A long-time industrial agglomeration pollution risk intelligent analysis method and system

Abstract

The application provides a long-time industrial agglomeration area pollution risk intelligent analysis method and system, belongs to the field of pollution risk management, and is used for solving the problem that the pollution risk of the industrial agglomeration area is difficult to dynamically and accurately analyze in the related art.In the method and the system, the long-time environmental monitoring data and remote sensing image data can be gradually processed and analyzed based on a self-developed intelligent algorithm model, and the pollution risk data of the industrial agglomeration area can be determined, so that the pollution risk in the industrial agglomeration area is dynamically and accurately monitored, and the pollution risk in the industrial agglomeration area is better managed.

Description

Technical Field

[0001] The present application relates to the field of pollution risk management, and in particular to a method and system for intelligent analysis of pollution risks in industrial clusters over a long time period. Background Art

[0002] With the acceleration of global industrialization, the petrochemical industry, a key economic pillar, is facing increasingly prominent environmental risks in its clusters. Petrochemical clusters typically encompass multiple upstream and downstream industries, involving complex production processes and extensive raw material storage, processing, and waste disposal. These characteristics increase the risk of soil and groundwater contamination, posing a serious threat to the surrounding environment and human health, particularly in the event of an accident or leak. Therefore, timely and accurate identification and assessment of high-risk areas and the development of appropriate emergency management measures are crucial for environmental protection and public safety.

[0003] Traditional environmental monitoring methods rely heavily on on-site sampling and laboratory analysis, which are costly, time-consuming, and inadequate for comprehensively reflecting regional risk profiles. In recent years, the rapid development of remote sensing technology has provided new tools for environmental monitoring. By acquiring high-resolution remote sensing imagery, large areas can be monitored quickly and effectively. However, the application of existing remote sensing technology in petrochemical clusters still has certain limitations, particularly in terms of the accuracy of dynamic change monitoring and risk assessment. Summary of the Invention

[0004] The present application provides a long-term intelligent analysis method and system for pollution risks in industrial agglomeration areas, which can dynamically and accurately analyze the pollution risks in industrial agglomeration areas, and is conducive to better management of pollution risks in industrial agglomeration areas.

[0005] In the first aspect, the present application provides a method for intelligently analyzing pollution risks in industrial clusters over a long time period. The method comprises:

[0006] Obtain long-term environmental monitoring data and remote sensing image data for each divided area within the industrial agglomeration area;

[0007] Analyzing the remote sensing image data to obtain industrial characteristic data;

[0008] Determine the transient pollution risk of each divided area in a long time series based on the environmental monitoring data and industrial characteristic data;

[0009] Analyze long-term environmental monitoring data, industrial characteristic data, and transient pollution risks to obtain environmental change data, characteristic change data, and risk change data;

[0010] fusing the environmental monitoring data and the industrial characteristic data to obtain fused characteristic data;

[0011] The environmental monitoring data, industrial characteristic data, transient pollution risk, environmental change data, characteristic change data, risk change data and fusion characteristic data are input into a pre-acquired risk analysis model to obtain pollution risk data of industrial clusters.

[0012] By adopting the above technical solutions, it is possible to determine the pollution risk data of industrial agglomeration areas based on the gradual processing and analysis of long-term environmental monitoring data and remote sensing image data, thereby realizing dynamic and accurate monitoring of pollution risks in industrial agglomeration areas, which is conducive to better management of pollution risks in industrial agglomeration areas.

[0013] Furthermore, determining the transient pollution risk of each divided area in a long time series according to the environmental monitoring data and the industrial characteristic data includes:

[0014] Determining current concentration data of each pollutant in each environmental medium based on the environmental monitoring data;

[0015] Determining industrial pollution risks and the pollution concentration impact of each pollutant in each environmental medium based on the industrial characteristic data;

[0016] Determining the associated pollution sub-risk of each pollutant in each environmental medium based on the current concentration data, the pollution concentration impact, and a preset pollutant concentration threshold for each pollutant in each environmental medium;

[0017] The transient pollution risk is determined by combining the industrial pollution risk and the associated pollution sub-risks.

