A port environmental risk monitoring and early warning method and system

Abstract

The present invention relates to the technical field of environmental safety early warning, and in particular to a port environmental risk monitoring and early warning method and system. When performing safety early warning on the port environment, the present invention classifies prediction indicators into static indicators and non-static indicators. In the subsequent indicator utilization process, the time attributes of the non-static indicators are reflected to improve the accuracy of port environmental risk monitoring and early warning. The present invention weights the time attributes of the non-static indicators based on a continuous time function, and considers the influence of the monitoring value on the weight in the weighting process, thereby effectively improving the accuracy of the weighting of the non-static indicators at different times.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental safety early warning, and in particular to a port environmental risk monitoring and early warning method and system. Background Art

[0002] Dangerous goods container yards at ports are one of the main environmental safety issues and problems faced by the transportation industry. In order to effectively prevent and minimize the impact of accidents at dangerous goods container yards on the water environment and water safety, this paper studies the identification of water environment risks at dangerous goods container yards at ports, establishes a water environment risk identification list, reveals the impact mechanism, and develops an environmental monitoring and emergency intelligent management and control platform, which can provide technical support for environmental impact assessments and emergency plans for dangerous goods container yard projects, as well as environmental protection design and enterprise water pollution risk emergency management.

[0003] In the existing technology, early warning indicators are generally designed and then input into the prediction model to obtain the warning level. However, when issuing warnings for sudden environmental safety in ports, the above scheme inputs all indicators into the prediction model without distinction. For some indicators related to environmental prediction, they themselves have dynamic significance, that is, they show a certain trend of change over time, and this trend has a certain indicative role for early warnings of sudden environmental events. Therefore, ignoring their time attributes will lead to low accuracy of port environmental risk monitoring and early warning. Summary of the Invention

[0004] In view of this, the present invention proposes a port environmental risk monitoring and early warning method and system for improving the accuracy of port environmental risk monitoring and early warning.

[0005] In order to achieve the above object, a port environmental risk monitoring and early warning method is provided, the method comprising:

[0006] S1: Determine the predictive indicators for port environmental risk monitoring and early warning;

[0007] S2: classifying the prediction indicators into static indicators and non-static indicators;

[0008] S3: performing a weighting operation on the prediction indicators to obtain a weight of each prediction indicator;

[0009] The weighting operation of the prediction indicators is specifically as follows:

[0010] S3.1: Perform a weighting operation on the static indicators to obtain a weight base value for each static indicator;

[0011] S3.2: performing a weighting operation on the non-static indicators to obtain a weight base value for each non-static indicator;

[0012] The weighting formula of the non-static indicator is:

[0013] ;

[0014] ;

[0015] Where a(t) is the time attribute factor of the non-static indicator; t is the sampling time, t0 is the initial timing time, λ(t) is the weight base value of the non-static indicator at time t, b i (t) is the monitoring value of the i-th indicator at sampling time t; B i is the preset threshold of the i-th indicator;

[0016] S3.3: Obtain the weight of each prediction indicator based on the weight base value of each non-static indicator and the weight base value of each static indicator;

[0017] S4: Preprocessing the numerical indicators in the prediction indicators according to the weight of each prediction indicator, and combining the non-numerical indicators in the prediction indicators and the values of the preprocessed numerical indicators to obtain a data set for port environmental risk monitoring and early warning;

[0018] S5: Establish a port environmental risk monitoring and early warning model;

[0019] S6: Inputting the port environmental risk monitoring and early warning data set into the port environmental risk monitoring and early warning model to obtain a safety early warning result.

[0020] Preferably, t0 is the time when the content of dangerous goods in the air monitored by the monitoring point exceeds the corresponding threshold value as the initial timing time of t0; i The maximum historical content of dangerous goods in the air monitored by the monitoring point is used as the preset threshold value of the i-th indicator.

[0021] Preferably, in S1, the prediction indicators include the name of the dangerous goods in the port, the category of the dangerous goods in the port, the content of the dangerous goods in the air, the relative vapor density of the dangerous goods in the port, the maximum storage capacity of the dangerous goods in the port, the atmospheric toxicity endpoint concentration (1 / mg / m 3 ), atmospheric toxicity endpoint concentration (2 / mg / m 3 ), temperature at the location where the dangerous goods are located, humidity at the location where the dangerous goods are located, and wind speed at the location where the dangerous goods are located.

