An end-to-end urban environment monitoring method based on deep learning

Abstract

The present invention discloses an end-to-end urban environment monitoring method based on deep learning, including data collection, data encoding, a deep learning model of urban environment quality and model training; the present invention belongs to the field of environmental measurement and deep learning technology, and specifically is an end-to-end urban environment monitoring method based on deep learning. The advantages of the present invention are: a multi-source data collection method is used for environmental monitoring, cloud computing is used for data collection and data processing, a deep learning model of urban environment quality is proposed according to the attention mechanism, the data sources are rich, the environmental quality of the city is fully reflected, computing resources are effectively used, and accurate environmental evaluation is given according to the collected environmental data and their interrelationships.

Description

Technical Field

[0001] The present invention belongs to the field of environmental measurement and deep learning technology, and specifically is an end-to-end urban environment monitoring method based on deep learning. Background Art

[0002] Urban environmental monitoring refers to the long-term, systematic and accurate observation, measurement and analysis of various environmental elements in the city (including but not limited to the atmosphere, water, soil, noise, solid waste, etc.) through a series of scientific methods and technical means. Its main purpose is to: understand the quality of the urban environment: including the types, concentrations, distribution and changing trends of pollutants, for example, monitoring the concentrations of PM2.5, PM10 and other particulate matter in the atmosphere to determine whether the air quality meets the standards; discover environmental problems: timely detect environmental pollution incidents, ecological damage and other problems, for example, a sudden increase in the chemical oxygen demand (COD) of a river section may indicate the existence of illegal sewage discharge; serve environmental planning and management: help formulate reasonable urban development plans, environmental protection policies and regulations, for example, based on environmental monitoring data, demarcate the city's ecological protection areas and restricted development areas; protect public health: let the public understand the quality of their environment, take corresponding protective measures, and reduce the harm of environmental pollution to health. For example, when the air quality is poor, remind citizens to reduce outdoor activities or wear protective masks; verify the effectiveness of environmental protection measures: monitor the improvement of environmental quality after the implementation of environmental protection measures to evaluate the effectiveness of the measures. For example, after taking emission reduction measures, compare the sulfur dioxide emission data before and after to determine whether the measures have effectively reduced pollutant emissions.

[0003] Common existing technical methods for urban environment detection:

[0004] 1. Traditional sensor monitoring: Various types of sensors are deployed in various areas of the city, such as air quality sensors (detecting sulfur dioxide, nitrogen oxides, particulate matter, etc.), water quality sensors (monitoring pH, dissolved oxygen, chemical oxygen demand, etc.), noise sensors, etc. These sensors transmit the collected data to the central processing system for analysis and processing.

[0005] 2. Satellite remote sensing technology: Utilize various remote sensing instruments carried by satellites to obtain large-scale images and data of cities, including information such as vegetation coverage, land use, and heat island effect. By analyzing these remote sensing data, the ecological environment of the city can be evaluated.

[0006] 3. Mobile monitoring equipment: such as monitoring vehicles equipped with environmental monitoring instruments, which move around the city to collect data. This method can flexibly monitor different areas and supplement the data of fixed monitoring points. For example, during a specific time period, the monitoring vehicle monitors exhaust emissions on the main traffic arteries of the city.

[0007] 4. Manual sampling and laboratory analysis: Regularly collect samples of air, water, soil, etc. and bring them back to the laboratory for detailed chemical analysis to obtain more accurate environmental parameters.

[0008] 5. Internet of Things technology: Connect multiple sensors into a network to realize automatic data collection, transmission and sharing. It can monitor changes in environmental parameters in real time and issue alarms in time. Summary of the invention

[0009] The present invention proposes an end-to-end urban environment monitoring method based on deep learning to solve the following problems:

[0010] (1) Traditional urban environmental monitoring usually focuses on one aspect, such as air environment monitoring, water environment monitoring, soil environment monitoring, ecological environment monitoring, etc., without comprehensive monitoring of the urban environment, and cannot fully reflect the environmental quality of the city;

[0011] (2) Traditional environmental monitoring uses a single data collection method with a single data source and does not use multi-source data fusion;

[0012] (3) Traditional environmental monitoring systems can only provide independent current environmental quality parameters and cannot provide forecast reference values ​​for a period of time in the future, so they cannot make predictions about environmental quality.

