Environment-friendly pollution source automatic monitoring facility field mobile inspection terminal, system and method

Abstract

The invention discloses an on-site mobile inspection terminal, system and method for an automatic monitoring facility of an environment-friendly pollution source, relates to the technical field of environmental protection, and solves the technical problem that the monitoring efficiency of the environment-friendly pollution source is relatively low due to the fact that monitoring is carried out on a fixed area and pollution source positioning is not considered after a pollution condition occurs in the prior art. A detection anomaly coefficient is generated according to detection data; generating a state label according to the abnormal detection coefficient and generating an abnormal region; calculating an area anomaly coefficient according to the abnormal area; generating an alarm signal according to the regional anomaly coefficient; the detection point corresponding to the highest value of the detection anomaly coefficient is obtained as the initial detection point, the pollution source area is positioned according to the initial detection point and the environmental data, anomaly detection is performed by taking each index influencing the water quality as a key parameter, and the anomaly area is adaptively obtained by each anomaly detection point, so that the efficiency and accuracy of automatic anomaly detection are improved, and the detection efficiency is improved. And the pollution source region is adaptively searched in the abnormal region, so that the working efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the field of environmental protection technology, and specifically relates to an on-site mobile inspection terminal, system and method for automatic monitoring facilities for environmental pollution sources. Background Art

[0002] Environmental pollution source is a broad concept, which refers to the source of pollutants that cause environmental pollution. Pollution sources usually refer to places, equipment, devices or human bodies that emit harmful substances into the environment or have harmful effects on the environment. These harmful substances or energy enter the environmental system with inappropriate concentrations, quantities, speeds, forms and pathways, causing pollution or damage to the environment. The main pollution sources include industrial pollution sources, agricultural pollution sources, transportation pollution sources and domestic pollution sources. Water pollution, as one of the problems that urgently need to be solved in the current pollution situation, seriously affects people's life safety and the state of the ecological environment.

[0003] The prior art (invention patent application with publication number CN114217032A) discloses a water quality environment detection method and system, wherein the water quality environment detection system includes at least one water quality detection module, a measurement and control terminal connected to the above-mentioned water quality detection module, a database and video data storage module connected to the measurement and control terminal, as well as a cloud monitoring platform and a water environment management application platform; the measurement and control terminal collects and transmits data to the database and video data storage module for storage, and then analyzes and evaluates through the water environment management application platform to timely grasp the water quality status, so as to warn and predict major water pollution accidents.

[0004] In the above-mentioned cases, the water bodies in the target areas were continuously monitored 24 hours a day through the cloud monitoring platform and the water environment management application platform, and the water quality conditions were timely grasped after analysis and evaluation through the water environment management application platform; however, the above-mentioned cases lacked consideration of the problem of locating the pollution source after the pollution occurred, and the above-mentioned cases were targeted at fixed areas for detection, and random areas could not be automatically detected, which made the efficiency of environmental pollution source monitoring low; therefore, the automatic inspection system for environmental pollution sources still needs further improvement. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes an on-site mobile inspection terminal, system and method for automatic monitoring facilities for environmental pollution sources, which are used to solve the technical problem that the prior art monitors fixed areas and lacks consideration of the location of pollution sources after pollution occurs, resulting in low efficiency in environmental pollution source monitoring.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides an on-site mobile inspection system for automatic monitoring facilities of environmental pollution sources, including: a data acquisition module, a data analysis module, an early warning module and a database;

[0007] The data acquisition module is used to acquire detection data, environmental data and coordinate data of several detection points through data acquisition equipment;

[0008] The data analysis module: generates a detection anomaly coefficient according to the detection data; generates a detection point status label according to the detection anomaly coefficient of each detection point and generates an abnormal area based on it; calculates the regional anomaly coefficient according to the abnormal area; generates an alarm signal according to the regional anomaly coefficient; obtains the detection point corresponding to the highest detection anomaly coefficient as the starting detection point, and locates the pollution source area according to the starting detection point and environmental data;

[0009] The early warning module: makes prompts according to the alarm signal and contacts the management personnel.