[0018] Furthermore, determining the industrial pollution risk and the pollution concentration impact of each pollutant in each environmental medium based on the industrial characteristic data includes:

[0019] Identifying the industrial characteristics carried by the industrial characteristic data;

[0020] Determining the industrial pollution sub-risk for each industrial characteristic and the pollution concentration sub-impact for each pollutant in each environmental medium based on a pre-acquired industrial characteristic impact table, wherein the industrial characteristic impact table includes a mapping relationship between industrial characteristics and industrial pollution sub-risks, and a mapping relationship between industrial characteristics, pollutants, and environmental media and pollutant concentration sub-impacts;

[0021] The sum of all industrial pollution sub-risks is determined to be the industrial pollution risk, and the sum of all pollution concentration sub-impacts of pollutants in environmental media is determined to be the pollution concentration impact.

[0022] Furthermore, determining the associated pollution sub-risk of each pollutant in each environmental medium based on the current concentration data, the pollution concentration impact, and a preset pollutant concentration threshold for each pollutant for each environmental medium includes:

[0023] Let the current concentration data of the ith pollutant in the jth environmental medium be C dij , the pollutant concentration threshold be C pij , the pollution concentration influence be v ij , the associated pollutant risk be Z ij , then

[0024] Further, the combination of the industrial pollution risk and the associated pollutant risk to determine the transient pollution risk comprises:

[0025] Let the associated pollutant risk of the ith pollutant in the jth environmental medium be Z ij , the industrial pollution risk be Z g , and the transient pollution risk be Z s , then

[0026] Z s = 1-e -x

[0027]

[0028] In the formula, w g is a preset industrial pollution weight, w ij is a relative preset associated pollution weight of the ith pollutant in the jth environmental medium, A is a preset constant and A ≥ max(w g Z g , w ij Z ij ).

[0029] Further, the fusion of the environmental monitoring data and the industrial feature data to obtain the fusion feature data comprises:

[0030] The environmental monitoring data and the industrial feature data are both constructed into high-dimensional vectors to obtain an environmental monitoring vector and an industrial feature vector.

[0031] The environmental monitoring vector and the industrial feature vector are fused to obtain the fusion feature data, and the dimension of the fusion feature data is not higher than the maximum of the dimensions of the environmental monitoring vector and the industrial feature vector.

[0032] Further, the risk analysis model comprises a long short-term memory network submodel, a time series feature analysis submodel, and a neural network submodel.

[0033] The long short-term memory network sub-model is used to analyze long-term environmental monitoring data, industrial characteristic data, transient pollution risk, environmental change data, characteristic change data and risk change data respectively, and obtain environmental monitoring time series characteristics, industrial characteristic time series characteristics, pollution risk time series characteristics, environmental change time series characteristics, characteristic change time series characteristics and risk change time series characteristics respectively;

[0034] The time series feature analysis sub-model is used to analyze the time series feature prominence of environmental monitoring time series features, industrial feature time series features, pollution risk time series features, environmental change time series features, feature change time series features, and risk change time series features, and divides environmental monitoring data, industrial feature data, transient pollution risk, environmental change data, feature change data, and risk change data into time series dependent data and non-time series dependent data based on the time series feature prominence;

[0035] The neural network sub-model is used to input the time series features of time series dependent data and fuse feature data and non-time series dependent data to output the pollution risk data.

[0036] Furthermore, the time series feature analysis sub-model includes:

[0037] According to the ratio of the vector modulus of the time series feature to the square of the vector dimension number, the time series feature significance of the corresponding time series feature is determined. The time series feature with a significance higher than that of at least three other time series features or higher than a preset feature significance threshold is determined to be a significant time series feature. The data with significant time series features in the environmental monitoring data, industrial feature data, transient pollution risks, environmental change data, feature change data and risk change data are determined to be time series dependent data, and the others are non-time series dependent data.

[0038] In a second aspect, the present application provides a long-term intelligent analysis system for pollution risks in industrial clusters, wherein the system applies any of the methods described in the first aspect above.