[0013] In order to solve the above problems, the technical solution adopted by the present invention is: an end-to-end urban environment monitoring method based on deep learning, including data acquisition, data encoding, urban environmental quality deep learning model and model training.

[0014] The data collection includes five parts: air quality data collection, water quality data collection, noise data collection, vegetation coverage rate collection, and temperature data collection; the air quality data collection is carried out through multiple air quality sensors, which are arranged in various areas of the city, and the air quality data is uploaded to the cloud through the network. The data collected by the air quality sensors include sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, carbon dioxide concentration, PM2.5 concentration, and PM10 concentration; the water quality data collection is carried out through multiple water quality sensors, which are arranged in various areas of the city (for example: the water inlets and outlets of rivers and lakes, water flow intersections, water functional areas, water inlets of water plants in urban water supply systems, aquaculture areas, groundwater monitoring wells, water inlets and outlets of sewage treatment plants, and industrial wastewater discharge outlets and downstream locations), and the water quality data is uploaded to the cloud through the network. The water quality data includes chemical oxygen demand, pH, Ammonia nitrogen, total phosphine, total nitrogen, pH and dissolved oxygen; the noise data is collected through multiple noise sensors, which are arranged in various areas in the city (for example, along urban road traffic, industrial areas, construction sites, near airports, and at the entrance or surrounding of commercial areas and entertainment venues), and the noise data is uploaded to the cloud through the network, and the noise data includes industrial noise and living noise; the vegetation coverage rate is collected through satellite remote sensing technology, using satellite-borne spectral remote sensing to obtain large-scale satellite images of the city, and by analyzing the reflectivity of different bands in the satellite images, the construction land, green space and water bodies in the city are distinguished, the vegetation coverage rate is calculated, and the vegetation coverage rate is transmitted to the cloud through the satellite-ground network; the temperature data is collected through satellite remote sensing technology, using satellite-borne thermal infrared remote sensing, by detecting the thermal infrared radiation energy emitted by surface objects to obtain large-scale surface temperature data of the city, and the collected temperature data is transmitted to the cloud through the satellite-ground network. The data encoding includes normalizing the data received from the cloud: The process of normalizing the air quality data is: for each type of air quality data, calculate the normalized data of each type of data: A ix =B ix / M ix , i∈[1,7], where for the i-th type of data at position x, B ix is the current measured concentration, M ixis the maximum observed historical concentration; subscript i∈[1,7] represents sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, carbon dioxide concentration, PM2.5 concentration and PM10 concentration from small to large; subscript x represents the position index, x∈[1,X], X is the total number of air quality sensors; the process of normalizing water quality data is: for each type of water quality data, calculate the normalized data of each type of data: W jy =C jy / S jy , j∈[1,6], where for the j-th data at position y, C jy is the current measured concentration, S jy is the historical maximum concentration; subscript j∈[1,6] represents chemical oxygen demand, pH, ammonia nitrogen, total phosphine, total nitrogen, pH and dissolved oxygen from small to large, subscript y represents the position index, y∈[1,Y], Y is the total number of water quality sensors; the process of normalizing the noise data is: A k =D k / N k , where D k is the measured decibel number at position k, N k To set the maximum decibel number, the subscript k represents the position index, k∈[1,K], K is the total number of noise sensors; the temperature data is normalized and the current measured temperature data T m Divide by 100 degrees Celsius to get the normalized temperature value, the T m Represents the temperature at position m, where m is the position index, m∈[1,M], and M is the total number of noise sensors.

[0015] The urban environmental quality deep learning model includes:

[0016] The input of the urban environmental quality deep learning model includes an air quality vector, a water quality vector, a vegetation coverage rate, a noise vector and a temperature vector. The dimension of the air quality vector is X×168, the dimension of the water quality vector is Y×168, the dimension of the vegetation coverage rate is 1×168, the dimension of the noise vector is K×168, and the dimension of the temperature vector is M×168. The input order of the urban environmental quality deep learning model is the air quality vector, the water quality vector, the vegetation coverage rate, the noise vector and the temperature vector, and they are spliced ​​row by row to obtain a fusion matrix, whose input dimension is (X+Y+1+K+M)×168, and is used F express.

