Environmental protection information datamation management monitoring method and system based on artificial intelligence

Abstract

The invention discloses an environmental protection information datamation management monitoring method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, the system comprises a management center, and the management center is connected with an intelligent acquisition module, an information processing module, a data analysis module and a comprehensive monitoring module; constructing a monitoring domain space, extracting multi-dimensional environmental protection information, obtaining a statistical multi-dimensional coefficient, setting a dynamic amplitude function to perform dimension expansion, and obtaining an expanded amplitude function; carrying out local acquisition on the extended amplitude function to obtain a local amplitude function, carrying out equipartition extraction on the multi-dimensional environmental protection signal through the local amplitude function to obtain an environmental protection environmental protection local wave coefficient, and constructing an environmental protection feature representation graph; carrying out segmented extraction on the environmental protection characteristic representation graph to obtain a peak domain characteristic value, and setting an analysis threshold to carry out trace judgment on the peak domain characteristic value to obtain a pollution flow direction trace; the data management efficiency is improved, the pollution source is accurately positioned, and the monitoring effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and particularly to an environmental protection information digital management and monitoring method and system based on artificial intelligence. Background Art

[0002] With the aggravation of global environmental pollution problems and the increasing attention of people to ecological environment protection, the collection, processing and analysis of environmental protection information data have become the key links of environmental management. Traditional environmental protection information management has the following disadvantages: First, most of the environmental protection data collection relies on manual operation, making it difficult to achieve real-time and continuous monitoring; Second, due to the limitations of data management and analysis means, a large amount of environmental protection data has not been effectively utilized; Third, the multi-dimensional and non-linear characteristics of environmental protection data make it difficult for traditional analysis methods to meet the requirements, and more advanced data processing technologies are needed.

[0003] In recent years, the rapid development of artificial intelligence technology has brought new opportunities to environmental protection information management. The powerful capabilities of artificial intelligence in data processing, pattern recognition, intelligent decision-making, etc. provide new solutions for the digital management of environmental protection information data. Therefore, a method and system for digital management and monitoring of environmental protection information based on artificial intelligence are provided. By digitally managing environmental protection information, it is convenient to monitor the source path of pollution, give early warnings in a timely manner, and reduce environmental risks. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for digital management and monitoring of environmental protection information based on artificial intelligence.

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

[0006] An environmental protection information digital management and monitoring system based on artificial intelligence, including a management center, and the management center is connected with an intelligent acquisition module, an information processing module, a data analysis module and a comprehensive monitoring module;

[0007] The intelligent acquisition module is used to generate a virtual monitoring area and collect multi-dimensional environmental protection information and environmental protection collection time;

[0008] The information processing module is used to construct a monitoring domain space, transform the multi-dimensional environmental protection information to obtain multi-dimensional environmental protection signals, extract statistical multi-dimensional coefficients through the multi-dimensional environmental protection signals, set a dynamic amplitude function for dimension expansion, and obtain an extended amplitude function;

[0009] The data analysis module is used to locally collect the extended amplitude function according to the amplitude variable to obtain the local amplitude function, evenly extract the multi-dimensional environmental protection signal through the local amplitude function to obtain the environmental protection local wave coefficient, generate the local coefficient curve based on the environmental protection local wave coefficient and construct the environmental protection feature display graph, and perform internal construction based on the environmental protection feature display graph to obtain the peak domain display box;

[0010] The comprehensive monitoring module is used to perform partial extraction of the local coefficient curve according to the peak domain display box based on the environmental protection feature display graph to obtain the peak domain eigenvalue, set the analysis threshold, and perform trace judgment on the peak domain eigenvalue through the analysis threshold to obtain the pollution flow trace.

[0011] Preferably, the process of collecting multi-dimensional environmental protection information and environmental protection collection time includes:

[0012] Obtain the ground basic information of the environmental monitoring site, generate a virtual monitoring area according to the ground basic information, and perform sampling deployment on the virtual monitoring area to obtain the sampling monitoring terminal;

[0013] Collect data on the environmental monitoring site through the sampling monitoring terminal to obtain multi-dimensional environmental protection information, and perform time marking on the multi-dimensional environmental protection information to obtain the environmental protection collection time.

[0014] Preferably, the process of extracting and statistically analyzing multi-dimensional coefficients from multi-dimensional environmental protection signals includes:

[0015] Perform global construction on the virtual monitoring area to obtain the monitoring domain space, and upload the multi-dimensional environmental protection information to the virtual monitoring area corresponding to the monitoring domain space;

[0016] Perform detail extraction according to the multi-dimensional environmental protection information to obtain multi-dimensional characteristic factors, and perform factor statistics on the multi-dimensional characteristic factors to obtain the statistical multi-dimensional coefficients.

[0017] Preferably, the process of setting the dynamic amplitude function for dimension expansion includes:

[0018] Set the dynamic amplitude function, perform amplitude transformation on the dynamic amplitude function to obtain the amplitude variable, and perform variable measurement on the dynamic amplitude function according to the amplitude variable to obtain the amplitude spacing;

[0019] Perform combined transformation on the statistical multi-dimensional coefficients according to the amplitude variable and the amplitude spacing to obtain the multi-dimensional amplitude variable;

[0020] Perform variable expansion on the dynamic amplitude function through the multi-dimensional amplitude variable to obtain the extended amplitude function.