[0039] In summary, this application has at least the following beneficial effects:

[0040] A method and system for intelligent analysis of pollution risks in industrial agglomeration areas with a long time series is provided, which can gradually and intelligently analyze long-term environmental monitoring data and remote sensing image data, thereby realizing dynamic and accurate monitoring of pollution risks in industrial agglomeration areas.

[0041] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0043] Figure 1 A flow chart of a method for intelligent analysis of pollution risks in industrial clusters over a long time period in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0044] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0045] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0046] The present application provides a long-term intelligent analysis method and system for pollution risks in industrial agglomeration areas, which can dynamically and accurately analyze the pollution risks in industrial agglomeration areas, and is conducive to better management of pollution risks in industrial agglomeration areas.

[0047] In a first aspect, embodiments of the present application disclose a method for intelligently analyzing pollution risks in industrial clusters over a long time series, which can be executed by a server.

[0048] Figure 1 A flow chart of a method for intelligent analysis of pollution risks in industrial clusters over a long time period in an embodiment of the present application is shown.

[0049] Reference Figure 1 , the method specifically comprises the following steps:

[0050] S110: Obtain long-term environmental monitoring data and remote sensing image data for each divided area in the industrial cluster.

[0051] Environmental monitoring data and remote sensing image data are collected by environmental sensors and remote sensing systems connected to the server, respectively. Environmental monitoring data is used to reflect the real-time pollution situation in industrial clusters. Specifically, it can include soil pollutant concentrations of various pollutants, groundwater pollutant concentrations, air pollution data, surface water quality monitoring data, etc., with timestamps. Environmental monitoring data can undergo preprocessing such as data cleaning and noise reduction to ensure its quality. Remote sensing image data is used to reflect the important equipment and their evolution within industrial clusters. It carries timestamps and can undergo preprocessing such as radiometric correction, atmospheric correction, geometric correction, and data denoising to ensure the clarity of the information contained in the remote sensing image data.

[0052] S120: Analyze the remote sensing image data to obtain industrial feature data.

[0053] In the method of this step, the industrial feature data includes industrial features.

[0054] The method of this step can be implemented specifically based on a convolutional neural network model, specifically relying on the convolutional neural network model and a pre-trained data set to identify the types of production facilities, production link information, etc. in the remote sensing image data. The industrial features in the industrial feature data reflect the types of production facilities, production link information, etc. contained in the remote sensing image data one by one. Each different type of production facility, production link information, etc. corresponds to a special industrial feature. The industrial feature data composed of all industrial features is used to reflect the overall situation of the production facility types, production link information, etc. of the industrial agglomeration area.

[0055] In the method of this step, the industrial feature data carries a timestamp, that is, the long-term industrial feature data is obtained based on the long-term remote sensing image data.

[0056] S130: Determine the long-term transient pollution risk of each divided area according to the environmental monitoring data and the industrial characteristic data.

[0057] The method of this step specifically includes: determining the current concentration data of each pollutant in each environmental medium based on the environmental monitoring data; determining the industrial pollution risk and the pollution concentration impact of each pollutant in each environmental medium based on the industrial characteristic data; determining the associated pollution sub-risk of each pollutant in each environmental medium based on the current concentration data, the pollution concentration impact and the pollutant concentration threshold preset for each pollutant for each environmental medium; and determining the transient pollution risk in combination with the industrial pollution risk and the associated pollution sub-risk.

[0058] The environmental monitoring data carries a timestamp. Based on the content of the environmental monitoring data, the environmental monitoring data can directly reflect the current concentration data of each pollutant in each environmental medium corresponding to the timestamp at the current moment.

[0059] In the method of the present step, the determining of the industrial pollution risk and the pollution concentration influence of each pollutant in each environmental medium based on the industrial characteristic data comprises: identifying the industrial characteristics carried by the industrial characteristic data; determining the industrial pollution sub-risk of each industrial characteristic and the pollution concentration sub-influence of each pollutant in each environmental medium based on the pre-acquired industrial characteristic influence table, wherein the industrial characteristic influence table comprises the corresponding relationship between the industrial characteristics and the industrial pollution sub-risk, and the corresponding relationship between the industrial characteristics, the pollutants and the environmental media and the pollution concentration sub-influence; determining the sum of all the industrial pollution sub-risks as the industrial pollution risk, and determining the sum of all the pollution concentration sub-influences of the pollutants in the environmental media as the pollution concentration influence.

