Hazardous waste informatization risk assessment and supervision system based on AI artificial intelligence

Abstract

The invention relates to the technical field of assessment management, and discloses a hazardous waste informatization risk assessment and supervision system based on AI artificial intelligence. Comprising the following steps: extracting hazard characteristics, formulating an assessment and supervision period, collecting risk summary data of a landfill area in the assessment and supervision period, predicting a landfill risk state through a risk assessment model, determining whether to give a risk early warning prompt, and formulating a corresponding risk supervision level and a risk supervision measure. Compared with the prior art, the method has the advantages that the AI artificial intelligence model can be combined to accurately and quickly predict the real-time environmental pollution state of the hazardous waste landfill area, so that the limitation of data with relatively few dimensions during risk assessment is avoided; and effective risk optimization and avoidance measures can be provided for the landfill area with the pollution risk, so that the hazardous wastes are prevented from causing more serious pollution influence on the landfill area and the surrounding area thereof, and the assessment and supervision operation of hazardous waste informatization is further facilitated.

Description

Technical Field

[0001] The present invention relates to the field of assessment management technology, and more specifically, to a hazardous waste information risk assessment and supervision system based on AI artificial intelligence. Background Art

[0002] With the continuous acceleration of the industrialization process, the amount of hazardous waste generated has also increased dramatically. For some hazardous wastes that are toxic, corrosive, flammable and polluting, soil landfill is often used for treatment. After the soil landfill treatment of hazardous waste, it is also necessary to effectively evaluate and supervise the pollution caused by hazardous waste to the environment in the landfill area. Therefore, it is necessary to combine AI artificial intelligence technology to effectively evaluate the pollution risk of hazardous waste in the landfill area and provide data support for subsequent supervision.

[0003] The patent application with reference publication number CN118982247A discloses an environmental risk monitoring system for hazardous waste storage warehouses, which can timely discover potential environmental risks and prevent possible environmental pollution and safety accidents by real-time monitoring of key parameters of hazardous waste storage warehouses, such as temperature, humidity and gas concentration. It uses big data and cloud computing technology to process and store data, remove abnormal and duplicate data, calculate statistical indicators such as mean and variance, ensure the quality and timeliness of data, and provide a reliable basis for accurate risk assessment. It uses machine learning algorithms such as decision trees or neural networks to establish risk assessment models, which can comprehensively and accurately conduct risk assessments based on multiple factors, and divide different risk levels to make the risk situation clearer and more specific. The existing hazardous waste risk assessment and supervision system assesses the pollution risks that may be caused by hazardous wastes by continuously monitoring the environmental pollution risk data of hazardous wastes in real time. For example, in the above-mentioned patent application, it can discover potential environmental risks by real-time monitoring of the key parameters of temperature, humidity and gas concentration in hazardous waste storage warehouses, and use big data and cloud computing technology to ensure the quality and timeliness of data. Although this method can achieve the assessment effect of hazardous waste environmental pollution risks, it adopts real-time and uninterrupted data collection method, which can only meet the unified collection needs of hazardous waste pollution risk data of different types and attributes, and cannot formulate targeted data collection and risk assessment time according to the different types and attributes of hazardous wastes, resulting in too much collection of pollution risk data, which not only increases the workload of risk assessment calculation, but also easily causes inaccurate risk assessment calculation results, which is not conducive to the subsequent risk assessment and supervision of hazardous wastes.

[0004] In view of this, the present invention proposes an AI-based hazardous waste information risk assessment and supervision system to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a hazardous waste information risk assessment and supervision system based on AI artificial intelligence, applied to a risk supervision platform, comprising: The feature extraction module is used to collect basic hazard information of hazardous waste and extract hazard features from the basic hazard information. The hazard features include landfill type, hazard attributes, safety issues and landfill specifications; The cycle formulation module is used to calculate the hazard coefficient of hazardous wastes according to the hazard characteristics and formulate the assessment and supervision cycle of hazardous wastes; The data collection module is used to identify the landfill area of ​​hazardous waste and collect risk summary data of the landfill area during the assessment and supervision cycle. The risk summary data includes pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen decay rate; The intelligent prediction module is used to input the collected risk summary data into the risk assessment model to predict the landfill risk status, which includes a risk-free state and a risky state, and determine whether to issue a risk warning prompt; The risk supervision module is used to identify the supervision data of the landfill area and formulate corresponding risk supervision levels and risk supervision measures.