[0010] The present application generates a detection anomaly coefficient based on the detection data and a detection point status label based on the detection anomaly coefficient of each detection point in the above description, and generates an abnormal area based on this, performs anomaly detection with various indicators affecting water quality as key parameters, and adaptively obtains the abnormal area with each abnormal detection point, thereby improving the efficiency and accuracy of automatic anomaly detection, and at the same time locates the pollution source area according to the starting detection point and environmental data, and adaptively searches for the pollution source range within the abnormal area, thereby accelerating the process of finding the pollution source and improving work efficiency.

[0011] Further, generating a detection anomaly coefficient according to the detection data includes:

[0012] Obtain several test items and their corresponding test values ​​at the test point, as well as the corresponding standard safety range;

[0013] The test items are divided into a test item group 1 and a test item group 2 according to the standard safety range; the test item group 1 includes test items for which the standard safety range has only an upper limit value or a lower limit value; the test item group 2 includes test items for which the standard safety range has both an upper limit value and a lower limit value;

[0014] Generate abnormal coefficient 1 YX1 and abnormal coefficient 2 YX2 according to test item group 1 and test item group 2;

[0015] The detection abnormality coefficient JYX of the detection point is calculated by the formula JYX=YX1+YX2.

[0016] The present application groups and divides the detection items according to their corresponding standard safety ranges, calculates them separately based on the characteristics of each group, and finally obtains the detection anomaly coefficient of the detection point, thereby improving the accuracy of the detection anomaly coefficient of the detection point and improving the calculation efficiency.

[0017] Further, generating anomaly coefficient 1 YX1 and anomaly coefficient 2 YX2 according to detection item group 1 and detection item group 2 includes:

[0018] Extract the test values ​​JS corresponding to several test items in test item group 1 i , the upper or lower limit of the corresponding standard safety range DXZ i And the test values ​​JS corresponding to several test items in test item group 2 j , the upper and lower limits of the corresponding standard safety range;

[0019] Generate the standard safety intermediate value BAZ based on the upper and lower limits of the standard safety range j and floating deviation FP j ;

[0020] Judge each JS in turn i and JS j Whether it is within the corresponding standard safety range;

[0021] Yes, set the abnormal value coefficient of the detection value to "0", that is, YZX1 i =0,YZX2 j =0;

[0022] No, by formula Calculate the abnormal value coefficient YZX1 corresponding to each test item in test item group 1 i ;

[0023] By formula Calculate the abnormal value coefficient YZX2 corresponding to each test item in test item group 2 j ; Among them, β1 i and β2 j is the exponential coefficient, β1 i and β2 j ∈(0,1);DX1 i and DX2 j are the unit data of the i-th test item in test item group 1 and the j-th test item in test item group 2 respectively;

[0024] Through the formula YX1 = Σ i γ1 i ×YZX1 i and YX2 = Σ j γ2 j ×YZX2 j Calculate the abnormal coefficient 1 YX1 and the abnormal coefficient 2 YX2; where γ1 i and γ2 j is the weight coefficient, and γ1 i and γ2 j ∈(0,1).

[0025] Further, the standard safety intermediate value BAZ is generated according to the upper limit and lower limit of the standard safety range. j and floating deviation FP j ,include:

[0026] The upper limit value in the standard safety range corresponding to the test item numbered j is marked as BAFD j , the lower limit is marked as BAFX j ;

[0027] BAZ by Formula j =(BAFD j +BAFX j ) / 2 Calculate the standard safety intermediate value BAZ of several test data j ;

[0028] By formula FP j =(BAFD j -BAFX j ) / 2 Calculate the floating deviation FP of several test data j .

[0029] Furthermore, the step of generating a detection point status label according to the detection anomaly coefficient of each detection point and generating an abnormal area therefrom includes:

[0030] Obtain the detection anomaly coefficient and coordinate data of each detection point;

[0031] Determine whether the detection anomaly coefficient is greater than the anomaly threshold; if yes, set the detection point as an anomaly label; if no, set the detection point as a normal label;

[0032] Extract the coordinate data of the detection point as a normal label;

[0033] The Graham scanning method was used to construct a convex shape as the abnormal area.