[0021] Preferably, the process of evenly extracting the multi-dimensional environmental protection signal through the local amplitude function includes:

[0022] Perform local acquisition on the multi-dimensional environmental protection signal according to the obtained amplitude variable to obtain a multi-dimensional signal segment. Upload the local amplitude function to the multi-dimensional environmental protection signal based on the order of local acquisition, and make the position correspondence with the multi-dimensional signal segment after local acquisition;

[0023] Match and extract the multi-dimensional signal segment through the local amplitude function to obtain an amplitude signal segment;

[0024] Integrate the features of the obtained amplitude signal segment based on the order of local acquisition to obtain an environmental protection local wave coefficient.

[0025] Preferably, the process of obtaining the peak domain display box based on the environmental protection feature display diagram includes:

[0026] Upload the environmental protection local wave coefficient to the monitoring domain space, perform position matching on the environmental protection local wave coefficient, and upload the successfully matched environmental protection local wave coefficient to the corresponding virtual monitoring area;

[0027] Mark the intersection points of the local coefficient curve based on the environmental protection feature display diagram to obtain multi-dimensional feature points, and numerically mark the multi-dimensional feature points to obtain feature coefficient values;

[0028] Set the peak domain display box and upload the obtained peak domain display box to the environmental protection feature display diagram.

[0029] Preferably, the process of obtaining the peak domain eigenvalue includes:

[0030] Select feature points from the environmental protection feature map according to the peak domain display box to obtain a selected matching area, and mark the multi-dimensional feature points in the selected matching area as selected feature points;

[0031] Extract the form of the selected matching area based on the peak domain display box to obtain the peak domain eigenvalue;

[0032] Set an interaction distance for the obtained peak domain display box, translate the obtained peak domain display box based on the interaction distance to reach the next selected matching area, and extract the form of the selected matching area through the peak domain display box to obtain the peak domain eigenvalue until the local coefficient curve is covered, then end the translation of the peak domain display box.

[0033] Preferably, the process of obtaining the pollution flow trace by performing trace judgment on the peak domain eigenvalue through an analysis threshold includes:

[0034] Sort the peak domain eigenvalues to obtain a coefficient feature set;

[0035] Perform critical judgment on the coefficient feature set according to the analysis threshold to obtain an abnormal judgment coefficient;

[0036] Perform time sorting on the abnormal judgment coefficient based on the environmental protection acquisition time to obtain a coefficient abnormal time series;

[0037] Based on the monitoring domain space, the virtual monitoring area is characterized by the obtained coefficient anomaly time series to obtain the monitoring identification area;

[0038] Based on the environmental protection collection time, a time comparison is made on the monitoring identification area to obtain the pollution flow trace.

[0039] Based on the above-mentioned environmental protection information digital management monitoring system based on artificial intelligence, the present invention also provides an environmental protection information digital management monitoring method based on artificial intelligence, including the following steps:

[0040] Step 1: Generate a virtual monitoring area and collect multi-dimensional environmental protection information;

[0041] Step 2: Construct a monitoring domain space, transform the multi-dimensional environmental protection information to obtain multi-dimensional environmental protection signals, extract statistical multi-dimensional coefficients through the multi-dimensional environmental protection signals, set a dynamic amplitude function for dimension expansion, and obtain an extended amplitude function;

[0042] Step 3: Perform local collection on the extended amplitude function according to the amplitude variable to obtain a local amplitude function, evenly extract the multi-dimensional environmental protection signals through the local amplitude function to obtain environmental protection local wave coefficients, generate a local coefficient curve based on the environmental protection local wave coefficients and construct an environmental protection feature performance diagram, and perform internal construction based on the environmental protection feature performance diagram to obtain a peak domain display box;

[0043] Step 4: Based on the environmental protection feature performance diagram, perform partial extraction on the local coefficient curve according to the peak domain display box to obtain peak domain eigenvalues, set an analysis threshold, and perform trace judgment on the peak domain eigenvalues through the analysis threshold to obtain the pollution flow trace.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] 1. By constructing a virtual domain space to comprehensively collect environmental protection information, expanding the source of environmental protection information, which helps to improve the comprehensiveness of environmental protection information management. The collected environmental protection information is processed into a signal form for digital management, increasing the convenience of data analysis. By digitally processing the collected environmental protection information, it effectively reduces the reliance on manpower and reduces the operating costs of environmental protection departments;

[0046] 2. Standardize the environmental protection information in the form of signals, extract local feature information, use it to construct a curve graph for abnormal data analysis, obtain abnormal environmental protection indicators, improve the data processing speed and analysis accuracy, and convert the digital information into a graphical form for further analysis, which is conducive to quickly identifying trends and laws;

[0047] 3. Analyze the time of the analysis results of the curve graph in the virtual domain space, obtain the time characteristics of the abnormal pollution data in each monitoring area, and then obtain the pollution path of the pollutant, which is conducive to mastering the pollutant diffusion path, deploying emergency measures in a timely manner, effectively preventing pollution diffusion, and improving the environmental governance effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0051] As Figure 1 shown, an environmental protection information digital management and monitoring system based on artificial intelligence includes a management center, and the management center is connected with an intelligent acquisition module, an information processing module, a data analysis module, and a comprehensive monitoring module;