[0022] Preferably, in S2, the static indicators are: name of dangerous goods in the port, category of dangerous goods in the port, relative vapor density of dangerous goods in the port, maximum storage capacity of dangerous goods in the port, atmospheric toxicity endpoint concentration (1 / mg / m 3 ), atmospheric toxicity endpoint concentration (2 / mg / m 3); The non-static indicators are: the content of dangerous goods in the air, the temperature of the location where the dangerous goods are located, the humidity of the location where the dangerous goods are located, and the wind speed of the location where the dangerous goods are located.

[0023] Preferably, in S3.3, the weight of each prediction indicator is obtained by normalizing the weight base value of each non-static indicator and the weight base value of each static indicator.

[0024] Preferably, in said S4, the numerical indicators in the prediction indicators are the content of dangerous goods in the air, the relative vapor density of dangerous goods in the port, the maximum storage capacity of dangerous goods in the port, the atmospheric toxicity endpoint concentration (1 / mg / m 3 ), atmospheric toxicity endpoint concentration (2 / mg / m 3 ), temperature at the location where the dangerous goods are located, humidity at the location where the dangerous goods are located, and wind speed at the location where the dangerous goods are located.

[0025] Preferably, in S4, the preprocessing operation is specifically: multiplying the monitoring value of each numerical indicator by the weight of the prediction indicator to obtain the value of the prediction indicator.

[0026] Preferably, in said S5, an artificial neural network model is adopted as the port environmental risk monitoring and early warning model.

[0027] Preferably, the artificial neural network model includes an input layer, a hidden layer and an output layer.

[0028] According to another aspect of the present invention, a port environmental risk monitoring and early warning system is provided. The system adopts any one of the above-mentioned port environmental risk monitoring and early warning methods, and the system includes:

[0029] A prediction indicator determination module is used to determine prediction indicators for port environmental risk monitoring and early warning;

[0030] An indicator classification module is used to classify the prediction indicators into static indicators and non-static indicators;

[0031] A weighting module, configured to perform a weighting operation on the prediction indicators to obtain a weight for each prediction indicator;

[0032] A data set calculation module, configured to perform a preprocessing operation on the numerical indicators in the prediction indicators according to the weight of each prediction indicator, and combine the values of the non-numerical indicators in the prediction indicators and the preprocessed numerical indicators to obtain a data set for port environmental risk monitoring and early warning;

[0033] Safety early warning model establishment module, used to establish a port environmental risk monitoring and early warning model;

[0034] The early warning module is used to input the data set for port environmental risk monitoring and early warning into the port environmental risk monitoring and early warning model to obtain a safety early warning result. The advantages and beneficial effects of the present invention are:

[0035] When the present invention performs safety early warning monitoring on port environmental risks, the prediction indicators are classified into static indicators and non-static indicators. The static indicators are: the name of dangerous goods in the port, the category of dangerous goods in the port, the relative vapor density of dangerous goods in the port, the maximum storage capacity of dangerous goods in the port, the atmospheric toxicity endpoint concentration (1 / mg / m 3 ), atmospheric toxicity endpoint concentration (2 / mg / m 3 ); non-static indicators include: the content of dangerous goods in the air, the temperature of the dangerous goods location, the humidity of the dangerous goods location, and the wind speed of the dangerous goods location; and in the subsequent indicator utilization process, the time attribute of non-static indicators will be reflected to improve the accuracy of port environmental risk monitoring and early warning;

[0036] The present invention weights the time attributes of non-static indicators based on a continuous time function, and considers the influence of monitoring values on the weights during the weighting process, thereby effectively improving the accuracy of weighting non-static indicators at different times. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the description of the present invention or the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flow chart of a port environmental risk monitoring and early warning method provided by an embodiment of the present invention;

[0039] Figure 2 A flowchart of the weighting operation of the prediction indicators provided by the embodiment of the present invention;

[0040] Figure 3 This is a structural diagram of the artificial neural network model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] As attached Figure 1 As shown, a port environmental risk monitoring and early warning method comprises the following steps:

[0043] S1: Determine the predictive indicators for port environmental risk monitoring and early warning;

[0044] In this step, the prediction indicators for port environmental risk monitoring and early warning are determined by searching literature and consulting experts;

[0045] In this embodiment, the prediction indicators include the name of dangerous goods in the port, the category of dangerous goods in the port, the content of dangerous goods in the air, the relative vapor density of dangerous goods in the port, the maximum storage capacity of dangerous goods in the port (TEU), the atmospheric toxicity endpoint concentration (1 / mg / m 3 ), atmospheric toxicity endpoint concentration (2 / mg / m 3 ), temperature of the location where the dangerous goods are located, humidity of the location where the dangerous goods are located, wind speed of the location where the dangerous goods are located;

[0046] Dangerous goods are classified according to their hazardous properties and potential hazards, and are generally divided into the following nine categories: Explosives (Class 1): These items include explosive substances and articles, such as dynamite, detonators, and fireworks. Explosives can react rapidly and violently under pressure, temperature changes, or other factors, producing explosive effects. Gases (Class 2): Gases are classified as flammable, non-flammable, non-toxic, and toxic. Common gases include hydrogen, methane, and carbon dioxide. Flammable liquids (Class 3): These substances can ignite and burn rapidly at room temperature, such as gasoline, alcohol, and diesel. Flammable solids (Class 4): These substances are flammable in air or react violently with water, such as sulfur and white phosphorus. Oxidizing substances and organic peroxides (Class 5): Oxidizing substances can release oxygen to ignite combustion, while organic peroxides easily decompose under external influences such as high temperature and shock, releasing large amounts of heat energy. Common oxidizing substances include potassium permanganate and 84 disinfectant; Toxic and infectious substances (Class 6): These substances include infectious substances such as pesticides and viruses; Radioactive substances (Class 7): Radioactive substances are radioactive, and common examples include glow-in-the-dark powder and uranium; Corrosive substances (Class 8): These substances are corrosive, such as sulfuric acid and hydrochloric acid; Miscellaneous hazardous substances and articles (Class 9): These include environmentally hazardous substances such as lithium batteries and blue asbestos;

[0047] In this embodiment, an environmental safety early warning is performed for gaseous dangerous goods in ports. Therefore, the content of dangerous goods in the air is used as a prediction indicator. Furthermore, the content of dangerous goods in the air is the content of dangerous goods in the air monitored by the sensor at the monitoring point.

[0048] For example, the following table shows some prediction indicators for some other dangerous goods. Please refer to the table below for details.

[0049] Table 1 Some prediction indicators for gaseous dangerous goods (item 2.1)

[0050]

[0051] S2: classifying the prediction indicators into static indicators and non-static indicators;

[0052] In the field of port environmental risk safety early warning, the existing technology generally designs early warning indicators and then inputs the indicators into the prediction model to obtain the early warning level; however, when issuing port environmental risk safety early warning, the above scheme inputs all indicators into the prediction model without distinction. For some indicators related to environmental prediction, they themselves have dynamic significance, that is, they show a certain trend of change over time, and this trend has a certain indicative effect on the early warning of environmental risks. Therefore, ignoring their time attributes will lead to low accuracy of port environmental risk monitoring and early warning; therefore, according to the above situation, this embodiment classifies the prediction indicators into static indicators and non-static indicators, and in the subsequent indicator utilization process, reflects the time attributes of the non-static indicators to improve the accuracy of sudden environmental safety early warning;

[0053] Specifically, the static indicators are: name of dangerous goods in the port, category of dangerous goods in the port, relative vapor density of dangerous goods in the port, maximum storage capacity of dangerous goods in the port (TEU), atmospheric toxicity endpoint concentration (1 / mg / m 3 ), atmospheric toxicity endpoint concentration (2 / mg / m 3 );

[0054] The non-static indicators are: the content of dangerous goods in the air, the temperature of the location where the dangerous goods are located, the humidity of the location where the dangerous goods are located, and the wind speed of the location where the dangerous goods are located;

[0055] S3: performing a weighting operation on the prediction indicators to obtain a weight of each prediction indicator;