[0017] The calculation steps of the urban environmental quality deep learning model include attention calculation, residual calculation and full connection calculation, which are three parts. The steps of attention calculation are:

[0018] S1, randomly generate a trainable weight matrix with a mean of zero and a variance of 1 W k , W v and W q , whose dimensions are all 168×16;

[0019] S2, according to the weight matrix W k , W v , W q and fusion matrix F Generate key matrix K , value matrix V And the query matrix Q , the calculation formula is as follows:

[0020] K=F×W k

[0021] V=F×W v

[0022] Q=F×W q

[0023] In the above formula, the bond matrix K , value matrix V , query matrix Q The dimensions are (X+Y+1+K+M)×16, and the symbol “×” represents the ordinary matrix product;

[0024] S3. Using the key matrix K Value Matrix V , query matrix Q Calculate the fusion matrix F The similarity between each vector of each mode in E Matrix, the formula is as follows:

[0025] ;

[0026] In the above formula, similarity E The dimension of the matrix is ​​16×16; d represents the fusion matrix F The number of columns, here is 168;

[0027] S4, activate the similarity matrix with the relu function, set the similarity E The expanded form is: ;

[0028] Then, similarity E The form after activation by the relu function is recorded as RE , the specific calculation formula is: ;

[0029] In the above formula, relu is the activation function;

[0030] S5. Calculate the self-attention output. The calculation formula is:

[0031] C=V×RE

[0032] In the above formula, C The dimension is (X+Y+1+K+M)×16;

[0033] The steps of residual calculation are:

[0034] B1. Repeat steps S1 to S5 once, where the trainable weight matrix specified in S1 W k , W v and W q The dimensions are all 16×16;

[0035] B2. Repeat steps B1 N times

[0036] B3. Perform a residual connection between the output result of B2 and the output result of the attention calculation, and obtain a residual result after processing by the softmax activation function. The dimension of the residual result is (X+Y+1+K+M)×16.

[0037] The steps of the full connection calculation are:

[0038] C1. Rearrange the residual results to obtain a feature matrix G with a dimension of 4×4×(X+Y+1+K+M), where (X+Y+1+K+M) is the number of channels;

[0039] C2. Perform the maximum pooling convolution operation on the feature matrix G to obtain the feature vector S of (X+Y+1+K+M)×1;

[0040] C3, the feature vector S is fully connected to the neurons, the number of neurons is 10, the activation function used by the neurons is the softmax function, and finally the probability prediction result with a dimension of 10 is output.

[0041] Through the calculation of the urban environmental quality deep learning model, a probability prediction result with a dimension of 10 is obtained. The value of each component of the probability prediction result is between 0 and 1, and the sum of all components is 1. The value of the maximum component in the prediction result is taken as the final urban environmental evaluation result, and the corresponding scoring result category is obtained based on the evaluation result.

[0042] The model training includes the following steps:

[0043] A1. Customized urban environment evaluation level: Based on the collected urban sample data, experts, scholars and professionals will rate the urban environment of the designated city and perform weighted calculation to obtain the environmental evaluation score;

[0044] A2, data set preparation: based on the collected urban sample data and the corresponding environmental evaluation scores, a data set is prepared for model training of the urban environmental quality deep learning model;

[0045] A3. Model training: Input sample data into the urban environmental quality deep learning model, perform directional propagation and iterative calculation based on the difference between model output and sample label. When the accuracy of the consistency between model output and sample label reaches the maximum value, stop training and the model training is completed.

[0046] The present invention provides an end-to-end urban environment monitoring method based on deep learning.

[0047] (1) The present invention overcomes the traditional urban environmental monitoring which usually focuses on one aspect, such as atmospheric environment monitoring, water environment monitoring, ecological environment monitoring, etc. The present invention conducts comprehensive monitoring of the urban environment and comprehensively reflects the environmental quality of the city;

[0048] (2) The present invention uses a multi-source data collection method for environmental monitoring, with abundant data sources. Since cloud computing is used for data collection and data processing, in order to effectively utilize computing resources, larger-scale and more comprehensive data can be introduced into the urban environmental quality deep learning model, thereby obtaining more accurate evaluation results.