[0052] The intelligent acquisition module is used to generate a virtual monitoring area and collect multi-dimensional environmental protection information and environmental protection acquisition time;

[0053] The information processing module is used to construct a monitoring domain space, transform the multi-dimensional environmental protection information, obtain multi-dimensional environmental protection signals, extract and statistically analyze multi-dimensional coefficients through the multi-dimensional environmental protection signals, set a dynamic amplitude function for dimension expansion, and obtain an extended amplitude function;

[0054] The data analysis module is used to perform local acquisition on the extended amplitude function according to the amplitude variable to obtain a local amplitude function, evenly extract the multi-dimensional environmental protection signals through the local amplitude function to obtain environmental protection local wave coefficients, generate a local coefficient curve based on the environmental protection local wave coefficients and construct an environmental protection feature display graph, and perform internal structure based on the environmental protection feature display graph to obtain a peak domain display frame;

[0055] The comprehensive monitoring module is used to extract the partial coefficient curves according to the peak domain display box based on the environmental protection feature performance diagram, obtain the peak domain characteristic values, set the analysis threshold, and perform trace judgment on the peak domain characteristic values through the analysis threshold to obtain the pollution flow trace.

[0056] In practical applications, the process of the intelligent acquisition module for acquiring multi-dimensional environmental protection information includes:

[0057] Obtain the ground basic information of the environmental monitoring site, and generate a virtual monitoring area according to the obtained ground basic information;

[0058] The environmental monitoring site refers to the area where environmental monitoring is carried out, and the ground basic information refers to all the basic information within the environmental monitoring area, such as ground soil information, building facility information within the area, vegetation growth information within the area, and human settlement information within the area. The virtual monitoring area is a three-dimensional virtual conversion of the environmental monitoring site according to the ground basic information. Use existing three-dimensional modeling software to construct a three-dimensional solid model according to the ground basic information, denoted as the virtual monitoring area. Among them, the three-dimensional modeling software can be AutoCAD, SketchUp, etc. The structure and function of the virtual monitoring area are exactly the same as those of the real environmental monitoring site;

[0059] Perform sampling deployment on the obtained virtual monitoring area to obtain sampling monitoring terminals, and associate the obtained sampling monitoring terminals with the corresponding virtual monitoring areas;

[0060] Collect data on the environmental monitoring site through the sampling monitoring terminals to obtain multi-dimensional environmental protection information, and perform time marking on the multi-dimensional environmental protection information to obtain the environmental protection collection time. Among them, the time marking means that each multi-dimensional environmental protection information has a corresponding time point, and this time point is the environmental protection collection time of the multi-dimensional environmental protection information;

[0061] The multi-dimensional environmental protection information includes air pollution data, water pollution data, noise pollution data, solid waste data, and soil pollution data;

[0062] Associate the obtained multi-dimensional environmental protection information with the corresponding sampling monitoring terminals;

[0063] Furthermore, the sampling deployment means to monitor and deploy the places where information needs to be collected in the virtual monitoring area for collecting environmental protection information at the deployment positions, that is, to collect the environmental protection information at the corresponding positions of the environmental monitoring site by the sampling monitoring terminals, denoted as multi-dimensional environmental protection information.

[0064] The information processing module is used to construct a monitoring domain space, extract the multi-dimensional environmental protection information, obtain the statistical multi-dimensional coefficients, and set a dynamic amplitude function for dimension expansion to obtain an extended amplitude function. The specific process includes:

[0065] Globally construct the obtained virtual monitoring area to obtain a monitoring domain space;

[0066] The global construction means constructing a blank virtual space according to the virtual monitoring area, uploading the obtained virtual monitoring area to the blank virtual space to obtain a monitoring domain space, where the blank virtual space is a space for storing virtual monitoring areas, and in the monitoring domain space, the arrangement order and positional relationship of the virtual monitoring areas are exactly the same as the arrangement order and positional relationship of the environmental monitoring site;

[0067] Upload the obtained multi-dimensional environmental protection information to the monitoring domain space, and upload the multi-dimensional environmental protection information to the corresponding virtual monitoring area according to the virtual monitoring area associated with the multi-dimensional environmental protection information;

[0068] Perform cell conversion on the obtained multi-dimensional environmental protection information to obtain multi-dimensional environmental protection signals;

[0069] The cell conversion means converting the obtained multi-dimensional environmental protection signals into signal forms. Then, according to the air pollution data, water pollution data, noise pollution data, solid waste data, and soil pollution data included in the multi-dimensional environmental protection information, the multi-dimensional environmental protection signals include air pollution signals, water pollution signals, noise pollution signals, solid waste signals, and soil pollution signals;

[0070] Extract details from the obtained multi-dimensional environmental protection information to obtain multi-dimensional characteristic factors;

[0071] The detail extraction means extracting the signal characteristics of the multi-dimensional environmental protection information to obtain signal characteristics, where the signal characteristics include amplitude, frequency, phase, and bandwidth, and the frequency and amplitude in the signal characteristics are denoted as multi-dimensional characteristic factors;

