource transmission route, where N is a positive integer;

[0013] D secondary detection points are set between adjacent main detection points, and all D secondary detection points are marked as the first secondary detection point, the second secondary detection point, ..., the Dth secondary detection point according to the order of the water resource transmission routes between the adjacent main detection points;

[0014] All the main detection points are connected and combined, and several secondary detection points between all adjacent main detection points are filled and bound to obtain detection point combination connection lines.

[0015] Preferably, the water resource transmission distance between adjacent main detection points is obtained and the corresponding interval continuity D is calculated by the formula D=[d / d0]; wherein d0 is the standard transmission distance; and D secondary detection points are set between the calculated adjacent main detection points according to the interval continuity D.

[0016] Preferably, the target detection item is detected at the main detection point and the secondary detection point by a target detection sensor, and the detection data obtained by the detection is displayed and connected through a pre-constructed detection coordinate system to obtain a target detection data curve corresponding to the target detection item;

[0017] According to the marks corresponding to the main detection points and the secondary detection points, the associated target detection data curves are sorted and combined to obtain the water affairs target detection data set.

[0018] Preferably, the target detection items include an abnormal odor detection item, a dissolved oxygen detection item, a phosphate detection item, and a turbidity detection item.

[0019] Preferably, the real-time detection data in all target detection data curves and the preset warning values ​​in the detection coordinate system are judged and classified;

[0020] If the real-time detection data is not greater than the warning value, a normal detection label is generated;

[0021] Otherwise, a detection anomaly label is generated, and the detection point corresponding to the real-time detection data is marked as the target detection point according to the detection anomaly label, and the time point when the detection anomaly label is generated is marked as the target start time point;

[0022] And based on the target start time point and the preset evaluation period, a stability retrospective evaluation is conducted on the abnormal detection data appearing at the target detection point.

[0023] Preferably, if the intersection point of the real-time detection data of the target detection point within the evaluation period and the warning value is one, an abnormal recovery label is generated; otherwise, an abnormal persistence label is generated.

[0024] Preferably, if the sign is positive, a positive impact type label is generated; if the sign is negative, a negative impact type label is generated.

[0025] A smart water data evaluation method based on industrial Internet, comprising:

[0026] According to the water resource transmission routes at different locations, the corresponding main detection points and secondary detection points are obtained, and the detection of target detection items and data statistics are carried out on the main detection points and secondary detection points, so as to obtain a water affairs target detection data set composed of a number of marked main detection points and secondary detection points and the associated target detection data curves.

[0027] Data processing and analysis are performed on all target detection data curves in the water affairs target detection data set, and data processing and calculation are performed on the target detection points obtained through analysis to obtain the corresponding active detection status. According to the active detection status, data evaluation is performed on the abnormal impact status corresponding to the target detection point at the moment, and a digital risk prompt of the risk level of the abnormal impact status at the stage is provided;

[0028] For the stage abnormal impact status risk level obtained from the detection and analysis of the target detection point, the corresponding data calculation and analysis of the continuous impact of abnormal fluctuations are implemented, and based on the analysis results, targeted digital visualization risk warnings are implemented for the continuous impact of abnormal fluctuations on the target detection point.

[0029] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0030] The present invention can provide reliable location data support for subsequent detection and analysis of water resources at different locations by implementing modular processing and marking on the transmission route of water resources. By implementing periodic verification and analysis on the abnormal type corresponding to the abnormal data of the target detection point through a preset evaluation cycle, the abnormal persistence of the target detection point can be obtained. By performing data analysis and classification on the calculated active detection status, the corresponding stage abnormal impact state risk level can be obtained and targeted risk prompts can be given. The abnormal data detected by the target detection item corresponding to the target detection point can be analyzed in a targeted manner to obtain the corresponding stage abnormal impact state risk level. The corresponding continuous impact verification period is obtained according to the stage abnormal impact state risk level obtained through the detection and analysis of the target detection point. The continuous impact degree corresponding to the target detection point is calculated and obtained using the continuous impact verification period condition. By obtaining the sign and value of the continuous impact degree and performing continuous impact type analysis and continuous impact degree analysis respectively, the continuous impact risk level corresponding to the target detection point can be obtained and targeted digital risk display prompts can be given, thereby improving the reliability and diversity of digital risk prompts corresponding to the detection abnormality evaluation link and the traceability evaluation link. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] Figure 1 This is a module block diagram of a smart water data evaluation system based on industrial Internet in the present invention.