[0049] (3) The present invention proposes a deep learning model of urban environmental quality based on the attention mechanism, which can give an accurate environmental evaluation based on the collected environmental data and their inter-correlations;

[0050] (4) The present invention can further expand the scope and accuracy of data collection, for example, by increasing data collection on soil quality, electromagnetic radiation, etc. In terms of models, the deep learning algorithm can be continuously optimized to improve the prediction accuracy and stability of the model. At the same time, by combining big data technology and Internet of Things technology, real-time and dynamic urban environmental monitoring can be achieved, and environmental problems can be discovered in a timely manner and early warnings can be issued. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flowchart of the end-to-end urban environment monitoring method based on deep learning proposed in the present invention;

[0052] Figure 2 This is a structural diagram of the urban environmental quality deep learning model proposed in this invention.

[0053] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0054] The technical scheme in the embodiments of the present scheme will be clearly and completely described below in conjunction with the drawings in the embodiments of the present scheme. Obviously, the described embodiments are only part of the embodiments of the present scheme, not all of the embodiments; based on the embodiments in the present scheme, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present scheme.

[0055] See also Figure 1 ,The present invention proposes an end-to-end urban environment monitoring method based on ,deep learning, including data collection, data encoding, urban environmental quality deep learning ,model and model training.

[0056] The environmental quality of a city is comprehensively evaluated from multiple dimensions through the city's air quality, water quality, noise, vegetation coverage and temperature, so the data collection includes five parts: air quality data collection, water quality data collection, noise data collection, vegetation coverage rate collection and temperature data collection.

[0057] Air quality data collection is carried out through multiple air quality sensors, which are arranged in various areas of the city and upload air quality data to the cloud through the network. The data collected by the air quality sensors include sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, carbon dioxide concentration, PM2.5 concentration and PM10 concentration.

[0058] Water quality data collection is carried out through multiple water quality sensors, which are arranged in various areas within the city, such as: water inlets and outlets of rivers and lakes, water flow intersections, water functional areas, water inlets of water plants in urban water supply systems, aquaculture areas, groundwater monitoring wells, water inlets and outlets of sewage treatment plants, industrial wastewater discharge outlets and downstream locations. And the water quality sensor uploads the collected water quality data to the cloud through the network. The water quality data includes chemical oxygen demand, pH, ammonia nitrogen, total phosphine, total nitrogen, pH and dissolved oxygen.

[0059] Noise data is collected through multiple noise sensors, which are arranged in various areas in the city, such as along urban road traffic, industrial areas, construction sites, near airports, and at the entrances or around commercial areas and entertainment venues. The noise sensors upload the collected noise data to the cloud through the network. The noise data includes industrial noise and life noise.

[0060] The vegetation coverage rate is collected through satellite remote sensing technology. The spectral remote sensing technology carried by satellites is used to obtain large-scale satellite images of the city. By analyzing the reflectivity of different bands in the satellite images, the construction land, green space and water bodies in the city are distinguished, and the vegetation coverage rate is calculated. The vegetation coverage rate is then transmitted to the cloud through the satellite-to-ground network.

[0061] Temperature data collection is also carried out through satellite remote sensing technology. By using the thermal infrared remote sensing carried by satellites, the thermal infrared radiation energy emitted by surface objects is detected to obtain surface temperature data over a large area of ​​the city, and the collected temperature data is transmitted to the cloud through the satellite-to-ground network.

[0062] Through the above process, the cloud obtains air quality data, water quality data, noise data, vegetation coverage and temperature data. The above data comes from multiple data sources and the format is not uniform. In order to facilitate subsequent calculations, the above data is encoded. The data encoding is to normalize the data received by the cloud.

[0063] Air quality data includes multiple categories of data, namely sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, carbon dioxide concentration, PM2.5 concentration and PM10 concentration. For each category of air quality data, the normalized data of each category is calculated separately:

[0064] A ix =B ix / M ix , i∈[1,7]

[0065] Among them, for the i-th type of data at position x, B ix is the current measured concentration, M ix is the maximum observed historical concentration; the subscript i∈[1,7] represents the sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, carbon dioxide concentration, PM2.5 concentration and PM10 concentration from small to large; the subscript x represents the position index, x∈[1,X], X is the total number of air quality sensors.