[0034] Further, the calculating of the regional anomaly coefficient according to the abnormal region includes:

[0035] Get the abnormal area S of the abnormal area and the detection abnormality coefficients JYX of several detection points in the abnormal area b ;

[0036] By formula Calculate the regional anomaly coefficient QYX; where α1 and α2 are weight coefficients, α1 and α2∈(0,1); DS is the unit area.

[0037] Further, the generating of an alarm signal according to the regional anomaly coefficient includes:

[0038] Obtain regional anomaly coefficient, anomaly area and several detection anomaly coefficients;

[0039] Determine whether the regional anomaly coefficient is greater than the regional anomaly threshold;

[0040] Yes, a serious regional pollution warning signal is generated;

[0041] No, determine whether the abnormal area is greater than the area threshold; Yes, generate a serious pollution spread alarm signal; No, do nothing;

[0042] Determine whether there is a detection anomaly coefficient greater than the detection threshold; if yes, generate a pollution alarm signal; if no, do nothing.

[0043] Furthermore, locating the pollution source area according to the starting detection point and environmental data includes the following steps:

[0044] Step 1: Obtain the coordinate data of the starting detection point, the detection anomaly coefficient JYX and the water flow velocity SV in the environmental data;

[0045] Step 2: Calculate the moving distance YJ by the formula YJ=BYJ×(1+ln(SV+1)); BYJ represents the standard moving distance; take the coordinate position of the starting detection point as the center, take the moving distance as the radius, and set a number of surrounding detection points according to the moving direction; when the surrounding detection point is beyond the abnormal area, the intersection with the abnormal area is used as the surrounding detection point;

[0046] Step 3: Calculate the detection anomaly coefficients JYXc of several surrounding detection points; where c represents the number of the surrounding detection points; c=1, 2, ..., Q; Q is the total number of surrounding detection points; determine whether there is a detection anomaly coefficient of the surrounding detection point that is greater than the detection anomaly coefficient of the starting detection point;

[0047] If yes, set the coordinate position corresponding to the maximum value of the detection anomaly coefficients around the detection point as the starting detection point and go to step 1; if no, go to step 4;

[0048] Step 4: Use the starting detection point as the center of the circle and the distance R as the radius to construct the pollution source area.

[0049] This application uses the detection point corresponding to the highest value of the detection anomaly coefficient in the abnormal area as the starting detection point, and adaptively searches for the position coordinates with the highest detection anomaly coefficient in the abnormal area, thereby constructing the pollution source area, providing strong support for users to find pollution sources, speeding up the process of finding pollution sources, and improving work efficiency.

[0050] The second aspect of the present invention provides an on-site mobile inspection method for an automatic monitoring facility for environmental pollution sources, comprising:

[0051] S0: Acquire the detection data, environmental data and coordinate data of several detection points;

[0052] S1: Generate a detection anomaly coefficient based on the detection data; generate a detection point status label based on the detection anomaly coefficient of each detection point and generate an abnormal area based on it;

[0053] S2: Calculate the regional anomaly coefficient according to the abnormal area; generate an alarm signal according to the regional anomaly coefficient;

[0054] S3: Obtain the detection point corresponding to the highest value of the detection anomaly coefficient as the starting detection point, and locate the pollution source area according to the starting detection point and environmental data;

[0055] S4: Provide prompts based on alarm signals and contact management.

[0056] Another aspect of the present invention provides an on-site mobile inspection terminal for an automatic monitoring facility for environmental pollution sources, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0057] Compared with the prior art, the beneficial effects of this application are:

[0058] 1. The present application generates a detection anomaly coefficient according to the detection data; generates a detection point status label according to the detection anomaly coefficient of each detection point and generates an abnormal area based on it; calculates the regional anomaly coefficient according to the abnormal area; generates an alarm signal according to the regional anomaly coefficient; obtains the detection point corresponding to the highest value of the detection anomaly coefficient as the starting detection point, locates the pollution source area according to the starting detection point and environmental data, performs anomaly detection with various indicators affecting water quality as key parameters, and adaptively obtains the abnormal area with each abnormal detection point, thereby improving the efficiency and accuracy of automatic anomaly detection, adaptively searches for the pollution source area in the abnormal area, and improves work efficiency.