[0033] Figure 2 This is a flowchart of the acquisition of the detection point combination connection line in the present invention.

[0034] Figure 3 The figure is a flowchart of the operation of the detection data tracing and evaluation module in the present invention.

[0035] Figure 4 This is a flowchart of a smart water data evaluation method based on the industrial Internet in the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary operation and maintenance personnel in this field without creative work are within the scope of protection of the present invention.

[0037] Example 1: Figure 1 As shown, the present invention is a smart water affairs data evaluation system based on the industrial Internet, including a water affairs data detection setting module, a detection data processing evaluation module, a detection data traceability evaluation module and a supervision platform;

[0038] The water affairs data detection setting module is used to obtain the corresponding main detection points and secondary detection points according to the water resources transmission routes at different locations, and implement the detection of target detection items and data statistics on the main detection points and secondary detection points, obtain the water affairs target detection data set and upload it to the supervision platform in real time; including:

[0039] like Figure 2 As shown, all transfer stations on the way from the origin to the user end of the water resource are obtained, the origin can be a water plant, the water resource can be tap water, and all transfer stations are marked as the first main detection point, the second main detection point, ..., the Nth main detection point in the order of the water resource transmission route, where N is a positive integer;

[0040] And, obtain the water resource transmission distance d between adjacent main detection points, and calculate the corresponding interval continuity D through the formula D=[d / d0]; in the formula, d0 is the standard transmission distance, and the specific value of the standard transmission distance can be determined according to the existing detection point distance design parameters, and can also be customized based on the actual application scenario; according to the interval continuity D, set D secondary detection points between the calculated adjacent main detection points, and mark all D secondary detection points as the first secondary detection point, the second secondary detection point, ..., the Dth secondary detection point according to the order of the water resource transmission route between the adjacent main detection points;

[0041] All main detection points are connected and combined, and several secondary detection points between all adjacent main detection points are filled and bound to obtain detection point combination connection lines;

[0042] In the embodiment of the present invention, by implementing modular processing and marking on the transmission route of water resources, reliable location data support can be provided for subsequent detection and analysis of water resources at different locations. Compared with the design scheme of only setting the main detection point in the prior art scheme, the embodiment of the present invention realizes more efficient and accurate water data detection and abnormal location tracing by designing the coordinated detection of the main detection point and the auxiliary detection point.

[0043] When the target detection items are detected at the main detection points and the secondary detection points, the target detection items are detected at the main detection points and the secondary detection points through the target detection sensors, and the detection data obtained by the detection are displayed and connected through the pre-built detection coordinate system to obtain the target detection data curve corresponding to the target detection item; the horizontal axis of the detection coordinate system is the real-time Beijing time, and the vertical axis is the detection difference value with an arithmetic difference value change, and the specific value of the detection difference value is determined according to the historical detection data of the actual target detection item;

[0044] The target detection sensor is determined according to the corresponding target detection items, which include but are not limited to abnormal odor detection items, dissolved oxygen detection items, phosphate detection items, and turbidity detection items; the corresponding target detection sensor may be a hydrogen sulfide sensor, a chloride sensor or a volatile organic compound (VOCs) sensor, a dissolved oxygen sensor, a phosphate sensor, and a turbidity sensor;

[0045] According to the marks corresponding to the main detection points and the secondary detection points, the associated target detection data curves are sorted and combined to obtain a water affairs target detection data set;

[0046] In an embodiment of the present invention, by implementing detection of target detection items on the processed main detection points and secondary detection points and obtaining a water target detection data set through data statistics, real-time detection data of different detection points can be obtained, and reliable local detection data support can be provided for subsequent abnormal detection status analysis of different detection points.