[0060] Specifically, when the industrial characteristics contained in the industrial characteristic data are clear and the industrial characteristic influence table is clear, the industrial characteristics can be substituted into the industrial characteristic influence table to determine the industrial pollution sub-risk, and the industrial characteristic influence table can be substituted into the industrial characteristic influence table to determine the pollution concentration sub-influence of each pollutant in each environmental medium, wherein the obtained industrial pollution sub-risk and the pollution concentration sub-influence are both quantitative values. Therefore, the sum of the industrial pollution sub-risks can be calculated as the quantitative industrial pollution risk, and the pollution concentration influence can be determined based on all the pollution concentration sub-influences of each pollutant in each environmental medium determined based on the industrial characteristic data, and the pollution concentration influence is determined independently for each pollutant in each environmental medium.

[0061] In the method of the present step, the determining of the associated pollution sub-risk of each pollutant in each environmental medium based on the current concentration data, the pollution concentration influence and the preset pollution concentration threshold of each pollutant in each environmental medium comprises: setting the current concentration data of the ith pollutant in the jth environmental medium as C dij , the pollution concentration threshold as C pij , the pollution concentration influence as v ij , and the associated pollution sub-risk as Z ij , then wherein the pollution concentration threshold is a preset constant.

[0062] In the method of the present step, the determining of the transient pollution risk based on the industrial pollution risk and the associated pollution sub-risk comprises: setting the associated pollution sub-risk of the ith pollutant in the jth environmental medium as Z ij , the industrial pollution risk as Z g , and the transient pollution risk as Z s , then

[0063] Z s = 1-e -x

[0064]

[0065] in the formula, w g is a preset industrial pollution weight, w ij is a preset correlation pollution weight of the i-th pollutant in the j-th environmental medium, A is a preset constant and A≥max(w g Z g , w ij Z ij ).

[0066] S140: analyzing the long-time environmental monitoring data, the industrial feature data and the transient pollution risk to obtain environmental change data, feature change data and risk change data.

[0067] In the method of this step, the environmental change data is the change amount of the environmental monitoring data of adjacent time stamps, which is generally the quantitative value of the multi-dimensional environmental monitoring data of the latter minus the quantitative value of the environmental monitoring data of the former.

[0068] The feature change data reflects the change of the industrial feature data of adjacent time stamps. This process needs to quantitatively encode the industrial feature data first, for example, the production facility type is assigned different values according to its potential pollution emission intensity and other factors (such as high-pollution facilities coded as 3, medium-pollution as 2, and low-pollution as 1). Let the industrial feature data matrix be I, the dimension be n t ×n i (n i is the number of industrial features), and the encoded data can be used to calculate the feature change data. The algorithm is similar to that of the environmental change data, and the corresponding values are calculated by subtracting the former from the latter according to the adjacent time stamps.

[0069] The risk change data reflects the change of the transient pollution risk of adjacent time stamps, which can be obtained by subtracting the transient pollution risk of the former from the transient pollution risk of the latter.

[0070] S150: fusing the environmental monitoring data and the industrial feature data to obtain fusion feature data.

[0071] In one example, the method of this step includes: constructing the environmental monitoring data and the industrial feature data into high-dimensional vectors to obtain environmental monitoring vectors and industrial feature vectors; and fusing the environmental monitoring vectors and the industrial feature vectors to obtain the fusion feature data, the dimension of the fusion feature data being not higher than the maximum of the dimensions of the environmental monitoring vectors and the industrial feature vectors.