[0006] Further, the extraction steps of landfill type, hazard attributes, safety issues and landfill specifications are as follows: All basic hazard information of hazardous waste before landfill is queried through the database, and the hazard semantics of all basic hazard information are identified one by one through natural language processing technology; The basic hazard information with hazard semantics of type, attribute, matter and specification is recorded as first data, second data, third data and fourth data respectively; The recording moments of all the first data, the second data, the third data and the fourth data are identified one by one through the timestamps, and recorded as the first moment, the second moment, the third moment and the fourth moment respectively; The first moment, the second moment, the third moment and the fourth moment are arranged in sequence according to the chronological order, and the first data, the second data, the third data and the fourth data corresponding to the first moment, the second moment, the third moment and the fourth moment at the last position are filtered out and recorded as the landfill type, hazard attribute, safety matter and landfill specification respectively.

[0007] Furthermore, the calculation steps of the hazard coefficient are as follows: The numerical parts of landfill type, hazard attribute, safety matter and landfill specification are separated by word segmentation technology to obtain type value, attribute value, matter value and specification value; Split the specification value into an integer part and a decimal part, and perform scaling comparison on the decimal part; When the decimal part of the specification value is greater than 0.45, add 1 to the integer part of the specification value and remove the decimal part of the specification value to obtain the corrected specification value; When the decimal part of the specification value is less than or equal to 0.45, the decimal part of the specification value is eliminated to obtain the corrected specification value; The type value, attribute value, event value and modified specification value are assigned corresponding proportional coefficients in turn to calculate the hazard coefficient; The expression of the hazard coefficient is: ; In the formula, is the hazard factor, is a type value, is the attribute value, is the item value, is the revised specification value, , , , All of them are proportional coefficients greater than 0.

[0008] Furthermore, the steps for developing the evaluation supervision cycle are as follows: Multiply the hazard factor by the standard assessment duration to generate the assessment supervision cycle; The expression for evaluating the supervision cycle is: ; In the formula, To evaluate the regulatory cycle, This is the standard assessment duration.

[0009] Furthermore, the steps for collecting the acid-base fluctuation value are as follows: The closed line of the landfill area is divided equally according to the four directions of top, bottom, left and right to obtain the upper boundary, lower boundary, left boundary and right boundary respectively; The minimum distance from the left boundary to the right boundary is measured horizontally by a scale to obtain the left-right distance value; The minimum distance from the upper boundary to the lower boundary is measured along the vertical direction by using a scale to obtain the upper and lower distance values; Compare the left-right distance value with the up-down distance value, and record one-third of the smallest value after comparison as the standard length; The center point of the landfill area is identified by computer vision technology, and a circle is drawn with the center point as the base point and the standard length as the radius to generate a standard circle; Mark the boundaries of the standard circle at equal distances. detection points and measure them separately through pH sensors The pH value of each test point at the current moment and after an evaluation and supervision cycle is obtained. The original pH value and Real-time pH value; Will The original pH value and The real-time pH values ​​are accumulated and averaged, and the difference between the average values ​​is taken to get the absolute value to generate the pH fluctuation value; The expression of acid-base fluctuation value is: ; In the formula, is the pH fluctuation value, For the Real-time pH value of each detection point, For the The original pH value of each test point.

[0010] Furthermore, the steps for collecting the concentration rise rate are as follows: Based on the preset evaluation duration, mark B evaluation moments in the evaluation supervision cycle; The VOCs sensor collects the The concentration of volatile organic compounds at each detection point is obtained. concentration value, The concentration values ​​are accumulated and averaged to obtain B real-time concentration values; The expression of real-time concentration value is: ; In the formula, For the The real-time concentration value at the evaluation moment, =1,2...B, For the The first evaluation moment concentration value; The first The real-time concentration value at the evaluation moment is After subtracting the real-time concentration value at each evaluation moment, we can obtain concentration difference, and The concentration differences are accumulated and averaged to obtain the sub-climbing value; The expression of the sub-climb value is: ; In the formula, is the sub-climb value, For the Real-time concentration value at each evaluation moment; After comparing the sub-climb value with the preset evaluation time, the concentration climb rate is obtained; The expression of concentration climbing rate is: ; In the formula, is the concentration climbing rate, The preset duration.