[0047] The detection data processing and evaluation module is used to perform data processing and analysis on all target detection data curves in the water affairs target detection data set, and to perform data processing and calculation on the target detection points obtained by the analysis to obtain the corresponding active detection status, and to perform data evaluation on the abnormal impact status corresponding to the target detection point at the moment according to the active detection status, and to provide digital risk prompts for the risk level of the abnormal impact status at the stage; including:

[0048] The real-time detection data in all target detection data curves are judged and classified with the preset warning values ​​in the detection coordinate system; the warning values ​​are determined according to the existing standard requirement data of the corresponding target detection items, and the warning values ​​are displayed in the detection coordinate system in the form of horizontal lines;

[0049] If the real-time detection data is not greater than the warning value, a normal detection label is generated;

[0050] Otherwise, a detection anomaly label is generated, and the detection point corresponding to the real-time detection data is marked as the target detection point according to the detection anomaly label, and the time point when the detection anomaly label is generated is marked as the target start time point, and the unit of the start time point is accurate to seconds;

[0051] And according to the target start time point and the preset evaluation period, a stability retrospective evaluation is performed on the abnormal detection data appearing at the target detection point; wherein the cycle duration corresponding to the evaluation period is in seconds, and the specific value may be 30 seconds;

[0052] If the intersection of the real-time detection data and the warning value of the target detection point within the evaluation period is one, an abnormal recovery label is generated, which does not include the intersection of the real-time detection data and the warning value when the detection abnormal label is generated; otherwise, an abnormal continuous label is generated;

[0053] The abnormal recovery tag or abnormal persistence tag constitutes abnormal retrospective evaluation data;

[0054] In the embodiment of the present invention, a periodic verification and analysis is performed on the abnormal type corresponding to the abnormal data of the target detection point through a preset evaluation cycle, which can not only obtain the abnormal persistence of the target detection point, but also provide reliable verification data support for the subsequent traceability analysis and evaluation of the abnormal persistence.

[0055] According to the abnormal persistence label in the abnormal tracing assessment data, the data of the abnormal impact status of the stage corresponding to the target detection point is calculated and analyzed;

[0056] All subsequent detection points with water resource transmission relationship between the target detection point and it are counted and marked as passive impact detection points. The total number of passive impact detection points N is counted and the active detection state Zj corresponding to the target detection point is calculated by the formula Zj=N×S / t; where S is the area enclosed by the target detection data curve greater than the warning value within the evaluation period and the warning value, and t is the duration corresponding to the evaluation period;

[0057] Obtain the design parameters corresponding to the target detection items, and build an abnormal impact recognition model based on the design parameters. The abnormal impact recognition model can be established based on the isolation forest algorithm. The active detection status is input into the abnormal impact recognition model for data analysis to obtain the corresponding stage abnormal impact status risk level and prompt;

[0058] Among them, the expression of the abnormal impact identification model is: ; Wherein, Zj′ is the minimum value of the active detection state threshold, and Zj″ is the maximum value of the active detection state threshold; the minimum value of the active detection state threshold and the maximum value of the active detection state threshold can be determined according to the minimum value and maximum value of the existing standard requirement data of the corresponding target detection item;

[0059] According to the value 1, 2 or 3 output by the abnormal impact identification model, the corresponding stage abnormal low impact state risk level, stage abnormal medium impact state risk level or stage abnormal high impact state risk level are generated respectively;

[0060] In the embodiment of the present invention, by performing data analysis and classification on the calculated active detection status to obtain the corresponding stage abnormal impact state risk level and providing targeted risk prompts, the abnormal data detected by the target detection point corresponding to the target detection item can be analyzed in a targeted manner to obtain the corresponding stage abnormal impact state risk level, thereby improving the diversity of abnormal data processing and analysis found in water affairs data detection and the reliability of data evaluation prompts;

[0061] The detection data traceability assessment module is used to calculate and analyze the data corresponding to the continuous impact of abnormal fluctuations on the risk level of the stage abnormal impact status obtained by the detection and analysis of the target detection point, and implement targeted digital visualization risk prompts for the continuous impact of abnormal fluctuations on the target detection point based on the analysis results; including:

[0062] like Figure 3 As shown, the corresponding continuous impact verification period is obtained according to the stage abnormal impact state risk level obtained by the target detection point detection analysis, wherein the continuous impact verification period corresponding to the stage abnormal low impact state risk level, the stage abnormal medium impact state risk level or the stage abnormal high impact state risk level are all different, and the corresponding continuous impact verification period The value decreases in sequence, while realizing the abnormal traceability verification of abnormal continuity, it can also reduce the negative impact of different impact state risks during analysis, and obtain the evaluation area S′ surrounded by the target detection data curve and the warning value in the continuous impact verification period, and obtain the standard area S0 surrounded by the target detection data curve and the warning value that are adjacent to the continuous impact verification period and have the same duration in the evaluation cycle, and calculate the continuous impact degree Cy corresponding to the target detection point in the continuous impact verification period through the formula Cy=[S′-S0];

[0063] Obtain the sign and value of the sustained impact and conduct sustained impact type analysis and sustained impact degree analysis respectively;

[0064] If the sign is positive, a positive impact type label is generated; if the sign is zero, a stable impact type label is generated; if the sign is negative, a negative impact type label is generated;

[0065] It should be noted that when an abnormality is detected at a target detection point, it is necessary to further trace back and analyze the continuous impact of the abnormality so that the operation and maintenance personnel can take more timely and reliable solutions to handle and solve it; the continuous impact of the abnormality at the target detection point includes serious increase risk, serious stable risk and serious reduction risk;

[0066] According to the positive impact type label or the negative impact type label, the value of the continuous impact degree is input into the pre-built continuous impact assessment model to evaluate the continuous impact degree and output the continuous impact identification and the corresponding continuous impact risk level; wherein, the historical anomaly detection data of the target detection item is obtained and the continuous impact assessment model is established based on the isolation forest algorithm to evaluate and classify the continuous impact degree;

[0067] Among them, the expression of the continuous impact assessment model is: ; In the formula, k=1,2; Cy1 is the continuous influence corresponding to the positive influence type label, Cy2 is the continuous influence corresponding to the negative influence type label, Cy′1 is the continuous influence threshold corresponding to the positive influence type label, Cy′2 is the continuous influence threshold corresponding to the negative influence type label, and the continuous influence threshold corresponding to different labels can be determined according to the median of the continuous influence in the historical anomaly detection data of the corresponding target detection item;

[0068] The continuous impact of the output is marked as 21, 11 or 22, 12, which respectively represent the high impact level of risk increase, the low impact level of risk increase, or the high impact level of risk reduction, the low impact level of risk reduction;

[0069] According to the input continuous impact identifier, the corresponding high impact level of risk increase, low impact level of risk increase, high impact level of risk reduction or low impact level of risk reduction is generated and prompted.

[0070] In an embodiment of the present invention, the corresponding continuous impact verification period is obtained according to the stage abnormal impact state risk level obtained through the detection and analysis of the target detection point, and the continuous impact verification period condition is used to calculate the continuous impact degree corresponding to the target detection point. By obtaining the sign and value of the continuous impact degree and performing continuous impact type analysis and continuous impact degree analysis respectively, the continuous impact risk level corresponding to the target detection point can be obtained and targeted digital risk display prompts can be performed, thereby improving the reliability and diversity of the corresponding risk digital prompts in the detection abnormality assessment link and the traceability assessment link.

[0071] Example 2: Figure 4 As shown, a smart water data evaluation method based on the industrial Internet includes:

[0072] According to the water resource transmission routes at different locations, the corresponding main detection points and secondary detection points are obtained, and the detection of target detection items and data statistics are carried out on the main detection points and secondary detection points, so as to obtain a water affairs target detection data set composed of a number of marked main detection points and secondary detection points and the associated target detection data curves.