[0059] 2. This application groups and divides the detection items according to their corresponding standard safety ranges, calculates them separately based on the characteristics of each group, and finally obtains the detection anomaly coefficient of the detection point, thereby improving the accuracy of the detection anomaly coefficient of the detection point and improving the calculation efficiency.

[0060] 3. This application uses the detection point corresponding to the highest value of the anomaly coefficient in the abnormal area as the starting detection point, and adaptively searches for the position coordinates with the highest detection anomaly coefficient in the abnormal area, thereby constructing the pollution source area, providing strong support for users to find pollution sources, speeding up the process of finding pollution sources, and improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 This is a schematic diagram of the principle of the on-site mobile inspection system for the automatic monitoring facility of environmental pollution sources of this application;

[0063] Figure 2 A flow chart of the method for locating the pollution source area of ​​the present application;

[0064] Figure 3 This is a flow chart of the on-site mobile inspection method for the automatic monitoring facility of environmental pollution sources of this application. DETAILED DESCRIPTION

[0065] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0066] See also Figure 1 , the first aspect of the present application provides an on-site mobile inspection system for automatic monitoring facilities for environmental pollution sources, including: a data acquisition module, a data analysis module, an early warning module and a database;

[0067] Data acquisition module: acquires the detection data, environmental data and coordinate data of several detection points through data acquisition equipment; the data acquisition equipment includes various sensors, etc.; the detection data includes several detection items and their corresponding detection values;

[0068] Data analysis module: Generate detection anomaly coefficients based on detection data. Detection anomaly coefficients refer to the degree of abnormality of detection points; Generate detection point status labels based on detection anomaly coefficients of each detection point and generate abnormal areas based on them; Calculate regional anomaly coefficients based on abnormal areas. Regional anomaly coefficients refer to the degree of abnormality of abnormal areas; Generate alarm signals based on regional anomaly coefficients; Obtain detection points corresponding to the highest detection anomaly coefficients as starting detection points, and locate pollution source areas based on starting detection points and environmental data;

[0069] Early warning module: Prompts are given based on alarm signals and management personnel are contacted; alarm signals include severe regional pollution alarm signals, severe pollution spread alarm signals, and pollution existence alarm signals, etc.

[0070] In this embodiment, the detection anomaly coefficient is generated according to the detection data, including:

[0071] Obtain several test items and their corresponding test values ​​at the test point, as well as the corresponding standard safety range;

[0072] The test items are divided into test item group 1 and test item group 2 according to the standard safety range; test item group 1 includes test items for which the standard safety range has only an upper limit value or a lower limit value; test item group 2 includes test items for which the standard safety range has both an upper limit value and a lower limit value;

[0073] Anomaly coefficient one YX1 and anomaly coefficient two YX2 are generated according to test item group one and test item group two; anomaly coefficient one and anomaly coefficient two refer to the degree of anomaly of test item group one and test item group two;

[0074] The detection anomaly coefficient JYX of the detection point is calculated by the formula JYX=YX1+YX2; the detection anomaly coefficient of the detection point increases as the anomaly coefficient one and the anomaly coefficient two of the detection point increase.

[0075] This embodiment groups the detection data according to the detection items in the detection data, uses the standard safety range corresponding to the detection items as the division standard, calculates the anomaly coefficient of each group respectively and merges them to finally obtain the detection anomaly coefficient of the detection point, and calculates separately according to the characteristics of each group of detection items, which greatly improves the calculation efficiency.

[0076] In this embodiment, the abnormal coefficient 1 YX1 and the abnormal coefficient 2 YX2 are generated according to the detection item group 1 and the detection item group 2, including:

[0077] Extract the test values ​​JS corresponding to several test items in test item group 1 i , the upper or lower limit of the corresponding standard safety range DXZ i , and the test values ​​JS corresponding to several test items in test item group 2 j , the upper and lower limits of the corresponding standard safety range;

[0078] Generate the standard safety intermediate value BAZ based on the upper and lower limits of the standard safety range j and floating deviation FP j ; The standard safety middle value refers to the value in the middle of the standard safety range; the floating deviation refers to the distance between the standard safety middle value and the boundary of the standard safety range;

[0079] Judge each JS in turn i and JS j Whether it is within the corresponding standard safety range;