[0072] Perform factor statistics on the obtained multi-dimensional characteristic factors to obtain statistical multi-dimensional coefficients;

[0073] The factor statistics means performing internal combination statistics on the multi-dimensional characteristic factors to obtain multi-dimensional coefficients, denoted as statistical multi-dimensional coefficients, and marking the obtained statistical multi-dimensional coefficients as where, represents the lowest effective frequency of the multi-dimensional environmental protection signal, represents the highest effective frequency of the multi-dimensional environmental protection signal, represents the center frequency of the multi-dimensional environmental protection signal, represents the amplitude of the multi-dimensional environmental protection signal, i represents the number of the multi-dimensional environmental protection signal, i = 1, 2, 3,..., v1, and v1 is a positive integer;

[0074] ​Set a dynamic amplitude function, and the manifestation form of the dynamic amplitude function is in the form of a function; in this embodiment, based on the wavelet function, a wavelet function is selected and denoted as the dynamic amplitude function, where the wavelet function includes but is not limited to Haar wavelet, SymN wavelet, and Morlet wavelet;

[0075] Perform amplitude transformation on the obtained dynamic amplitude function to obtain an amplitude variable, and mark the obtained amplitude variable as B;

[0076] The amplitude transformation represents controlling the stretching and translation of the dynamic amplitude function in the time dimension and frequency dimension, and marking the distances of stretching and translation to obtain an amplitude variable;

[0077] Perform variable measurement on the dynamic amplitude function according to the obtained amplitude variable to obtain an amplitude spacing, and mark the obtained amplitude spacing as ZJ, where, , represents the maximum value of the amplitude variable in the dynamic amplitude function, represents the minimum value of the amplitude variable in the dynamic amplitude function, and e represents the number of amplitude variables in the dynamic amplitude function;

[0078] Perform combined transformation on the statistical multi-dimensional coefficient according to the obtained amplitude variable and amplitude spacing to obtain a multi-dimensional amplitude variable, and mark the obtained multi-dimensional amplitude variable as ,where, ;

[0079] Perform variable expansion on the dynamic amplitude function through the multi-dimensional amplitude variable to obtain an expanded amplitude function;

[0080] Mark the obtained expanded amplitude function as KZ, where, , DB represents the dynamic amplitude function, and by multiplying with the characteristics of multi-dimensional environmental protection information extraction, it can enhance the local characteristics of the expanded amplitude function, improve the matching degree between the dynamic amplitude function and the multi-dimensional environmental protection signal, and is conducive to extracting the representative characteristics of the multi-dimensional environmental protection signal, that is, the characteristics that can indicate the identity information of the multi-dimensional environmental protection signal.

[0081] The data analysis module is used to perform local acquisition on the expanded amplitude function to obtain a local amplitude function, and evenly extract the multi-dimensional environmental protection signal through the local amplitude function to obtain an environmental protection local wave coefficient and construct an environmental protection feature representation diagram. The specific process includes:

[0082] Obtain the amplitude variable, and perform local acquisition on the expanded amplitude function according to the amplitude variable to obtain a local amplitude function;

[0083] The local acquisition means segmenting the extended amplitude function according to the obtained amplitude variables, that is, equally dividing the extended amplitude function according to the number of amplitude variables in the dynamic amplitude function to obtain local amplitude functions with equal lengths, and the number of segmented local amplitude functions is equal to the number of amplitude variables in the dynamic amplitude function;

[0084] Upload the obtained local amplitude functions to the multi-dimensional environmental protection signal, and evenly extract the multi-dimensional environmental protection signal through the local amplitude functions to obtain environmental protection local wave coefficients;

[0085] It should be further noted that in the specific implementation process, the process of the even extraction includes:

[0086] Perform local acquisition on the multi-dimensional environmental protection signal according to the obtained amplitude variables to obtain multi-dimensional signal segments, that is, divide the multi-dimensional environmental protection signal into multi-dimensional signal segments with equal lengths according to the number of amplitude variables in the dynamic amplitude function, and the number of multi-dimensional signal segments is equal to the number of amplitude variables in the dynamic amplitude function;

[0087] Upload the obtained local amplitude functions to the multi-dimensional environmental protection signal based on the order of local acquisition, and make them correspond to the positions of the multi-dimensional signal segments after local acquisition, that is, according to the order of local acquisition, make the local amplitude functions correspond one by one to the multi-dimensional signal segments at the corresponding positions;

[0088] Perform matching extraction on the multi-dimensional signal segments through the local amplitude functions to obtain amplitude signal segments;

[0089] The matching extraction means convolving the local amplitude functions with the multi-dimensional signal segments at the corresponding positions to obtain amplitude signal segments;

[0090] Perform feature integration on the obtained amplitude signal segments based on the order of local acquisition to obtain environmental protection local wave coefficients, associate the obtained environmental protection local wave coefficients with the corresponding multi-dimensional environmental protection information, and associate the environmental protection acquisition time corresponding to the multi-dimensional environmental protection information with the environmental protection local wave coefficients;

[0091] The feature integration means summing the amplitude signal segments after matching extraction according to the order of local acquisition to obtain environmental protection local wave coefficients. By extracting the segmented extended amplitude function and the segmented multi-dimensional environmental protection signal, the calculation complexity is reduced and the local features of the multi-dimensional environmental protection signal are enhanced.