[0073] Data processing and analysis are performed on all target detection data curves in the water affairs target detection data set, and data processing and calculation are performed on the target detection points obtained through analysis to obtain the corresponding active detection status. According to the active detection status, data evaluation is performed on the abnormal impact status corresponding to the target detection point at the moment, and a digital risk prompt of the risk level of the abnormal impact status at the stage is provided;

[0074] For the stage abnormal impact status risk level obtained from the detection and analysis of the target detection point, the corresponding data calculation and analysis of the continuous impact of abnormal fluctuations are implemented, and based on the analysis results, targeted digital visualization risk warnings are implemented for the continuous impact of abnormal fluctuations on the target detection point.

[0075] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0076] In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0077] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0079] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic features of the present invention.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary operation and maintenance personnel in the field should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A smart water data evaluation system based on industrial Internet, characterized in that: include: The water affairs data detection setting module is used to obtain the corresponding main detection points and secondary detection points according to the water resources transmission routes at different locations, and implement the detection of target detection items and data statistics on the main detection points and secondary detection points to obtain a water affairs target detection data set composed of a number of marked main detection points and secondary detection points and the associated target detection data curves. Among them, all transfer sites on the way from the origin to the user end of the water resources are obtained, and all the transfer sites are marked as the first main detection point, the second main detection point, ..., the Nth main detection point in the order of the water resources transmission route, where N is a positive integer; Obtain the water resource transmission distance between adjacent main detection points and calculate the corresponding interval continuity D through the formula D=[d / d0]; where d0 is the standard transmission distance; set D secondary detection points between the calculated adjacent main detection points according to the interval continuity D, and mark all D secondary detection points as the first secondary detection point, the second secondary detection point, ..., the Dth secondary detection point according to the order of the water resource transmission route between the adjacent main detection points; All main detection points are connected and combined, and several secondary detection points between all adjacent main detection points are filled and bound to obtain detection point combination connection lines; The detection data processing and evaluation module is used to perform data processing and analysis on all target detection data curves in the water affairs target detection data set obtained through monitoring, and to perform data processing and calculation on the target detection points obtained through analysis to obtain the corresponding active detection status, and to perform data evaluation on the abnormal impact status corresponding to the target detection point at the moment according to the active detection status, and to provide a digital risk prompt of the risk level of the abnormal impact status at the stage; Among them, according to the abnormal continuous label obtained by analysis, all the detection points that have a water resource transmission relationship with the target detection point are counted and marked as passive impact detection points, and the total number of passive impact detection points N is counted and the active detection state Zj corresponding to the target detection point is calculated by the formula Zj=N×S / t; where S is the area enclosed by the target detection data curve greater than the warning value within the evaluation period and the warning value, and t is the duration corresponding to the evaluation period; Obtain the design parameters corresponding to the target detection items, and build an abnormal impact recognition model based on the design parameters. The abnormal impact recognition model can be established based on the isolation forest algorithm. The active detection status is input into the abnormal impact recognition model for data analysis to obtain the corresponding stage abnormal impact status risk level and prompt; Among them, the expression of the abnormal impact identification model is: Wherein, Zj′ is the minimum value of the active detection state degree threshold, and Zj″ is the maximum value of the active detection state degree threshold; the minimum value of the active detection state degree threshold and the maximum value of the active detection state degree threshold can be determined according to the minimum value and maximum value of the existing standard requirement data of the corresponding target detection item; According to the value 1, 2 or 3 output by the abnormal impact identification model, the corresponding stage abnormal low impact state risk level, stage abnormal medium impact state risk level or stage abnormal high impact state risk level are generated respectively; The detection data traceability assessment module is used to calculate and analyze the data corresponding to the continuous impact of abnormal fluctuations on the risk level of the stage abnormal impact status obtained through the detection and analysis of the target detection point, and implement targeted digital visualization risk prompts for the continuous impact of abnormal fluctuations on the target detection point based on the analysis results; The continuous impact degree Cy corresponding to the target detection point in the continuous impact verification period is calculated by the formula Cy=[S′-S0]; where S′ is the evaluation area enclosed by the target detection data curve and the warning value in the continuous impact verification period, and S0 is the standard area; the sign of the continuous impact degree is obtained and the continuous impact type analysis is performed to obtain a positive impact type label or a negative impact type label; According to the positive impact type label or the negative impact type label, the value of the continuous impact degree is input into the pre-built continuous impact assessment model to evaluate the continuous impact degree and output the continuous impact identification and the corresponding continuous impact risk level; wherein, the historical anomaly detection data of the target detection item is obtained and the continuous impact assessment model is established based on the isolation forest algorithm to evaluate and classify the continuous impact degree; Among them, the expression of the continuous impact assessment model is: Wherein, k = 1, 2; Cy1 is the continuous influence degree corresponding to the positive influence type label, Cy2 is the continuous influence degree corresponding to the negative influence type label, Cy′1 is the continuous influence degree threshold corresponding to the positive influence type label, Cy′2 is the continuous influence degree threshold corresponding to the negative influence type label, and the continuous influence degree threshold corresponding to different labels can be determined according to the median value of the continuous influence degree in the historical anomaly detection data of the corresponding target detection item; The continuous impact of the output is marked as 21, 11 or 22, 12, which respectively represent the high impact level of risk increase, the low impact level of risk increase, or the high impact level of risk reduction, the low impact level of risk reduction; According to the input continuous impact identifier, the corresponding high impact level of risk increase, low impact level of risk increase, high impact level of risk reduction or low impact level of risk reduction are generated and prompted.