[0072] For the fusion of the environmental monitoring vector and the industrial feature vector, a padding method (filling the lower-dimensional vector with elements such as 0 to the higher dimension, and then calculating the sum of the two vectors), a repetition method (repeating the elements in the lower-dimensional vector until it expands to the higher dimension), or a projection method, etc. can be used. The specific description of the projection method includes: assuming that the dimension of vector A is m, and the dimension of vector B is n (m > n). First, principal component analysis (PCA) is performed on vector A, and the first n principal components are extracted to obtain a new vector A', which has the same dimension as vector B. Let A be in the form of a matrix X (m x p, p is the sample number), and its covariance matrix is The eigenvalues and eigenvectors of C are calculated, sorted according to the size of the eigenvalues, and the first n eigenvectors are selected to form a projection matrix P (m x n). Then A' = XP. Let the weight be w A and w B The element calculation formula of the fused vector C is: c i = w A x a i + w B x b i , where i = 1, 2,..., n. For example, if w A = 0.6 and w B = 0.4, then c1 = 0.6 x a1 + 0.4 x b1, c2 = 0.6 x a2 + 0.4 x b2, etc.

[0073] Based on the method of this step, the environmental monitoring data and the industrial feature data can be fused to obtain fused feature data, which is in the form of a multi-dimensional vector.

[0074] S160: input the environmental monitoring data, industrial feature data, transient pollution risk, environmental change data, feature change data, risk change data, and fused feature data into the pre-obtained risk analysis model to obtain pollution risk data of the industrial agglomeration area.

[0075] In the method of this step, the risk analysis model includes a long short-term memory network sub-model, a time series feature analysis sub-model, and a neural network sub-model.

[0076] The long short-term memory network sub-model is used to analyze the long time series of environmental monitoring data, industrial feature data, transient pollution risk, environmental change data, feature change data, and risk change data, respectively, to obtain environmental monitoring time series features, industrial feature time series features, pollution risk time series features, environmental change time series features, feature change time series features, and risk change time series features, respectively.

[0077] The LSTM sub-model, i.e., a long short-term memory network, can be used to input various data carrying timestamps and output time sequence feature results, which can be represented as predicted values of next timestamps or predicted change speeds, etc. The principle of the LSTM sub-model is introduced below.

[0078] The LSTM specifically includes an input gate, a forget gate, an output gate and a candidate memory unit. The calculation principles of the input gate, the forget gate, the output gate and the candidate memory unit are described below (taking a timestamp t as an example).

[0079] Input gate:

[0080] i t =σ(W xi x t +W hi h t-1 +b i )

[0081] wherein W xi is a weight matrix of the input data x t to the input gate, with a dimension of n h ×n x (n h is the number of LSTM hidden units), W hi is a weight matrix of the previous hidden state h t-1 to the input gate, with a dimension of n h ×n h , b i is a bias vector of the input gate, with a dimension of n h , and σ is a sigmoid function.

[0082] Forget gate:

[0083] f t =σ(W xf x t +W hf h t-1 +b f )

[0084] wherein W xf , W hf and b f are similar to the corresponding parameters in the input gate, and are respectively a weight matrix and a bias vector related to the forget gate.

[0085] Output gate:

[0086] o t =σ(W xo x t +W ho h t-1 +b o )

[0087] Where W xo 、W ho and b o Parameters related to the output gate.

[0088] Candidate memory cells:

[0089]

[0090] Where W xc is the input data x t To the weight matrix of the candidate memory unit, dimension is n h ×n x , W hc is the previous hidden state H t-1 To the weight matrix of the candidate memory unit, dimension is n H ×n H ,b c is the bias vector of the candidate memory unit, with dimension n h ,tanh is the hyperbolic tangent function.

[0091] Memory cell update and hidden state update

[0092] Memory unit update:

[0093]

[0094] in Represents element-wise multiplication, c t-1 is the previous memory unit state.

[0095] Hide status update:

[0096]

[0097] Of course, the above content is only a brief introduction to the LSTM principle. The basic means is to input time series data (whether single-dimensional or multi-dimensional data) into LSTM and extract the time series features (such as predicted values, time series change trend values, etc.). This application does not improve the internal principles of the LSTM model, so it will not be elaborated.