[0092] Upload the obtained environmental protection local wave coefficients to the monitoring domain space, perform position matching on the environmental protection local wave coefficients, and upload the successfully matched environmental protection local wave coefficients to the corresponding virtual monitoring area;

[0093] Further, the position matching indicates obtaining a virtual monitoring area associated with multi-dimensional environmental protection information corresponding to an environmental protection partial wave coefficient, which is denoted as successful matching between the environmental protection partial wave coefficient and the virtual monitoring area;

[0094] Construct a two-dimensional rectangular coordinate system based on the environmental protection acquisition time, generate a local coefficient curve according to the obtained environmental protection partial wave coefficient, upload the obtained local coefficient curve to the two-dimensional rectangular coordinate system, and obtain an environmental protection feature representation diagram;

[0095] Further, the horizontal axis of the environmental protection feature representation diagram represents the environmental protection acquisition time, the intersection of the horizontal axis and the vertical axis on the local coefficient curve represents the value of the environmental protection partial wave coefficient, and each environmental protection feature representation diagram includes a curve of the environmental protection partial wave signal of all multi-dimensional environmental protection information in a virtual monitoring area;

[0096] Obtain the environmental protection feature representation diagram, mark the intersections of the obtained local coefficient curves to obtain multi-dimensional feature points, and mark the obtained multi-dimensional feature points as W m , where m represents the number of multi-dimensional feature points on the local coefficient curve, m = 1, 2, 3, ……, v2, v2 is a positive integer, and numerically mark the obtained multi-dimensional feature points to obtain feature coefficient values;

[0097] The intersection marking means that in the environmental protection feature representation diagram, the intersection of the time point on the horizontal axis and the corresponding position of the local coefficient curve is denoted as a multi-dimensional feature point, and the value at the multi-dimensional feature point is denoted as the feature coefficient value;

[0098] Set a peak region display box, which is a variable-sized rectangular box used to enclose the selected local coefficient curve. Among them, the size of the peak region display box is determined according to the number of multi-dimensional feature points selected from the local coefficient curve in the environmental protection feature representation diagram, satisfying that the selected multi-dimensional feature points are continuous and the number of multi-dimensional feature points has a square root. In this embodiment, the number of multi-dimensional feature points selected by the peak region display box is 9;

[0099] For example, if you want to select 4 multi-dimensional feature points of the local coefficient curve in the environmental protection feature representation diagram, such as W4, W5, W6, W7, then change the size of the peak region display box until it can enclose the selected multi-dimensional feature points W4, W5, W6, W7. If 9 multi-dimensional feature points of the local coefficient curve are selected in the environmental protection feature representation diagram, such as W6, W7, W8, W9, W 10 、W 11 、W 12 、W 13 、W 14 , then change the size of the peak region display box until it can enclose the selected multi-dimensional feature points W6, W7, W8, W9, W 10 、W 11 、W12 、W 13 、W 14 ;

[0100] Upload the obtained peak region display frame to the environmental protection feature performance graph. Among them, the initial position for uploading the peak region display frame is that the first selected feature point selected by the peak region display frame corresponds and coincides with the first multi-dimensional feature point of the local coefficient curve.

[0101] The comprehensive monitoring module is used to perform partial extraction on the environmental protection feature performance graph, obtain peak region feature values, set an analysis threshold to perform trace judgment on the peak region feature values, and obtain the pollution flow trace. The specific process includes:

[0102] Based on the environmental protection feature performance graph, perform partial extraction on the local coefficient curve according to the obtained peak region display frame to obtain peak region feature values;

[0103] It should be further noted that in the specific implementation process, the process of the partial extraction includes:

[0104] Select feature points from the environmental protection feature graph according to the obtained peak region display frame to obtain a selection matching area, and mark the multi-dimensional feature points in the selection matching area as selected feature points;

[0105] The feature point selection means that according to the number of multi-dimensional feature points that can be selected by the peak region display frame, the curve area that the peak region display frame can select in the local coefficient curve is recorded as the selection matching area, and the multi-dimensional feature points falling in the selection matching area are marked as selected feature points;

[0106] Perform form extraction on the obtained selection matching area based on the peak region display frame to obtain peak region feature values;

[0107] The process of the form extraction includes:

[0108] Construct an equal-row and equal-column blank matrix according to the selection matching area corresponding to a peak region display frame, and upload the obtained selected feature points to the blank matrix. According to this embodiment, the number of selected feature points selected by the peak region display frame is 9, so the constructed blank matrix is a 3-row and 3-column matrix, and according to the numbering order of the selected feature points, upload the corresponding feature coefficient values of the selected feature points to the corresponding positions of the blank matrix to obtain a selected feature matrix; for example, there are selected feature points W6, W7, W8, W9, W 10 、W 11 、W 12 、W 13 、W 14 , then the first row of the obtained selected feature matrix is W6, W7, W8, the second row is W9, W 10 、W 11 , and the third row is W 12 、W13 、W 14 ;