[0011] Furthermore, the steps for collecting the oxygen decay rate are as follows: The oxygen concentration sensor is used to collect The oxygen concentration value of each detection point at the current moment and after an evaluation and supervision cycle is obtained. The original concentration value and End concentration value; Will The original concentration value and The final concentration values ​​are accumulated and averaged, and the difference between the two average values ​​is used to generate the oxygen attenuation value; The expression of oxygen attenuation value is: ; In the formula, is the oxygen attenuation value, For the The original concentration value of each detection point, For the The end concentration value of each detection point; Compare the oxygen decay value with the duration corresponding to the assessment supervision cycle to obtain the oxygen decay rate; The expression of oxygen decay rate is: ; In the formula, is the oxygen decay rate, The duration corresponding to the assessment supervision cycle.

[0012] Furthermore, the training steps of the risk assessment model are as follows: Collect multiple sets of risk summary data in advance when the landfill area is in a risk-free state or a risky state; Mark each group of risk summary data as a training feature, annotate the landfill risk status of each group of training features, convert the risk-free status to 0, and convert the risky status to 1, divide the annotated training features into a training set and a test set, use the training set to train the risk assessment model, and use the test set to test the risk assessment model, preset an error threshold, and output the risk assessment model when the mean of the prediction errors of all training features in the test set is less than the error threshold; The steps to determine whether to issue a risk warning are as follows: When the output of the risk assessment model is 0, the landfill risk status is a risk-free status, and it is determined that no risk warning prompt will be issued; When the output of the risk assessment model is 1, the landfill risk status is a risky state, and a risk warning prompt is issued.

[0013] Furthermore, the steps for identifying regulatory data are as follows: The abnormalities of pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen decay rate were compared in turn; When the acid-base fluctuation value is greater than the acid-base fluctuation threshold, the acid-base fluctuation value is recorded as regulatory data; When the concentration climbing rate is greater than the concentration climbing threshold, the concentration climbing rate is recorded as regulatory data; When the temperature value is not within the temperature standard range, the temperature value is recorded as regulatory data; When the humidity value is not within the humidity standard range, the humidity value is recorded as regulatory data; When the oxygen decay rate is greater than the oxygen decay threshold, the oxygen decay rate is recorded as regulatory data.

[0014] Furthermore, the risk supervision level includes the primary risk level and the secondary risk level; The steps for establishing the primary and secondary risk levels are as follows: Count the number of regulatory data, and when the number of regulatory data is 1 or 2, formulate a first-level risk level; When the number of regulatory data is 3, 4, or 5, a secondary risk level is developed; Risk management measures include building anti-penetration shielding walls, biochemical soil remediation, soil excavation and replacement, and complete closure of landfill areas; The steps for establishing an impermeable barrier wall, biochemical soil remediation, soil excavation and replacement, and complete closure of the landfill area are as follows: When the risk supervision level is the first-level risk level, measures for building anti-seepage shielding walls, biochemical remediation of soil, and excavation and replacement of soil are formulated; When the risk supervision level is the secondary risk level, measures shall be formulated to completely close the landfill area.