[0080] Yes, set the outlier coefficient of the detected value to "0", that is, YZX1 i =0,YZX2 j =0;

[0081] No, by formula Calculate the abnormal value coefficient YZX1 corresponding to each test item in test item group 1 i ; The more each test value in the test item group 1 deviates from its corresponding standard safety range, the more serious the abnormal state of the test item corresponding to the test value is; therefore, the abnormal value coefficient of each test item in the test item group 1 increases accordingly;

[0082] By formula Calculate the abnormal value coefficient YZX2 corresponding to each test item in test item group 2 j ; Among them, β1 i and β2 j is the exponential coefficient, β1 i and β2 j ∈(0,1), the specific value is set according to experience; DX1 i and DX2 j are the unit data of the i-th test item in test item group 1 and the j-th test item in test item group 2, respectively. The specific values ​​are set according to experience. The more each test value in test item group 2 deviates from its corresponding standard safety range, the more serious the abnormal state of the test item corresponding to the test value is. Therefore, the abnormal value coefficient of each test item in test item group 2 increases accordingly.

[0083] Through the formula YX1 = Σ i γ1 i ×YZX1 i and YX2 = Σ j γ2 j ×YZX2 j Calculate the abnormal coefficient 1 YX1 and the abnormal coefficient 2 YX2; where γ1 i and γ2 j is the weight coefficient, and γ1 i and γ2 j ∈(0, 1), the specific value is set according to experience; the abnormal coefficients corresponding to the detection item group 1 and the detection item group 2 increase with the increase of the abnormal value coefficient in the detection item group.

[0084] In this embodiment, the standard safety intermediate value BAZ is generated according to the upper limit and lower limit of the standard safety range. j and floating deviation FP j ,include:

[0085] The upper limit value in the standard safety range corresponding to the test item numbered j is marked as BAFDj , the lower limit is marked as BAFX j ;

[0086] BAZ by Formula j =(BAFD j +BAFX j ) / 2 Calculate the standard safety intermediate value BAZ of several test data j ; The standard safety middle value increases as its corresponding upper and lower limits increase;

[0087] By formula FP j =(BAFD j -BAFX j ) / 2 Calculate the floating deviation FP of several test data j ; The larger the standard safety range, the greater the corresponding floating deviation.

[0088] In this embodiment, the detection point status label is generated according to the detection abnormality coefficient of each detection point and the abnormal area is generated based on it, including:

[0089] Obtain the detection anomaly coefficient and coordinate data of each detection point; the coordinate data includes two-dimensional coordinates and three-dimensional coordinates, etc.;

[0090] Determine whether the detection anomaly coefficient is greater than the anomaly threshold, which is set based on experience; if yes, set the detection point as an anomaly label; if no, set the detection point as a normal label;

[0091] Extract the coordinate data of the detection point as a normal tag; the coordinate data in this embodiment adopts two-dimensional coordinates;

[0092] The Graham scanning method was used to construct a convex shape as the abnormal area.

[0093] In this embodiment, a status label is set for each detection point by detecting an abnormality coefficient, and an abnormal area is constructed for each detection point whose label status is an abnormal label through the Graham scanning method. The abnormal area changes adaptively with the coordinate data of the abnormal label detection point, thereby improving the accuracy and efficiency of generating the abnormal area.

[0094] In this embodiment, calculating the regional anomaly coefficient according to the abnormal region includes:

[0095] Get the abnormal area S of the abnormal area and the detection abnormality coefficients JYX of several detection points in the abnormal area b ;

[0096] By formula Calculate the regional anomaly coefficient QYX; wherein α1 and α2 are weight coefficients, α1 and α2∈(0,1), and the specific values ​​are set according to experience; DS is the unit area, and the specific value is set according to experience. In this embodiment, DS is set to 1㎡; the larger the abnormal area in the abnormal region, the more serious the pollution in the abnormal region; similarly, the larger the average detection anomaly coefficient of each detection point in the abnormal region, the more serious the pollution in the abnormal region; therefore, the regional anomaly coefficient increases with the increase of the abnormal area and the average detection anomaly coefficient.