[0098] In the method of this step, the time series feature analysis sub-model is used to analyze the time series feature visibility of environmental monitoring time series features, industrial feature time series features, pollution risk time series features, environmental change time series features, feature change time series features and risk change time series features, and divides the environmental monitoring data, industrial feature data, transient pollution risk, environmental change data, feature change data and risk change data into time series dependent data and non-time series dependent data based on the time series feature visibility.

[0099] The time series feature analysis sub-model is an improved model of the embodiment of the present application. The time series feature analysis sub-model includes: according to the ratio of the vector modulus of the time series feature to the square of the vector dimension number as the time series feature significance of the corresponding time series feature, determining that the time series feature with a significance higher than that of at least three other time series features or higher than a preset feature significance threshold is an obvious time series feature, and determining that the data with obvious time series features in the environmental monitoring data, industrial feature data, transient pollution risk, environmental change data, feature change data and risk change data is time series dependent data, and the others are non-time series dependent data.

[0100] Specifically, the time series features include environmental monitoring time series features, industrial feature time series features, pollution risk time series features, environmental change time series features, feature change time series features and risk change time series features, which correspond to the six feature data of environmental monitoring data, industrial feature data, transient pollution risk, environmental change data, feature change data and risk change data respectively. Each time series feature is a multidimensional vector. The ratio of the vector modulus of each vector to the square of the vector dimension is calculated to obtain the time series feature significance. The time series feature significance is a quantitative value. The time series features are sorted from large to small according to the time series feature significance. The first three are taken as obvious time series features, and the last three that are higher than the preset feature significance threshold are also determined to be obvious time series features. Therefore, the six feature data can be divided into two categories according to the time series features: time series dependent data and non-time series dependent data. That is, the feature data with obvious time series features are time series dependent data, and the others are non-time series dependent data.

[0101] In this step, the neural network sub-model inputs the time series features of time-dependent data, as well as fused feature data and non-time-dependent data, to output the pollution risk data. The specific principle here is to first input the fused feature data, and then, for each of the six feature data, input the non-time-dependent data itself. Instead of inputting the time series features of the time-dependent data itself, input its time series features. The final output is quantified pollution risk data.

[0102] In an embodiment of the present application, the neural network sub-model includes an input layer, multiple hidden layers and an output layer, wherein the input layer includes multiple input nodes, which are used to input the time series features of time series dependent data and fusion feature data and non-time series dependent data respectively; the output layer includes an output node, which is used to output quantified pollution risk data.

[0103] The specific calculation principle of the neural network sub-model is not improved in the embodiments of this application, so only the following exemplary description is provided. The following is a detailed construction and calculation formula of a neural network with multiple input nodes, one output node, multiple hidden layers, and each hidden layer containing multiple neurons.

[0104] Input layer: Assume that the input layer has n nodes and the input vector is represented as x = [x1, x2, ..., x n ].

[0105] Hidden layer: Assume there are L hidden layers, and the lth hidden layer has m l neurons, where l = 1, 2, …, L.

[0106] Output layer: has 1 output node.

[0107] Input layer to the first hidden layer: Let the weight matrix from the input layer to the first hidden layer be W1, with a dimension of m1×n, and the bias vector be b1, with a dimension of m1. For the input vector x, the input z1 of the first hidden layer is calculated as:

[0108] z1=W1x+b1

[0109] The activation function of the first hidden layer uses the ReLU function (other activation functions such as Tanh or Sigmoid can also be used) to obtain the output a1 of the first hidden layer:

[0110] a1=Re LU(z1)=max(0,z1)

[0111] Hidden layer to hidden layer: For the lth hidden layer (l = 2, 3, ..., L), let the weight matrix from the l-1th hidden layer to the lth hidden layer be W l , dimension is m l ×m l-1 , the bias vector is b l , dimension is m l The input z of the lth hidden layer l Calculated as:

[0112] z l =W l a l-1 +b l

[0113] The output of the lth hidden layer a l Processed by activation function:

[0114] a l =ReLU(z l )=max(0,z l )