[0015] The technical effects and advantages of the hazardous waste information risk assessment and supervision system based on AI artificial intelligence of the present invention are as follows: The present invention extracts hazard characteristics from basic hazard information, calculates the hazard coefficient of hazardous waste, and formulates an assessment and supervision cycle for hazardous waste. It can accurately formulate the corresponding pollution risk assessment time according to the pollution risk of hazardous wastes of different types and properties to the environment, thereby providing an accurate and reasonable time span limit for the subsequent collection of pollution risk data of hazardous wastes, ensuring that sufficient required data can be collected within a reasonable time range, and by collecting risk summary data of the landfill area within the assessment and supervision cycle, the collected risk summary data is input into the risk assessment model to predict the landfill risk status, which can be combined with the AI ​​artificial intelligence model to landfill hazardous waste. The real-time environmental pollution status of the area can be accurately and quickly predicted, thereby realizing multi-dimensional and comprehensive risk assessment and early warning operations in the landfill area, avoiding the limitations of data with fewer dimensions in risk assessment, and greatly improving the accuracy of risk assessment results. At the same time, by formulating corresponding risk supervision levels and risk supervision measures, the landfill areas with pollution risks can be accurately divided into risk levels, and effective risk optimization and avoidance measures can be provided, so that the hazardous waste pollution risk in the landfill area can be timely optimized and avoided, preventing hazardous waste from causing more serious pollution to the landfill area and its surrounding areas, which is conducive to the information-based assessment and supervision of hazardous waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a hazardous waste information risk assessment and supervision system based on AI artificial intelligence provided in Embodiment 1 of the present invention; Figure 2 A flow chart of a hazardous waste information risk assessment and supervision method based on AI artificial intelligence provided in Embodiment 2 of the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention; Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 As shown, the hazardous waste information risk assessment and supervision system based on AI artificial intelligence described in this embodiment is applied to the risk supervision platform, including: The feature extraction module collects the basic hazard information of hazardous waste and extracts hazard features from the basic hazard information; Basic hazard information refers to the relevant information on the pollution and harm that hazardous wastes have to the environment before they are landfilled, so that the basic hazard information can provide a preliminary and comprehensive representation of the basic types, specifications, sizes and other information of hazardous wastes, and can also provide a data basis for the subsequent identification of hazard characteristics; Basic hazard information includes, but is not limited to, specification and quantity information, pollution property information, hazard level information, precaution information, etc. of hazardous waste. For example, specification and quantity information may be the weight and volume of hazardous waste; pollution property information may be the corrosiveness, flammability, toxicity, and radioactivity of hazardous waste; hazard level information may be the high hazard or low hazard of hazardous waste; precaution information may be the transportation requirements and landfill requirements of hazardous waste; basic hazard information is obtained through database query.

[0019] Hazard characteristics refer to the data in the basic hazard information that can truly and specifically indicate the degree of negative impact of hazardous waste on the landfill environment, and serve as the prerequisite for subsequent risk assessment; Hazard characteristics include landfill type, hazard attributes, safety issues and landfill specifications; landfill type refers to the amount of solid, liquid and paste contained in the hazardous waste to be landfilled; hazard attributes refer to the amount of corrosive, flammable, toxic and radioactive substances contained in the hazardous waste to be landfilled; safety issues refer to the amount of moisture-proof, squeeze-proof and high-temperature-proof substances contained in the hazardous waste to be landfilled; landfill specifications refer to the specific volume and size of the hazardous waste to be landfilled; The steps for extracting landfill types, hazard attributes, safety issues and landfill specifications are as follows: All basic hazard information of hazardous waste before landfill is queried through the database, and the hazard semantics of all basic hazard information are identified one by one through natural language processing technology; hazard semantics is used to concisely express the true meaning of basic hazard information, so that complex and lengthy basic hazard information can be converted into a concise and accurate form; The basic hazard information with hazard semantics of type, attribute, matter and specification is recorded as first data, second data, third data and fourth data respectively; The recording moments of all the first data, the second data, the third data and the fourth data are identified one by one through the timestamps, and recorded as the first moment, the second moment, the third moment and the fourth moment respectively; The first moment, the second moment, the third moment and the fourth moment are arranged in sequence according to the chronological order, and the first data, the second data, the third data and the fourth data corresponding to the first moment, the second moment, the third moment and the fourth moment at the last position are filtered out and recorded as the landfill type, hazard attribute, safety matter and landfill specification respectively.

[0020] It should be noted that the data content of landfill type, hazard attributes, safety matters and landfill specifications are all a set of words and numbers, which can concisely and clearly express the basic hazard situation of hazardous waste and avoid complicated and lengthy negative phenomena.