[0097] In this embodiment, generating an alarm signal according to the regional anomaly coefficient includes:

[0098] Obtain regional anomaly coefficient, anomaly area and several detection anomaly coefficients;

[0099] Determine whether the regional anomaly coefficient is greater than the regional anomaly threshold, and the regional anomaly threshold is set based on experience;

[0100] Yes, a serious regional pollution warning signal is generated;

[0101] No, determine whether the abnormal area is larger than the area threshold, which is set based on experience; if yes, generate a serious pollution spread alarm signal; if no, do nothing;

[0102] Determine whether there is a detection anomaly coefficient greater than the detection threshold, which is set based on experience; if yes, generate a pollution alarm signal; if no, do nothing.

[0103] See also Figure 2 In this embodiment, locating the pollution source area according to the starting detection point and environmental data includes the following steps:

[0104] Step 1: Obtain the coordinate data of the starting detection point, the detection anomaly coefficient JYX and the water flow velocity SV in the environmental data;

[0105] Step 2: Calculate the moving distance YJ by the formula YJ=BYJ×(1+ln(SV+1)); BYJ represents the standard moving distance, and the specific value is set according to experience. In this embodiment, BYJ is set to 1m; with the coordinate position of the starting detection point as the center and the moving distance as the radius, a number of surrounding detection points are set according to the moving direction; when the surrounding detection point is beyond the abnormal area, the intersection with the abnormal area is used as the surrounding detection point; in this embodiment, the moving direction is set to 8, which are divided into east, south, west, north, southeast, northeast, southwest and northwest; the faster the water flow rate, the pollutants can diffuse to a farther distance in a short time, so the moving distance will increase accordingly; when the water flow rate is 0, that is, the water surface is in a static state, the moving distance is the standard moving distance;

[0106] Step 3: Calculate the detection anomaly coefficients JYXc of several surrounding detection points; wherein c represents the number of the surrounding detection points; c=1, 2, ..., Q; Q is the total number of surrounding detection points; Q in this embodiment is set to 8; determine whether there is a detection anomaly coefficient of the surrounding detection point that is greater than the detection anomaly coefficient of the starting detection point;

[0107] If yes, set the coordinate position corresponding to the maximum value of the detection anomaly coefficients around the detection point as the starting detection point and go to step 1; if no, go to step 4;

[0108] Step 4: Use the starting detection point as the center of the circle and the distance R as the radius to construct the pollution source area; the distance R is set based on experience.

[0109] This embodiment uses the detection point corresponding to the highest value of the detection anomaly coefficient in the abnormal area as the starting detection point, and each time selects the surrounding detection point with the highest detection anomaly coefficient from the surrounding detection points around the starting detection point as the starting detection point for the next cycle until the position of the detection point with the final detection anomaly coefficient is found, thereby constructing the pollution source area, narrowing the search range of the pollution source, improving work efficiency, and enhancing the user experience.

[0110] See also Figure 3 The second aspect of the present application provides an on-site mobile inspection method for an automatic monitoring facility for environmental pollution sources, comprising:

[0111] S0: Acquire the detection data, environmental data and coordinate data of several detection points;

[0112] S1: Generate a detection anomaly coefficient based on the detection data; generate a detection point status label based on the detection anomaly coefficient of each detection point and generate an abnormal area based on it;

[0113] S2: Calculate the regional anomaly coefficient according to the abnormal area; generate an alarm signal according to the regional anomaly coefficient;

[0114] S3: Obtain the detection point corresponding to the highest value of the detection anomaly coefficient as the starting detection point, and locate the pollution source area according to the starting detection point and environmental data;

[0115] S4: Provide prompts based on alarm signals and contact management.

[0116] Another embodiment of the present application provides an on-site mobile inspection terminal for an automatic monitoring facility for environmental pollution sources, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0117] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula 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.