[0109] Perform matrix extraction on the obtained selected feature matrix to obtain peak domain eigenvalues;

[0110] The matrix extraction means calculating the determinant value of the selected feature matrix, which is the peak domain eigenvalue;

[0111] Set an interaction distance for the obtained peak domain display box. The interaction distance represents the distance to control the peak domain display box to move to the next position on the local coefficient curve. In this embodiment, the interaction distance is to skip one multi-dimensional feature point, that is, the distance from the first selected feature point selected in the peak domain display box to the next position is separated by one multi-dimensional feature point. For example, if the first selected feature point in the peak domain display box is W8, when the peak domain display box moves to the next position, the first selected feature point in the peak domain display box is W9;

[0112] Translate the obtained peak domain display box based on the interaction distance to reach the next selection matching area, and perform form extraction on the selection matching area through the peak domain display box to obtain peak domain eigenvalues until the local coefficient curve is covered. Then, end the translation of the peak domain display box. Among them, "until the local coefficient curve is covered" means that the last selected feature point of the peak domain display box coincides with the last multi-dimensional feature point of the local coefficient curve, then end the translation, and perform statistics on the obtained peak domain eigenvalues;

[0113] Sort the obtained peak domain eigenvalues in descending order to obtain a coefficient feature set. The coefficient feature set contains peak domain eigenvalues arranged in order. Each coefficient feature set represents the peak domain eigenvalues corresponding to an environmental protection local wave coefficient, that is, the corresponding associated multi-dimensional environmental protection information;

[0114] Set an analysis threshold. The analysis threshold is preset and set according to the content of various multi-dimensional environmental protection information and combined with official environmental protection requirements. The official environmental protection requirements represent the environmental protection requirements set by local governments or institutions. Among them, according to each type of environmental protection information, there are different requirement standards, so the analysis thresholds set for each type of environmental protection information are different. For example, water pollution data and noise pollution data in environmental protection information are two different environmental protection data, and the corresponding analysis thresholds are also different, both are thresholds set according to official environmental protection requirements; In particular, upload the obtained analysis threshold to the information processing module for signal processing to obtain a threshold extended amplitude function, then upload the threshold extended amplitude function to the data analysis module for analysis to obtain a threshold environmental protection local wave coefficient, and upload the threshold environmental protection local wave coefficient to the environmental protection feature display graph to generate a threshold local coefficient curve, and extract the peak domain eigenvalues of the threshold. Then, the final form of the obtained analysis threshold is the same as the peak domain eigenvalue, which is convenient for threshold comparison;

[0115] Perform a critical judgment on the coefficient feature set according to the obtained analysis threshold to obtain an abnormal judgment coefficient;

[0116] The critical judgment means that according to the analysis threshold corresponding to each multi-dimensional environmental protection information, the peak domain feature value in the coefficient feature set is compared with the analysis threshold, and the peak domain feature value that meets the abnormal data in the analysis threshold is marked and recorded as the abnormal judgment coefficient. For example, the analysis threshold of the carbon monoxide concentration in air pollution data is: level one when it is less than or equal to g1, level two when it is greater than g1 and less than or equal to g2, and the concentration exceeds the standard when it is greater than g2. Then, according to the analysis threshold, compare the size relationship of the peak domain feature values in the coefficient feature set corresponding to the carbon monoxide concentration. If there is a peak domain feature coefficient greater than g2, it is recorded as the abnormal judgment coefficient;

[0117] Perform a time sorting on the obtained abnormal judgment coefficients based on the environmental protection collection time to obtain a coefficient abnormal time series;

[0118] Furthermore, the coefficient abnormal sequence means that based on the same multi-dimensional environmental protection information, it is sorted according to the chronological order of the environmental protection collection time of the abnormal judgment coefficients, and the obtained sequence represents the different occurrence time points of the abnormal situation of the multi-dimensional environmental protection information. The time point when each multi-dimensional environmental protection information first reaches abnormality can be obtained according to the coefficient abnormal time series; for example, the carbon monoxide concentration in air pollution data is p0 at time t5, p1 at time t7, and p2 at time t8. t5, t7, and t8 increase in sequence with time, p0 > g2, p0 < p2 < p1. Then the corresponding coefficient abnormal time series is the environmental protection collection time of t5, the environmental protection collection time of t7, and the environmental protection collection time of t8. Then the earliest time point to reach abnormality is obtained according to the coefficient abnormal time series as the environmental protection collection time of t5;

[0119] Perform a feature marking on the virtual monitoring area based on the monitoring domain space according to the obtained coefficient abnormal time series to obtain a monitoring identification area;

[0120] The feature marking means performing an abnormal comparison on the virtual monitoring area. If there is a coefficient abnormal time series in the virtual monitoring area, the virtual monitoring area is marked with the corresponding coefficient abnormal time series, and the virtual monitoring area is recorded as the monitoring identification area, indicating that the multi-dimensional environmental protection information containing the coefficient abnormal time series in the virtual monitoring area is marked as the feature marking, and the monitoring identification area is marked with a special color in the monitoring domain space. For example, the monitoring identification area is marked as red; if there is no multi-dimensional environmental protection information containing the coefficient abnormal time series in the virtual monitoring area, it remains the virtual monitoring area;