[0021] The cycle formulation module calculates the hazard coefficient of hazardous wastes based on hazard characteristics and formulates the assessment and supervision cycle of hazardous wastes; The hazard coefficient is used to numerically represent the degree of environmental hazard represented by the hazard characteristics of hazardous wastes, so as to indicate the different degrees of hazard of different hazardous wastes and serve as the basis for the subsequent risk assessment of hazardous wastes; The steps for calculating the hazard factor are as follows: The numerical parts of landfill type, hazard attribute, safety issue and landfill specification are separated by word segmentation technology to obtain type value, attribute value, issue value and specification value; the numerical part is used to represent the specific numerical values ​​of landfill type, hazard attribute, safety issue and landfill specification, and serves as the numerical basis for the subsequent hazard coefficient calculation; The specification value is split into an integer part and a decimal part, and the decimal part is scaled and compared; the scaled comparison is used to compare the size of the decimal part of the specification value, and the size change method of the integer part and the decimal part is selected according to the result of the size comparison; When the decimal part of the specification value is greater than 0.45, add 1 to the integer part of the specification value and remove the decimal part of the specification value to obtain the corrected specification value; When the decimal part of the specification value is less than or equal to 0.45, the decimal part of the specification value is eliminated to obtain the corrected specification value; The type value, attribute value, event value and modified specification value are assigned corresponding proportional coefficients in turn to calculate the hazard coefficient; The expression of the hazard coefficient is: ; In the formula, is the hazard factor, is a type value, is the attribute value, is the item value, is the revised specification value, , , , All of them are proportional coefficients greater than 0.

[0022] in, , , , , The setting is to balance the proportion of type value, attribute value, item value and modified specification value in the hazard coefficient, while ensuring the accuracy of the calculation of the hazard coefficient.

[0023] After the hazard coefficient is calculated, it is necessary to combine the hazard coefficient with the standard assessment duration, so as to obtain the assessment supervision cycle that limits the duration of the subsequent risk assessment of hazardous wastes, so that the data collection duration range and the effective duration of the risk assessment corresponding to the subsequent risk assessment of hazardous wastes are based on the assessment supervision cycle; The steps for developing an assessment supervision cycle are as follows: The hazard coefficient is multiplied by the standard assessment duration to generate the assessment supervision cycle; the standard assessment duration refers to the minimum duration that can be used to conduct risk assessments on different types and categories of hazardous waste landfills under normal circumstances, thereby providing a basis for calculating the duration of different assessment supervision cycles; the standard assessment duration is usually obtained by collecting the minimum value of the minimum duration of risk assessments on a large number of different types and categories of hazardous waste landfills in history and calculating the average value; The expression for evaluating the supervision cycle is: ; In the formula, To evaluate the regulatory cycle, This is the standard assessment duration.

[0024] The data collection module identifies the landfill area of ​​hazardous waste and collects risk summary data of the landfill area during the assessment and supervision cycle. The risk summary data includes pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen decay rate; The landfill area refers to the area where hazardous waste is landfilled in the soil, and serves as the target location for subsequent assessment of whether the hazardous waste will cause pollution risks to the overall environment of the landfill location; Since the landfill area corresponds to a large soil area, when identifying the landfill area, it is necessary to first mark the area where the hazardous waste is landfilled through an electronic map, and then use the positioning system to accurately locate the specific landfill location of the hazardous waste in the area. Then, a closed line is drawn along the outer edge of the specific landfill location. At this time, the area within the closed line is the landfill area.

[0025] Risk summary data refers to the data that can express the specific situation of the pollution risk caused by hazardous waste to the environment in the landfill area during the assessment and supervision cycle, which can be used as the basis for subsequent judgment whether the landfill area is contaminated by hazardous waste; The risk summary data includes pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen decay rate; The pH fluctuation value refers to the change in soil pH in the landfill area during the assessment and supervision cycle, which can be used to indicate the degree of influence of hazardous waste on soil pH in the landfill area. The greater the pH fluctuation value, the greater the influence of hazardous waste on soil pH in the landfill area. The steps for collecting acid-base fluctuation values ​​are as follows: The closed line of the landfill area is divided equally according to the four directions of top, bottom, left and right to obtain the upper boundary, lower boundary, left boundary and right boundary respectively; The minimum distance from the left boundary to the right boundary is measured horizontally by a scale to obtain the left-right distance value; The minimum distance from the upper boundary to the lower boundary is measured along the vertical direction by using a scale to obtain the upper and lower distance values; Compare the left-right distance value with the up-down distance value, and record one-third of the smallest value after comparison as the standard length; The center point of the landfill area is identified by computer vision technology, and a circle is drawn with the center point as the base point and the standard length as the radius to generate a standard circle; the center point refers to the intersection of the two diagonal lines of the four corners formed by the upper boundary, lower boundary, left boundary and right boundary in the landfill area, which can be used as the center of the subsequent standard circle; Mark the boundaries of the standard circle at equal distances. detection points and measure them separately through pH sensors The pH value of each test point at the current moment and after an evaluation and supervision cycle is obtained. The original pH value and Real-time pH value; Will The original pH value and The real-time pH values ​​are accumulated and averaged, and the difference between the average values ​​is taken to get the absolute value to generate the pH fluctuation value; The expression of acid-base fluctuation value is: ; In the formula, is the pH fluctuation value, For the Real-time pH value of each detection point, For the The original pH value of each test point.