[0118] The working principle of the present application is as follows: by acquiring detection data, environmental data and coordinate data of several detection points; generating detection anomaly coefficients according to the detection data; generating detection point status labels according to the detection anomaly coefficients of each detection point and generating an abnormal area based on this; calculating the regional anomaly coefficient according to the abnormal area; generating an alarm signal according to the regional anomaly coefficient; obtaining the detection point corresponding to the highest value of the detection anomaly coefficient as the starting detection point, and locating the pollution source area according to the starting detection point and environmental data; making prompts according to the alarm signal and contacting the management personnel, performing anomaly detection with various indicators affecting water quality as key parameters, and adaptively obtaining the abnormal area with each abnormal detection point, thereby improving the efficiency and accuracy of automatic anomaly detection, adaptively searching for the pollution source area within the abnormal area, improving work efficiency, and avoiding the problem that the existing technology monitors fixed areas and lacks consideration of locating the pollution source after pollution occurs, resulting in low efficiency in environmental pollution source monitoring.

[0119] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. On-site mobile inspection system for automatic monitoring facilities of environmental pollution sources, characterized by: include: Data collection module, data analysis module, early warning module and database; The data acquisition module is used to acquire detection data, environmental data and coordinate data of several detection points through data acquisition equipment; The data analysis module generates a detection anomaly coefficient according to the detection data; generates a detection point status label according to the detection anomaly coefficient of each detection point and generates an abnormal area based on the detection anomaly coefficient of each detection point; Calculate a regional anomaly coefficient based on the abnormal area; generate an alarm signal based on the regional anomaly coefficient; Obtain the detection point corresponding to the highest value of the detection anomaly coefficient as the starting detection point, and locate the pollution source area based on the starting detection point and environmental data; The early warning module: makes prompts according to the alarm signal and contacts the management personnel.

2. The on-site mobile inspection system for automatic monitoring facilities for environmental pollution sources according to claim 1 is characterized in that: The generating of the detection anomaly coefficient according to the detection data comprises: Obtain several test items and their corresponding test values ​​at the test point, as well as the corresponding standard safety range; The test items are divided into a test item group 1 and a test item group 2 according to the standard safety range; the test item group 1 includes test items for which the standard safety range has only an upper limit value or a lower limit value; the test item group 2 includes test items for which the standard safety range has both an upper limit value and a lower limit value; Generate abnormal coefficient 1 YX1 and abnormal coefficient 2 YX2 according to test item group 1 and test item group 2; The detection anomaly coefficient of the detection point is obtained by summing the anomaly coefficient one and the anomaly coefficient two.

3. The on-site mobile inspection system for automatic monitoring facilities of environmental pollution sources according to claim 2 is characterized in that: The generating of abnormal coefficient 1 YX1 and abnormal coefficient 2 YX2 according to the detection item group 1 and the detection item group 2 includes: Extract the test values ​​JS corresponding to several test items in test item group 1 i , the upper or lower limit of the corresponding standard safety range DXZ i And the test values ​​JS corresponding to several test items in test item group 2 j , the upper and lower limits of the corresponding standard safety range; Generate the standard safety intermediate value BAZ based on the upper and lower limits of the standard safety range j and floating deviation FP j ; Judge each JS in turn i and JS j Whether it is within the corresponding standard safety range; Yes, set the abnormal value coefficient of the detection value to "0", that is, YZX1 i =0,YZX2 j =0; No, by formula Calculate the abnormal value coefficient YZX1 corresponding to each test item in test item group 1 i ; By formula Calculate the abnormal value coefficient YZX2 corresponding to each test item in test item group 2 j ; Among them, β1 i and β2 j is the exponential coefficient, β1 i and β2 j ∈(0,1);DX1 i and DX2 j are the unit data of the i-th test item in test item group 1 and the j-th test item in test item group 2 respectively; Through the formula YX1=∑ i γ1 i ×YZX1 i and YX2=∑ j γ2 j ×YZX2 j Calculate the abnormal coefficient 1 YX1 and the abnormal coefficient 2 YX2; where γ1 i and γ2 j is the weight coefficient, and γ1 i and γ2 j ∈(0,1).

4. The on-site mobile inspection system for automatic monitoring facilities of environmental pollution sources according to claim 3 is characterized in that: The standard safety intermediate value BAZ is generated according to the upper limit and lower limit of the standard safety range. j and floating deviation FP j ,include: The upper limit value in the standard safety range corresponding to the test item numbered j is marked as BAFD j , the lower limit is marked as BAFX j ; The standard safety intermediate value BAZ of several test data is obtained by taking half of the sum of the corresponding upper limit and lower limit. j ; The floating deviation FP of several test data is obtained by taking half of the difference between the corresponding upper limit and lower limit. j .