[0056] In this step, attach Figure 2 The flowchart of the weighting operation of the prediction indicators is shown in the attached figure. Figure 2 As shown, the weighting operation of the prediction indicators is specifically as follows:

[0057] S3.1: Perform a weighting operation on the static indicators to obtain a weight base value for each static indicator;

[0058] A preset number of experts are selected to score the importance of the static indicators, and then the average score is taken as the weight base value;

[0059] S3.2: performing a weighting operation on the non-static indicators to obtain a weight base value for each non-static indicator;

[0060] Compared with the existing technology, this embodiment is the first to perform weighting of time attributes based on non-static indicators. Therefore, the expert scoring method cannot perform effective weighting operations. This embodiment proposes a non-static indicator weighting method for implementing non-static indicator weighting operations.

[0061] The weighting formula of the non-static indicator is:

[0062] ;

[0063] ;

[0064] Where a(t) is the time attribute factor of the non-static indicator; t is the sampling time, t0 is the initial timing time, λ(t) is the weight base value of the non-static indicator at time t, b i (t) is the monitoring value of the i-th indicator at sampling time t; B i is the preset threshold of the i-th indicator;

[0065] In this embodiment, t0 is the time when the content of dangerous goods in the air monitored by the monitoring point exceeds the corresponding threshold value, which is used as the initial timing time of t0; i The maximum historical content of dangerous goods in the air monitored by the monitoring point is used as the preset threshold value of the i-th indicator;

[0066] For the purpose of convenient calculation, time t0 can be assigned a value of 1, and time t is defined as the ratio of the difference from time t0 to the sampling interval; for example, if time t0 is 02:20; time t is 02:40, and the sampling interval is 5 minutes, then t0 is 1 and t is 4;

[0067] Through the above operations, the time attributes of non-static indicators are weighted based on continuous time functions, and the influence of monitoring values on the weights is considered in the weighting process, which effectively improves the accuracy of weighting of non-static indicators at different times.

[0068] S3.3: Obtain the weight of each prediction indicator based on the weight base value of each non-static indicator and the weight base value of each static indicator;

[0069] In this step, the weight of each prediction indicator is obtained by normalizing the weight base value of each non-static indicator and the weight base value of each static indicator;

[0070] S4: Preprocessing the numerical indicators in the prediction indicators according to the weight of each prediction indicator, and combining the non-numerical indicators in the prediction indicators and the values of the preprocessed numerical indicators to obtain a data set for port environmental risk safety early warning;

[0071] The numerical indicators in the prediction index are the content of dangerous goods in the air, the relative vapor density of dangerous goods in the port, the maximum storage capacity of dangerous goods in the port (TEU), the atmospheric toxicity endpoint concentration (1 / mg / m 3 ), atmospheric toxicity endpoint concentration (2 / mg / m 3 ), temperature of the location where the dangerous goods are located, humidity of the location where the dangerous goods are located, wind speed of the location where the dangerous goods are located;

[0072] The preprocessing operation specifically includes: multiplying the monitoring value of each numerical indicator by the weight of the prediction indicator to obtain the value of the prediction indicator;

[0073] S5: Establish a port environmental risk monitoring and early warning model;

[0074] In this step, an artificial neural network model is used as the port environmental safety risk warning model. Artificial Neural Networks (ANN) are mathematical models that simulate the structure and function of biological nervous systems. They are composed of a large number of interconnected nodes (called neurons). Each node represents a specific output function (called an activation function), and the connections between nodes represent the weighted values of the signals (called weights). These weights are equivalent to the memory of the artificial neural network. The output of the artificial neural network model depends on the network structure, connection method, weights, and activation function. The artificial neural network model itself is usually an approximation of a certain algorithm or function in nature, or it may be an expression of a logical strategy. The artificial neural network model processes information by adjusting the connection relationships between a large number of nodes. Each node represents a specific output function (activation function), and the connections between nodes have weights. These weights determine the strength of the signal passing through the connection. The network continuously adjusts these weights through learning to optimize the network output. The network learning process is usually achieved through mathematical statistical methods, allowing the network to simulate human decision-making and judgment capabilities.