[0121] Perform a time comparison on the obtained monitoring identification area based on the environmental protection collection time to obtain a pollution flow trace;

[0122] It should be further noted that in the specific implementation process, the process of time comparison includes:

[0123] Obtain the coefficient anomaly time series of the monitoring identification area, and perform sequential comparison on the coefficient anomaly time series based on the monitoring domain space to obtain the primary anomaly time. Here, sequential comparison means recording the first environmental protection collection time in the sorted coefficient anomaly time series as the primary anomaly time, that is, the time and location of the first anomaly of the multi-dimensional environmental protection information in the monitoring identification area;

[0124] Sort the obtained primary anomaly times in chronological order based on the monitoring domain space to obtain the pollution flow trace;

[0125] The pollution flow trace means that in the monitoring domain space, connect the lines in the order of the primary anomaly times corresponding to the monitoring identification areas to obtain the pollution flow trace, which represents the pollution flow trace of the multi-dimensional environmental protection information of the anomaly judgment coefficient corresponding to the primary anomaly time, that is, the pollution flow path of the multi-dimensional environmental protection information, so as to obtain the monitoring identification area that is first polluted, facilitating source tracking;

[0126] Furthermore, according to the special color markings of each monitoring identification area, flash the marked colors in the order of the primary anomaly times, that is, in the order of the primary anomaly times, after the color of the current monitoring identification area flashes, then flash to the monitoring identification area of the next primary anomaly time. In this way, in the monitoring domain space, the pollution trend can be visually observed, facilitating locking the pollution direction and carrying out environmental protection prevention for the surrounding areas;

[0127] Particularly, when performing time comparison, each comparison is only for one type of multi-dimensional environmental protection information, that is, the same type of environmental protection information is compared.

[0128] Based on the above-mentioned environmental protection information digital management and monitoring system based on artificial intelligence, the present invention also provides an environmental protection information digital management and monitoring method based on artificial intelligence, including the following steps:

[0129] Step 1: Generate a virtual monitoring area and collect multi-dimensional environmental protection information;

[0130] Step 2: Construct a monitoring domain space, transform the multi-dimensional environmental protection information to obtain multi-dimensional environmental protection signals, extract statistical multi-dimensional coefficients through the multi-dimensional environmental protection signals, and set a dynamic amplitude function for dimension expansion to obtain an extended amplitude function;

[0131] Step 3: Locally collect the extended amplitude function according to the amplitude variable to obtain the local amplitude function. Use the local amplitude function to evenly extract the multi-dimensional environmental protection signal to obtain the environmental protection local wave coefficient. Generate a local coefficient curve based on the environmental protection local wave coefficient and construct an environmental protection feature representation diagram. Based on the environmental protection feature representation diagram, perform internal construction to obtain the peak domain display frame;

[0132] Step 4: Based on the environmental protection feature representation diagram, extract the local coefficient curve in parts according to the peak domain display frame to obtain the peak domain eigenvalue. Set the analysis threshold, and perform trace judgment on the peak domain eigenvalue through the analysis threshold to obtain the pollution flow trace.

[0133] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific implementation manners. Obviously, according to the content of this specification, many modifications and changes can be made. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An environmental protection information digital management and monitoring system based on artificial intelligence, including a management center, characterized in that, The management center is connected with an intelligent acquisition module, an information processing module, a data analysis module, and a comprehensive monitoring module; The intelligent acquisition module is used to generate a virtual monitoring area, and collect multi-dimensional environmental protection information and environmental protection acquisition time; The information processing module is used to construct a monitoring domain space, transform the multi-dimensional environmental protection information to obtain multi-dimensional environmental protection signals, extract statistical multi-dimensional coefficients through the multi-dimensional environmental protection signals, set a dynamic amplitude function for dimension expansion, and obtain an extended amplitude function; The data analysis module is used to perform local acquisition on the extended amplitude function according to the amplitude variable to obtain a local amplitude function, evenly extract the multi-dimensional environmental protection signals through the local amplitude function to obtain environmental protection local wave coefficients, generate a local coefficient curve based on the environmental protection local wave coefficients and construct an environmental protection feature display graph, and perform internal construction based on the environmental protection feature display graph to obtain a peak domain display box; The comprehensive monitoring module is used to perform partial extraction on the local coefficient curve based on the environmental protection feature display graph according to the peak domain display box to obtain peak domain characteristic values, set an analysis threshold, and perform trace judgment on the peak domain characteristic values through the analysis threshold to obtain pollution flow traces.

2. An environmental protection information digital management and monitoring system based on artificial intelligence according to claim 1, characterized in that The process of collecting multi-dimensional environmental protection information and environmental protection acquisition time includes: Obtain the ground basic information of the environmental monitoring site, generate a virtual monitoring area according to the ground basic information, and perform sampling deployment on the virtual monitoring area to obtain a sampling monitoring terminal; Collect data on the environmental monitoring site through the sampling monitoring terminal to obtain multi-dimensional environmental protection information, and perform time marking on the multi-dimensional environmental protection information to obtain environmental protection acquisition time.