[0066] Water quality data includes multiple categories of data, namely chemical oxygen demand, pH, ammonia nitrogen, total phosphine, total nitrogen, pH and dissolved oxygen. For each category of water quality data, calculate the normalized data of each category of data:

[0067] W jy =C jy / S jy , j∈[1,6],

[0068] Among them, for the j-th type of data at position y, C jy is the current measured concentration, S jy is the historical maximum concentration; the subscript j∈[1,6] represents chemical oxygen demand, pH, ammonia nitrogen, total phosphine, total nitrogen, pH and dissolved oxygen from small to large, and the subscript y represents the position index, y∈[1,Y], where Y is the total number of water quality sensors.

[0069] The process of normalizing the noisy data is: A k =D k / N k , where D k is the measured decibel number at position k, N k To set the maximum decibel number, the subscript k represents the location index, k∈[1,K], where K is the total number of noise sensors.

[0070] Normalize the temperature data and convert the current measured temperature data T m Divide by 100 degrees Celsius to get the normalized temperature value, the T m Represents the temperature at position m, where m is the position index, m∈[1,M], and M is the total number of noise sensors.

[0071] After the data is normalized, the normalized data is input into the urban environmental quality deep learning model. The urban environmental quality deep learning model is as follows: Figure 2 shown.

[0072] The input of the urban environmental quality deep learning model includes an air quality vector, a water quality vector, a vegetation coverage rate, a noise vector and a temperature vector; the dimension of the air quality vector is X×168, the dimension of the water quality vector is Y×168, the dimension of the vegetation coverage rate is 1×168, the dimension of the noise vector is K×168, and the dimension of the temperature vector is M×168; the input order of the urban environmental quality deep learning model is the air quality vector, the water quality vector, the vegetation coverage rate, the noise vector and the temperature vector, and they are spliced ​​row by row to obtain a fusion matrix, whose input dimension is (X+Y+1+K+M)×168, and is used F express.

[0073] The urban environmental quality deep learning model is calculated based on the above input. The calculation steps include attention calculation, residual calculation and full connection calculation. The steps of attention calculation are:

[0074] S1, randomly generate a trainable weight matrix with a mean of zero and a variance of 1 W k , W v and W q , whose dimensions are all 168×16;

[0075] S2, according to the weight matrix W k , W v , W q and fusion matrix F Generate key matrix K , value matrix V And the query matrix Q , the calculation formula is as follows:

[0076] K=F×W k

[0077] V=F×W v

[0078] Q=F×W q

[0079] In the above formula, the bond matrix K , value matrix V , query matrix Q The dimensions are (X+Y+1+K+M)×16, and the symbol “×” represents the ordinary matrix product;

[0080] S3. Using the key matrix K Value Matrix V , query matrix Q Calculate the fusion matrix F The similarity between each vector of each mode in E Matrix, the formula is as follows:

[0081]

[0082] In the above formula, similarity E The dimension of the matrix is ​​16×16; d represents the fusion matrix F The number of columns, here is 168;

[0083] S4, activate the similarity matrix with the relu function, set the similarity E The expanded form is: ;

[0084] Then, similarity EThe form after activation by the relu function is recorded as RE , the specific calculation formula is: ;

[0085] In the above formula, relu is the activation function;

[0086] S5. Calculate the self-attention output. The calculation formula is:

[0087] C=V×RE

[0088] In the above formula, C The dimension is (X+Y+1+K+M)×16;

[0089] The steps of residual calculation are:

[0090] B1. Repeat steps S1 to S5 once, where the trainable weight matrix specified in S1 W k , W v and W q The dimensions are all 16×16;

[0091] B2. Repeat steps B1 N times

[0092] B3. Perform a residual connection between the output result of B2 and the output result of the attention calculation, and obtain a residual result after processing by the softmax activation function. The dimension of the residual result is (X+Y+1+K+M)×16.

[0093] The steps of the full connection calculation are:

[0094] C1. Rearrange the residual results to obtain a feature matrix G with a dimension of 4×4×(X+Y+1+K+M), where (X+Y+1+K+M) is the number of channels;

[0095] C2. Perform the maximum pooling convolution operation on the feature matrix G to obtain the feature vector S of (X+Y+1+K+M)×1;

[0096] C3, the feature vector S is fully connected to the neurons, the number of neurons is 10, the activation function used by the neurons is the softmax function, and finally the probability prediction result with a dimension of 10 is output.