5. The on-site mobile inspection system for automatic monitoring facilities of environmental pollution sources according to claim 1 is characterized in that: The step of generating a detection point status label according to the detection anomaly coefficient of each detection point and generating an abnormal area therefrom includes: Obtain the detection anomaly coefficient and coordinate data of each detection point; Determine whether the detection anomaly coefficient is greater than the anomaly threshold; if yes, set the detection point as an anomaly label; if no, set the detection point as a normal label; Extract the coordinate data of the detection point as a normal label; The Graham scanning method was used to construct a convex shape as the abnormal area.

6. The on-site mobile inspection system for automatic monitoring facilities of environmental pollution sources according to claim 1 is characterized in that: The calculating of the regional anomaly coefficient according to the abnormal region includes: Get the abnormal area S of the abnormal area and the detection abnormality coefficients JYX of several detection points in the abnormal area b ; Through the formula QYX = 1-e -(α1×S / DS+α2×∑ b JYXb / max{b}) Calculate the regional anomaly coefficient QYX; where α1 and α2 are weight coefficients, α1 and α2∈(0,1); DS is the unit area.

7. The on-site mobile inspection system for automatic monitoring facilities of environmental pollution sources according to claim 1 is characterized in that: The generating of an alarm signal according to the regional anomaly coefficient comprises: Obtain regional anomaly coefficient, anomaly area and several detection anomaly coefficients; Determine whether the regional anomaly coefficient is greater than the regional anomaly threshold; Yes, a serious regional pollution warning signal is generated; No, determine whether the abnormal area is greater than the area threshold; Yes, generate a serious pollution spread alarm signal; No, do nothing; Determine whether there is a detection anomaly coefficient greater than the detection threshold; if yes, generate a pollution alarm signal; if no, do nothing.

8. The on-site mobile inspection system for automatic monitoring facilities of environmental pollution sources according to claim 1 is characterized in that: The method of locating the pollution source area according to the starting detection point and environmental data includes the following steps: Step 1: Obtain the coordinate data of the starting detection point, the detection anomaly coefficient JYX and the water flow velocity SV in the environmental data; Step 2: Calculate the moving distance YJ by the formula YJ=BYJ×(1+ln(SV+1)); BYJ represents the standard moving distance; take the coordinate position of the starting detection point as the center, take the moving distance as the radius, and set a number of surrounding detection points according to the moving direction; when the surrounding detection point is beyond the abnormal area, the intersection with the abnormal area is used as the surrounding detection point; Step 3: Calculate the detection anomaly coefficients JYXc of several surrounding detection points; where c represents the number of the surrounding detection points; c=1, 2, ..., Q; Q is the total number of surrounding detection points; determine whether there is a detection anomaly coefficient of the surrounding detection point that is greater than the detection anomaly coefficient of the starting detection point; If yes, set the coordinate position corresponding to the maximum value of the detection anomaly coefficients around the detection point as the starting detection point and go to step 1; if no, go to step 4; Step 4: Use the starting detection point as the center of the circle and the distance R as the radius to construct the pollution source area.

9. A method for on-site mobile inspection of automatic monitoring facilities for environmental pollution sources, applied to an on-site mobile inspection system for automatic monitoring facilities for environmental pollution sources as claimed in any one of claims 1 to 8, characterized in that: include: S0: Acquire the detection data, environmental data and coordinate data of several detection points; S1: Generate a detection anomaly coefficient based on the detection data; generate a detection point status label based on the detection anomaly coefficient of each detection point and generate an abnormal area based on it; S2: Calculate the regional anomaly coefficient according to the abnormal area; generate an alarm signal according to the regional anomaly coefficient; S3: Obtain the detection point corresponding to the highest value of the detection anomaly coefficient as the starting detection point, and locate the pollution source area according to the starting detection point and environmental data; S4: Provide prompts based on alarm signals and contact management.

10. An on-site mobile inspection terminal for automatic monitoring facilities for environmental pollution sources, applied to an on-site mobile inspection system for automatic monitoring facilities for environmental pollution sources according to any one of claims 1 to 8, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor.

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