[0026] The concentration climbing rate refers to the rate of increase in the concentration of volatile organic compounds in the soil of the landfill area during the assessment and supervision cycle, which can be used to indicate the degree of influence of hazardous wastes on volatile organic compounds in the landfill area. The greater the concentration climbing rate, the greater the influence of hazardous wastes on volatile organic compounds in the landfill area. The steps for collecting the concentration rise rate are as follows: Based on the preset evaluation time, B evaluation moments are marked in the evaluation supervision cycle; the preset evaluation time refers to the time span between each evaluation moment of the adjacent chain, which can ensure the independence between two adjacent evaluation moments and ensure that there will be no front-to-back adhesion between the concentration data at each evaluation moment, thereby improving the accuracy of subsequent data calculation; The VOCs sensor collects the The concentration of volatile organic compounds at each detection point is obtained. concentration value, The concentration values ​​are accumulated and averaged to obtain B real-time concentration values; The expression of real-time concentration value is: ; In the formula, For the The real-time concentration value at the evaluation moment, =1,2...B, For the The first evaluation moment concentration value; The first The real-time concentration value at the evaluation moment is After subtracting the real-time concentration value at each evaluation moment, we can obtain concentration difference, and The concentration differences are accumulated and averaged to obtain the sub-climbing value; The expression of the sub-climb value is: ; In the formula, is the sub-climb value, For the Real-time concentration value at each evaluation moment; After comparing the sub-climb value with the preset evaluation time, the concentration climb rate is obtained; The expression of concentration climbing rate is: ; In the formula, is the concentration climbing rate, The preset duration.

[0027] The temperature value refers to the temperature inside the soil in the landfill area during the assessment and supervision cycle, which can be used to indicate the degree of impact of hazardous waste in the landfill area on the temperature inside the soil. The higher the temperature value, the higher the impact of hazardous waste in the landfill area on the temperature inside the soil. The temperature value is obtained through monitoring by temperature sensors buried inside the soil of the landfill area.

[0028] The humidity value refers to the numerical value of the humidity inside the soil in the landfill area during the assessment and supervision cycle, which can be used to indicate the degree of impact of hazardous waste in the landfill area on the humidity inside the soil. The higher the humidity value, the higher the impact of hazardous waste in the landfill area on the humidity inside the soil. The humidity value is obtained through monitoring by a humidity sensor buried inside the soil of the landfill area.

[0029] The oxygen decay rate refers to the degree of decrease in the oxygen concentration in the soil of the landfill area during the assessment and supervision cycle, which can be used to indicate the degree of influence of hazardous waste on the oxygen concentration in the soil of the landfill area. The greater the oxygen decay value, the greater the influence of hazardous waste on the oxygen concentration in the soil of the landfill area. The steps for collecting oxygen decay rate are as follows: The oxygen concentration sensor is used to collect The oxygen concentration value of each detection point at the current moment and after an evaluation and supervision cycle is obtained. The original concentration value and End concentration value; Will The original concentration value and The final concentration values ​​are accumulated and averaged, and the difference between the two average values ​​is used to generate the oxygen attenuation value; The expression of oxygen attenuation value is: ; In the formula, is the oxygen attenuation value, For the The original concentration value of each detection point, For the The end concentration value of each detection point; Compare the oxygen decay value with the duration corresponding to the assessment supervision cycle to obtain the oxygen decay rate; The expression of oxygen decay rate is: ; In the formula, is the oxygen decay rate, The duration corresponding to the assessment supervision cycle.