[0097] The urban environmental quality deep learning model is used to calculate a probability prediction result with a dimension of 10. The value of each component of the probability prediction result is between 0 and 1, and the sum of all components is 1. The value of the maximum component in the prediction result is taken as the final urban environmental evaluation result, and the corresponding scoring result category is obtained based on the evaluation result. For example, the probability prediction result is [0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.05]. Since the maximum component is the 9th value, the corresponding evaluation result is 9.

[0098] Through the above content, a deep learning model of urban environmental quality is built, and the urban environment is comprehensively evaluated in multiple dimensions through the deep learning model of urban environmental quality. In order for the deep learning model of urban environmental quality to give accurate evaluation results, the model needs to be trained.

[0099] Model training includes the following steps:

[0100] A1. Customized urban environment evaluation level: Based on the collected urban sample data, experts, scholars and professionals will rate the urban environment of the designated city and perform weighted calculation to obtain the environmental evaluation score;

[0101] A2, data set preparation: based on the collected urban sample data and the corresponding environmental evaluation scores, a data set is prepared for model training of the urban environmental quality deep learning model;

[0102] A3. Model training: Input sample data into the urban environmental quality deep learning model, perform directional propagation and iterative calculation based on the difference between model output and sample label. When the accuracy of the consistency between model output and sample label reaches the maximum value, stop training and the model training is completed.

[0103] The above is the specific working process of the present invention, and you can repeat this step next time you use it.

[0104] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0105] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0106] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An end-to-end urban environmental monitoring method based on deep learning, including data collection, data encoding, urban environmental quality deep learning model and model training; The data collection includes five parts: air quality data collection, water quality data collection, noise data collection, vegetation coverage rate collection, and temperature data collection; the collected data is transmitted to the cloud through the network; The data encoding includes normalizing the data received from the cloud: The process of normalizing the air quality data is: for each type of air quality data, calculate the normalized data of each type of data: A ix =B ix / M ix ,i∈[1,7], where For the i-th type of data at position x, B ix is the current measured concentration, M ix is the maximum observed historical concentration; subscript i∈[1,7] represents sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, carbon dioxide concentration, PM2.5 concentration and PM10 concentration from small to large; subscript x represents the position index, x∈[1,X], X is the total number of air quality sensors; the process of normalizing water quality data is: for each type of water quality data, calculate the normalized data of each type of data: W jy =C jy / S jy ,j∈[1,6], where for the j-th type of data at position y, C jy is the current measured concentration, S jy is the historical maximum concentration; subscript j∈[1,6] represents chemical oxygen demand, pH, ammonia nitrogen, total phosphine, total nitrogen and dissolved oxygen from small to large, subscript y represents the position index, y∈[1,Y], Y is the total number of water quality sensors; the process of normalizing the noise data is: A k =D k / N k , where D k is the measured decibel number at position k, N k To set the maximum decibel number, the subscript k represents the position index, k∈[1,K], K is the total number of noise sensors; the temperature data is normalized and the current measured temperature data T m Divide by 100 degrees Celsius to get the normalized temperature value, the T m represents the temperature at position m, where m is the position index, m∈[1,M], and M is the total number of noise sensors; The urban environmental quality deep learning model includes: The input of the urban environmental quality deep learning model includes air quality vector, water quality vector, vegetation coverage, noise vector and temperature vector; the input order of the urban environmental quality deep learning model is air quality vector, water quality vector, vegetation coverage, noise vector and temperature vector, and they are spliced ​​row by row to obtain a fusion matrix F ; A probability prediction result with a dimension of 10 is obtained by calculating the urban environmental quality deep learning model. The value of each component of the probability prediction result is between 0 and 1, and the sum of all components is 1. The value of the maximum component in the prediction result is taken as the final urban environmental evaluation result, and the corresponding scoring result category is obtained based on the evaluation result; The model training includes the following steps: A1. Customized urban environment evaluation level: Based on the collected urban sample data, experts, scholars and professionals will rate the urban environment of the designated city and perform weighted calculation to obtain the environmental evaluation score; A2, data set preparation: based on the collected urban sample data and the corresponding environmental evaluation scores, a data set is prepared for model training of the urban environmental quality deep learning model; A3. Model training: input sample data into the urban environmental quality deep learning model, perform directional propagation and iterative calculation based on the difference between model output and sample labels, and stop training when the accuracy rate of consistency between model output and sample labels reaches the maximum value. The calculation steps of the urban environmental quality deep learning model include attention calculation, residual calculation and full connection calculation; The steps of attention calculation are: S1, randomly generate a trainable weight matrix with a mean of zero and a variance of 1 W k , W v and W q , whose dimensions are all 168×16; S2, according to the weight matrix W k , W v and W q And the fusion matrix F generates the key matrix K, value matrix V and query matrix Q. The calculation formula is as follows: K=F× W k V=F× W v Q=F× W q In the above formula, the dimensions of the key matrix K, value matrix V, and query matrix Q are all (X+Y+1+K+M)×16, and the symbol "×" represents the ordinary matrix product; S3. Use the key matrix K, value matrix V, and query matrix Q to calculate the similarity matrix E between each vector of each modality in the fusion matrix F. The formula is as follows: ; In the above formula, similarity E The dimension of the matrix is ​​16×16; d represents the fusion matrix F The number of columns, here is 168; S4, activate the similarity matrix with the relu function, set the similarity E The expanded form is: ; Then, similarity E The form after activation by the relu function is recorded as RE , the specific calculation formula is: ； In the above formula, relu is the activation function; S5. Calculate the self-attention output. The calculation formula is: C = V × RE, In the above formula, C The dimension is (X+Y+1+K+M)×16; The steps of residual calculation are: B1. Repeat steps S1 to S5 once, where the trainable weight matrix specified in S1 W k , W v and W q The dimensions are all 16×16; B2, repeat the steps of B1 N times; B3, perform residual connection on the output result of B2 and the output result of attention calculation, and obtain the residual result after processing by softmax activation function. The dimension of the residual result is (X+Y+1+K+M)×16; The steps of the full connection calculation are: C1, rearrange the residual results to obtain a feature matrix G with a dimension of 4×4×(X+Y+1+K+M), where (X+Y+1+K+M) is the number of channels; C2. Perform the maximum pooling convolution operation on the feature matrix G to obtain the feature vector S of (X+Y+1+K+M)×1; C3, the feature vector S is fully connected to the neurons, the number of neurons is 10, the activation function used by the neurons is the softmax function, and finally the probability prediction result with a dimension of 10 is output.