[0115] From the last hidden layer to the output layer: Let the weight matrix from the last hidden layer (the Lth hidden layer) to the output layer be W L+1 , with dimensions of 1×m L , the bias vector is b L+1 , dimension is 1. The input z of the output layer L+1 Calculated as:

[0116] z L+1 = W L+1 a L + b L+1

[0117] The output y of the output layer uses the Sigmoid function (if it is a regression task, it can not use the activation function, if it is a multi-classification task, it can use the Softmax function):

[0118]

[0119] Regarding the loss function, where the binary classification task: use the cross-entropy loss function. Let y be the predicted output of the network, y true is the true label (0 or 1), and the loss function L is:

[0120] L = -[y true log(y) + (1-y true ) log(1-y)]

[0121] Where the regression task: use the mean square error loss function. Let y be the predicted output of the network, y true is the true value, and the loss function L is:

[0122]

[0123] Regarding back propagation, the gradient calculation of the output layer includes, using the binary classification task: according to the chain rule, the derivative of the cross-entropy loss function L with respect to z L+1 :

[0124]

[0125] Using the regression task: the derivative of the mean square error loss function L with respect to z L+1 :

[0126]

[0127] And the gradient calculation of W L+1 and b L+1 :

[0128]

[0129] Regarding the gradient calculation of the hidden layer (from back to front): for the Lth hidden layer, the gradient calculation of a L :

[0130]

[0131] The gradient of z L , since aL =ReLU(z L ), when z L >0, When z L When ≤0,

[0132] so:

[0133]

[0134] To W L and b L Gradient calculation of ,

[0135]

[0136] For the lth hidden layer (l=L-1,L-2,…,1), and so on, calculate the value of a l Gradient:

[0137]

[0138] z l Gradient:

[0139] To W l and b l Gradient calculation of :

[0140]

[0141] Regarding parameter update, we can use the gradient descent algorithm to update the parameters. Let the learning rate be α and update W. L+1 :

[0142]

[0143] Update b L+1 :

[0144]

[0145] Update W l (l=1,2,…,L):

[0146]

[0147] Update b l (l=1,2,…,L):

[0148]

[0149] Based on the above content, a neural network sub-model can be constructed to determine the pollution risk data of industrial agglomeration areas based on all input data.

[0150] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to the embodiments of this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for this application.

[0151] In a second aspect, embodiments of the present application disclose a long-term intelligent analysis system for pollution risks in industrial clusters, wherein the system applies any of the methods disclosed in the first aspect above.

[0152] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0153] In summary, this application has at least the following beneficial effects:

[0154] A method and system for intelligent analysis of pollution risks in industrial agglomeration areas with a long time series is provided, which can gradually and intelligently analyze long-term environmental monitoring data and remote sensing image data, thereby realizing dynamic and accurate monitoring of pollution risks in industrial agglomeration areas.