[0075] In this embodiment, the attached Figure 3 The structure diagram of the artificial neural network model is shown in the attached figure. Figure 3 As shown, the artificial neural network model includes an input layer, a hidden layer and an output layer;

[0076] The input layer is the starting point of the artificial neural network model and is used to receive a data set. The input layer does not perform any calculations but only transmits the data in the data set to the hidden layer.

[0077] The hidden layer is the part of the artificial neural network model that actually performs calculations and is responsible for extracting features from the data set transmitted by the input layer. The hidden layer can be one or more layers. The number of hidden layers can be adjusted according to the requirements of the specific task. Generally, increasing the number of hidden layers can increase the complexity of the network, enabling it to learn more complex patterns. Each hidden layer introduces nonlinearity through an activation function, allowing the network to learn complex nonlinear relationships. The hidden layer works through forward propagation and backpropagation mechanisms. In forward propagation, the input data is calculated through the weights and biases of each layer to ultimately obtain the output. In backpropagation, the weights and biases of each layer are adjusted through the gradient descent algorithm based on the error of the output layer to minimize the loss function. This process is iterated continuously until the network achieves satisfactory performance.

[0078] In this embodiment, the activation function of the hidden layer is the ReLU activation function;

[0079] The output layer is the last layer of the artificial neural network model and is responsible for generating the final prediction result;

[0080] S6: Inputting the port environmental risk monitoring and early warning data set into the port environmental risk monitoring and early warning model to obtain a safety early warning result.

[0081] Embodiment 2: This embodiment includes a port environmental risk monitoring and early warning system. The system adopts a port environmental risk monitoring and early warning method of embodiment 1. The system includes:

[0082] A prediction indicator determination module is used to determine prediction indicators for port environmental risk monitoring and early warning;

[0083] An indicator classification module is used to classify the prediction indicators into static indicators and non-static indicators;

[0084] A weighting module, configured to perform a weighting operation on the prediction indicators to obtain a weight for each prediction indicator;

[0085] A data set calculation module, configured to perform a preprocessing operation on the numerical indicators in the prediction indicators according to the weight of each prediction indicator, and combine the values of the non-numerical indicators in the prediction indicators and the preprocessed numerical indicators to obtain a data set for port environmental risk monitoring and early warning;

[0086] Safety early warning model establishment module, used to establish a port environmental risk monitoring and early warning model;

[0087] The early warning module is configured to input the data set for port environmental risk monitoring and early warning into the port environmental risk monitoring and early warning model to obtain a safety early warning result. Embodiment 3. This embodiment includes a computer-readable storage medium having a data processing program stored thereon. The data processing program is executed by a processor according to the method for port environmental risk monitoring and early warning according to embodiment 1.

[0088] Those skilled in the art will appreciate that the embodiments herein may be provided as methods, apparatuses (devices), or computer program products. Therefore, the present invention may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. These include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery medium.

[0089] This document is described with reference to flowcharts and / or block diagrams of methods, apparatuses (devices), and computer program products according to the embodiments of this document. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] The embodiments and / or implementation methods described above are only used to illustrate the preferred embodiments and / or implementation methods for realizing the technology of the present invention, and are not intended to impose any formal restrictions on the implementation methods of the technology of the present invention. Any person skilled in the art may make some changes or modifications to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as technologies or embodiments that are essentially the same as the present invention. Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A port environmental risk monitoring and early warning method, characterized in that: The method comprises the following steps: S1: Determine the predictive indicators for port environmental risk monitoring and early warning; S2: classifying the prediction indicators into static indicators and non-static indicators; S3: performing a weighting operation on the prediction indicators to obtain a weight of each prediction indicator; The weighting operation of the prediction indicators is specifically as follows: S3.1: Perform a weighting operation on the static indicators to obtain a weight base value for each static indicator; S3.2: performing a weighting operation on the non-static indicators to obtain a weight base value for each non-static indicator; The weighting formula of the non-static indicator is: ； ； Where a(t) is the time attribute factor of the non-static indicator; t is the sampling time, t0 is the initial timing time, λ(t) is the weight base value of the non-static indicator at time t, b i (t) is the monitoring value of the i-th indicator at sampling time t; B i is the preset threshold of the i-th indicator; S3.3: Obtain the weight of each prediction indicator based on the weight base value of each non-static indicator and the weight base value of each static indicator; S4: Preprocessing the numerical indicators in the prediction indicators according to the weight of each prediction indicator, and combining the non-numerical indicators in the prediction indicators and the values of the preprocessed numerical indicators to obtain a data set for port environmental risk monitoring and early warning; S5: Establish a port environmental risk monitoring and early warning model; S6: Inputting the port environmental risk monitoring and early warning data set into the port environmental risk monitoring and early warning model to obtain a safety early warning result.