2. According to the industrial Internet-based smart water data evaluation system of claim 1, it is characterized in that: The target detection items are detected at the main detection points and the secondary detection points by using the target detection sensor, and the detection data obtained by the detection are displayed and connected through the pre-built detection coordinate system to obtain the target detection data curve corresponding to the target detection item; According to the marks corresponding to the main detection points and the secondary detection points, the associated target detection data curves are sorted and combined to obtain the water affairs target detection data set.

3. According to the industrial Internet-based smart water data evaluation system of claim 1, it is characterized in that: Target detection items include abnormal odor detection items, dissolved oxygen detection items, phosphate detection items and turbidity detection items.

4. According to the industrial Internet-based smart water data evaluation system of claim 1, it is characterized in that: Determine and classify the real-time detection data in all target detection data curves and the preset warning values ​​in the detection coordinate system; If the real-time detection data is not greater than the warning value, a normal detection label is generated; Otherwise, a detection anomaly label is generated, and the detection point corresponding to the real-time detection data is marked as the target detection point according to the detection anomaly label, and the time point when the detection anomaly label is generated is marked as the target start time point; And based on the target start time point and the preset evaluation period, a stability retrospective evaluation is conducted on the abnormal detection data appearing at the target detection point.

5. According to the industrial Internet-based smart water data evaluation system of claim 4, it is characterized in that: If the intersection of the real-time detection data and the warning value of the target detection point within the evaluation period is one, an abnormal recovery label is generated; otherwise, an abnormal persistence label is generated.

6. According to the industrial Internet-based smart water data evaluation system of claim 1, it is characterized in that: If the sign is positive, a positive impact type label is generated; if the sign is negative, a negative impact type label is generated.

7. A smart water data evaluation method based on the industrial Internet, using a smart water data evaluation system based on the industrial Internet as described in any one of claims 1 to 6, characterized in that: include: According to the water resource transmission routes at different locations, the corresponding main detection points and secondary detection points are obtained, and the detection of target detection items and data statistics are carried out on the main detection points and secondary detection points, so as to obtain a water affairs target detection data set composed of a number of marked main detection points and secondary detection points and the associated target detection data curves. Data processing and analysis are performed on all target detection data curves in the water affairs target detection data set, and data processing and calculation are performed on the target detection points obtained through analysis to obtain the corresponding active detection status. According to the active detection status, data evaluation is performed on the abnormal impact status corresponding to the target detection point at the moment, and a digital risk prompt of the risk level of the abnormal impact status at the stage is provided; For the stage abnormal impact status risk level obtained from the detection and analysis of the target detection point, the corresponding data calculation and analysis of the continuous impact of abnormal fluctuations are implemented, and based on the analysis results, targeted digital visualization risk warnings are implemented for the continuous impact of abnormal fluctuations on the target detection point.

Images