3. An environmental protection information digital management and monitoring system based on artificial intelligence according to claim 1, characterized in that, The process of extracting statistical multi-dimensional coefficients through multi-dimensional environmental protection signals includes: Perform global construction on the virtual monitoring area to obtain a monitoring domain space, and upload the multi-dimensional environmental protection information to the virtual monitoring area corresponding to the monitoring domain space; Perform detail extraction according to the multi-dimensional environmental protection information to obtain multi-dimensional characteristic factors, and perform factor statistics on the multi-dimensional characteristic factors to obtain statistical multi-dimensional coefficients.

4. An environmental protection information digital management and monitoring system based on artificial intelligence according to claim 1, characterized in that, The process of setting a dynamic amplitude function for dimension expansion includes: Set a dynamic amplitude function, perform amplitude transformation on the dynamic amplitude function to obtain an amplitude variable, and perform variable measurement on the dynamic amplitude function according to the amplitude variable to obtain an amplitude spacing; Perform combined transformation on the statistical multi-dimensional coefficients according to the amplitude variable and the amplitude spacing to obtain multi-dimensional amplitude variables; Perform variable expansion on the dynamic amplitude function through the multi-dimensional amplitude variables to obtain an extended amplitude function.

5. An environmental protection information digital management and monitoring system based on artificial intelligence according to claim 1, characterized in that, The process of evenly extracting multi-dimensional environmental protection signals through the local amplitude function includes: Perform local acquisition on the multi-dimensional environmental protection signals according to the obtained amplitude variable to obtain multi-dimensional signal segments, upload the local amplitude function to the multi-dimensional environmental protection signals based on the order of local acquisition, and make the positions correspond to the multi-dimensional signal segments after local acquisition; Perform matching extraction on the multi-dimensional signal segments through the local amplitude function to obtain amplitude signal segments; Perform feature integration on the obtained amplitude signal segments based on the order of local acquisition to obtain environmental protection local wave coefficients.

6. An environmental protection information digital management and monitoring system based on artificial intelligence according to claim 1, characterized in that, The process of performing internal construction based on the environmental protection feature display graph to obtain a peak domain display box includes: Upload the environmental protection local wave coefficient to the monitoring domain space, perform position matching on the environmental protection local wave coefficient, and upload the successfully matched environmental protection local wave coefficient to the corresponding virtual monitoring area; Based on the environmental protection feature display chart, mark the intersection points of the local coefficient curve to obtain multi-dimensional feature points, and numerically mark the multi-dimensional feature points to obtain the feature coefficient values; Set the peak domain display frame and upload the obtained peak domain display frame to the environmental protection feature display chart.

7. An environmental protection information digital management and monitoring system based on artificial intelligence according to claim 6, characterized in that The process of obtaining the peak domain feature value includes: Select feature points from the environmental protection feature chart according to the peak domain display frame to obtain the selected matching area, and mark the multi-dimensional feature points in the selected matching area as the selected feature points; Based on the peak domain display frame, perform form extraction on the selected matching area to obtain the peak domain feature value; Set the interaction distance for the obtained peak domain display frame, translate the obtained peak domain display frame based on the interaction distance to reach the next selected matching area, and perform form extraction on the selected matching area through the peak domain display frame to obtain the peak domain feature value until the local coefficient curve is covered, then end the translation of the peak domain display frame.

8. An environmental protection information digital management and monitoring system based on artificial intelligence according to claim 1, characterized in that, The process of obtaining the pollution flow trace by performing trace judgment on the peak domain feature value through the analysis threshold includes: Sort the peak domain feature values to obtain the coefficient feature set; Perform critical judgment on the coefficient feature set according to the analysis threshold to obtain the abnormal judgment coefficient; Sort the abnormal judgment coefficient based on the environmental protection collection time to obtain the coefficient abnormal time series; Based on the monitoring domain space, perform feature marking on the virtual monitoring area according to the obtained coefficient abnormal time series to obtain the monitoring identification area; Perform time comparison on the monitoring identification area based on the environmental protection collection time to obtain the pollution flow trace.

9. The environmental protection information digital management and monitoring method of an environmental protection information digital management and monitoring system based on artificial intelligence according to any one of claims 1 to 8, characterized in that, It includes the following steps: Step 1: Generate a virtual monitoring area and collect multi-dimensional environmental protection information; Step 2: Construct the monitoring domain space, transform the multi-dimensional environmental protection information to obtain multi-dimensional environmental protection signals, extract statistical multi-dimensional coefficients through the multi-dimensional environmental protection signals, set a dynamic amplitude function for dimension expansion to obtain the extended amplitude function; Step 3: Perform local collection on the extended amplitude function according to the amplitude variable to obtain the local amplitude function, evenly extract the multi-dimensional environmental protection signals through the local amplitude function to obtain the environmental protection local wave coefficient, generate the local coefficient curve based on the environmental protection local wave coefficient and construct the environmental protection feature display chart, and perform internal construction based on the environmental protection feature display chart to obtain the peak domain display frame; Step 4: Based on the environmental protection feature display chart, perform partial extraction on the local coefficient curve according to the peak domain display frame to obtain the peak domain feature value, set the analysis threshold, and perform trace judgment on the peak domain feature value through the analysis threshold to obtain the pollution flow trace.

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