2. A port environmental risk monitoring and early warning method according to claim 1, characterized in that: t0 is the moment when the content of dangerous goods in the air detected by the monitoring point exceeds the corresponding threshold, and this moment is used as the initial timing moment; i The maximum historical content of dangerous goods in the air monitored by the monitoring point is used as the preset threshold value of the i-th indicator.

3. A port environmental risk monitoring and early warning method according to claim 1, characterized in that: In S1, the prediction indicators include the name of dangerous goods in the port, the category of dangerous goods in the port, the content of dangerous goods in the air, the relative vapor density of dangerous goods in the port, the maximum storage capacity of dangerous goods in the port, and the atmospheric toxicity endpoint concentration_1 / mg / m 3 , atmospheric toxicity endpoint concentration_2 / mg / m 3 , temperature at the location where the dangerous goods are located, humidity at the location where the dangerous goods are located, and wind speed at the location where the dangerous goods are located.

4. A port environmental risk monitoring and early warning method according to claim 1, characterized in that: In S2, the static indicators are: name of dangerous goods in the port, category of dangerous goods in the port, relative vapor density of dangerous goods in the port, maximum storage capacity of dangerous goods in the port and atmospheric toxicity endpoint concentration_1 / mg / m 3 , atmospheric toxicity endpoint concentration_2 / mg / m 3 The non-static indicators are: the content of dangerous goods in the air, the temperature at the location where the dangerous goods are located, the humidity at the location where the dangerous goods are located, and the wind speed at the location where the dangerous goods are located.

5. A port environmental risk monitoring and early warning method according to claim 1, characterized in that: In S3.3, the weight of each prediction indicator is obtained by normalizing the weight base value of each non-static indicator and the weight base value of each static indicator.

6. A port environmental risk monitoring and early warning method according to claim 1, characterized in that: In S4, the numerical indicators in the prediction index are the content of dangerous goods in the air, the relative vapor density of dangerous goods in the port, the maximum storage capacity of dangerous goods in the port, and the atmospheric toxicity endpoint concentration_1 / mg / m 3 , atmospheric toxicity endpoint concentration_2 / mg / m 3 , temperature at the location where the dangerous goods are located, humidity at the location where the dangerous goods are located, and wind speed at the location where the dangerous goods are located.

7. A port environmental risk monitoring and early warning method according to claim 6, characterized in that: In S4, the preprocessing operation is specifically: multiplying the monitoring value of each numerical indicator by the weight of the prediction indicator to obtain the value of the prediction indicator.

8. A port environmental risk monitoring and early warning method according to claim 1, characterized in that: In S5, an artificial neural network model is used as a port environmental risk monitoring and early warning model.

9. A port environmental risk monitoring and early warning method according to claim 8, characterized in that: The artificial neural network model includes an input layer, a hidden layer and an output layer.

10. A port environmental risk monitoring and early warning system, characterized in that: The system adopts a port environmental risk monitoring and early warning method according to any one of claims 1 to 9, and the system comprises: A prediction indicator determination module is used to determine prediction indicators for port environmental risk monitoring and early warning; An indicator classification module is used to classify the prediction indicators into static indicators and non-static indicators; A weighting module, configured to perform a weighting operation on the prediction indicators to obtain a weight for each prediction indicator; A data set calculation module, configured to perform a preprocessing operation on the numerical indicators in the prediction indicators according to the weight of each prediction indicator, and combine the values of the non-numerical indicators in the prediction indicators and the preprocessed numerical indicators to obtain a data set for port environmental risk monitoring and early warning; Safety early warning model establishment module, used to establish a port environmental risk monitoring and early warning model; The early warning module is used to input the data set for port environmental risk monitoring and early warning into the port environmental risk monitoring and early warning model to obtain a safety early warning result.

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