[0155] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A long-term intelligent analysis method for pollution risk in industrial agglomeration areas, characterized by: include: Obtain long-term environmental monitoring data and remote sensing image data for each divided area within the industrial agglomeration area; Analyzing the remote sensing image data to obtain industrial characteristic data; Determine the transient pollution risk of each divided area in a long time series based on the environmental monitoring data and industrial characteristic data; Analyze long-term environmental monitoring data, industrial characteristic data, and transient pollution risks to obtain environmental change data, characteristic change data, and risk change data; fusing the environmental monitoring data and the industrial characteristic data to obtain fused characteristic data; Inputting the environmental monitoring data, industrial characteristic data, transient pollution risk, environmental change data, characteristic change data, risk change data and fused characteristic data into a pre-acquired risk analysis model to obtain pollution risk data for industrial clusters; Determining the transient pollution risk of each divided area in a long time series according to the environmental monitoring data and the industrial characteristic data includes: Determining current concentration data of each pollutant in each environmental medium based on the environmental monitoring data; Determining industrial pollution risks and the pollution concentration impact of each pollutant in each environmental medium based on the industrial characteristic data; Determining the associated pollution sub-risk of each pollutant in each environmental medium based on the current concentration data, the pollution concentration impact, and a preset pollutant concentration threshold for each pollutant in each environmental medium; Determining the transient pollution risk by combining the industrial pollution risk and the associated pollution sub-risks; Determining the industrial pollution risk and the pollution concentration impact of each pollutant in each environmental medium based on the industrial characteristic data includes: Identifying the industrial characteristics carried by the industrial characteristic data; Determining the industrial pollution sub-risk for each industrial characteristic and the pollution concentration sub-impact for each pollutant in each environmental medium based on a pre-acquired industrial characteristic impact table, wherein the industrial characteristic impact table includes a mapping relationship between industrial characteristics and industrial pollution sub-risks, and a mapping relationship between industrial characteristics, pollutants, and environmental media and pollutant concentration sub-impacts; Determine the sum of all industrial pollution sub-risks as the industrial pollution risk, and determine the sum of all pollution concentration sub-impacts of pollutants in environmental media as the pollution concentration impact; The determining of the associated pollution sub-risk of each pollutant in each environmental medium based on the current concentration data, the pollution concentration impact, and the pollutant concentration threshold preset for each pollutant in each environmental medium includes: Assume that the current concentration data of the i-th pollutant in the j-th environmental medium is C dij , the pollutant concentration threshold is C pij , the pollution concentration impact is v ij , the associated pollution sub-risk is Z ij ,but 2. The method according to claim 1, characterized in that Determining the transient pollution risk by combining the industrial pollution risk and the associated pollution sub-risks includes: Assume that the associated pollution risk of the i-th pollutant in the j-th environmental medium is Z ij , industrial pollution risk Z g , the transient pollution risk is Z s ,but With s =1-e -x Where w g is the preset industrial pollution weight, w ij is the preset associated pollution weight relative to the i-th pollutant in the j-th environmental medium, A is a preset constant and A≥max(w g Z g ,w ij Z ij ).

3. The method according to claim 1, characterized in that The fusing of the environmental monitoring data and the industrial characteristic data to obtain the fused characteristic data includes: Constructing the environmental monitoring data and the industrial characteristic data into high-dimensional vectors to obtain an environmental monitoring vector and an industrial characteristic vector; The environmental monitoring vector and the industrial feature vector are fused to obtain the fused feature data, wherein the dimension of the fused feature data is not higher than the maximum value of the dimensions of the environmental monitoring vector and the industrial feature vector.

4. The method according to claim 1, wherein The risk analysis model includes a long short-term memory network sub-model, a time series feature analysis sub-model and a neural network sub-model; The long short-term memory network sub-model is used to analyze long-term environmental monitoring data, industrial characteristic data, transient pollution risk, environmental change data, characteristic change data and risk change data respectively, and obtain environmental monitoring time series characteristics, industrial characteristic time series characteristics, pollution risk time series characteristics, environmental change time series characteristics, characteristic change time series characteristics and risk change time series characteristics respectively; The time series feature analysis sub-model is used to analyze the time series feature prominence of environmental monitoring time series features, industrial feature time series features, pollution risk time series features, environmental change time series features, feature change time series features, and risk change time series features, and divides environmental monitoring data, industrial feature data, transient pollution risk, environmental change data, feature change data, and risk change data into time series dependent data and non-time series dependent data based on the time series feature prominence; The neural network sub-model is used to input the time series features of time series dependent data and fuse feature data and non-time series dependent data to output the pollution risk data.

5. The method according to claim 4, characterized in that The timing feature analysis sub-model includes: According to the ratio of the vector modulus of the time series feature to the square of the vector dimension number, the time series feature significance of the corresponding time series feature is determined. The time series feature with a significance higher than that of at least three other time series features or higher than a preset feature significance threshold is determined to be a significant time series feature. The data with significant time series features in the environmental monitoring data, industrial feature data, transient pollution risks, environmental change data, feature change data and risk change data are determined to be time series dependent data, and the others are non-time series dependent data.

6. A long-term intelligent analysis system for pollution risk in industrial clusters, characterized by: Apply the method according to any one of claims 1 to 5.

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