2. The end-to-end urban environment monitoring method based on deep learning according to claim 1 is characterized in that: The air quality data is collected through a plurality of air quality sensors, which are arranged in various areas of the city, and the air quality data is uploaded to the cloud through the network. The data collected by the air quality sensors include sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, carbon dioxide concentration, PM2.5 concentration and PM10 concentration; the water quality data is collected through a plurality of water quality sensors, which are arranged in various areas of the city, and the water quality data is uploaded to the cloud through the network. The water quality data includes chemical oxygen demand, pH, ammonia nitrogen, total phosphine, total nitrogen, pH and dissolved oxygen; The noise data is collected by multiple noise sensors, which are arranged in various areas of the city and upload the noise data to the cloud through the network. The noise data includes industrial noise and living noise. The vegetation coverage rate is collected through satellite remote sensing technology, using spectral remote sensing carried by satellites to obtain large-scale satellite images of the city. By analyzing the reflectivity of different bands in the satellite images, the construction land, green space and water bodies in the city are distinguished, the vegetation coverage rate is calculated, and the vegetation coverage rate is transmitted to the cloud through the satellite-to-ground network; the temperature data is collected through satellite remote sensing technology, using thermal infrared remote sensing carried by satellites, and the surface temperature data of a large area of ​​the city is obtained by detecting the thermal infrared radiation energy emitted by surface objects, and the collected temperature data is transmitted to the cloud through the satellite-to-ground network.

3. The end-to-end urban environment monitoring method based on deep learning according to claim 1 is characterized in that: The dimension of the air quality vector is X×168, the dimension of the water quality vector is Y×168, the dimension of the vegetation coverage rate is 1×168, the dimension of the noise vector is K×168, and the dimension of the temperature vector is M×168; the input dimension of the fusion matrix is ​​(X+Y+1+K+M)×168.

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