[0030] The intelligent prediction module inputs the collected risk summary data into the risk assessment model, predicts the landfill risk status, and determines whether to issue a risk warning prompt; After the risk summary data is collected, it needs to be input into the risk assessment model as input data for model prediction. The risk assessment model is an AI artificial intelligence model that can predict the landfill risk status of the landfill area after repeated training and optimization of a large number of different historical risk summary data. It is also used as a tool to determine whether hazardous waste will cause risks to the environment of the landfill area. The landfill risk status is used to indicate whether there will be risk phenomena in the environment of the landfill area. It is also the output result of the risk assessment model. Specifically, the landfill risk status includes a risk-free status and a risky status. The risk-free status means that hazardous waste will not cause pollution risks to the environment of the landfill area, and the risky status means that hazardous waste will cause pollution risks to the environment of the landfill area. The risk-free status and the risky status are obtained by collecting a large number of historical pH fluctuation values, concentration climb rates, temperature values, humidity values ​​and oxygen attenuation rates and the corresponding environmental pollution conditions of the landfill area.

[0031] The training steps of the risk assessment model are as follows: Collect multiple sets of risk summary data in advance when the landfill area is in a risk-free state or a risky state; Mark each group of risk summary data as a training feature, annotate the landfill risk status of each group of training features, including a risk-free state and a risky state, convert the risk-free state and the risky state into digital labels, for example, convert the risk-free state into 0, and convert the risky state into 1; divide the annotated training features into a training set and a test set; use 70% of the training features as a training set and 30% of the training features as a test set; use the training set to train the risk assessment model, and use the test set to test the risk assessment model; preset an error threshold, and output the risk assessment model when the mean of the prediction errors of all training features in the test set is less than the error threshold.

[0032] Exemplarily, the risk assessment model adopts any one of a support vector machine model or a machine learning model; the preset error threshold is pre-set according to the accuracy actually required by the risk assessment model.

[0033] The collected risk summary data is input into the trained risk assessment model, so that the risk assessment model can predict the landfill risk status corresponding to the risk summary data, and determine whether to issue a risk warning prompt based on the predicted landfill risk status, that is, to issue a warning prompt when it is found that hazardous waste will cause pollution risks to the environment of the landfill area; The steps to determine whether to issue a risk warning are as follows: When the output of the risk assessment model is 0, the landfill risk status of the landfill area is no risk, indicating that the hazardous waste will not cause pollution risks to the environment of the landfill area, and it is determined that no risk warning prompt will be issued; When the output of the risk assessment model is 1, the landfill risk status of the landfill area is at risk, indicating that hazardous waste will cause pollution risks to the environment of the landfill area, and a risk warning prompt is issued.

[0034] The risk supervision module identifies the supervision data of the landfill area and formulates the corresponding risk supervision level and risk supervision measures; Regulatory data refers to the specific data in the risk summary data that can cause pollution risks to the environment of the landfill area, and can also be used as the object of subsequent regulatory treatment and risk avoidance of the landfill area; The steps for identifying regulatory data are as follows: The abnormality comparison is performed on the pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen attenuation rate in turn; the abnormality comparison is used to compare whether the pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen attenuation rate have abnormal phenomena, and is used as a direct operation to identify regulatory data; When the acid-base fluctuation value is greater than the acid-base fluctuation threshold, it means that the acid-base fluctuation range of the landfill area is high, and the acid-base fluctuation value is recorded as regulatory data; the acid-base fluctuation threshold refers to the maximum value of the acid-base fluctuation value when there is no risk in the landfill area, which can provide a basis for judging whether the acid-base fluctuation value is regulatory data; When the concentration climbing rate is greater than the concentration climbing threshold, it means that the concentration of volatile organic compounds in the landfill area has increased significantly, and the concentration climbing rate is recorded as regulatory data; the concentration climbing rate refers to the maximum value of the concentration climbing rate in the landfill area when there is no risk, which can provide a basis for judging whether the concentration climbing rate is regulatory data; When the temperature value is not within the temperature standard range, it means that the difference between the soil temperature of the landfill area and the standard temperature range is large, and the temperature value is recorded as regulatory data; the temperature standard range refers to the range between the minimum and maximum temperature values ​​of the landfill area when there is no risk, which can provide a basis for judging whether the temperature value is regulatory data; When the humidity value is not within the humidity standard range, it means that the gap between the soil humidity in the landfill area and the standard humidity range is large, and the humidity value is recorded as regulatory data; the humidity standard range refers to the range between the minimum and maximum humidity values ​​in the landfill area when there is no risk, which can provide a basis for judging whether the humidity value is regulatory data; When the oxygen attenuation rate is greater than the oxygen attenuation threshold, it means that the oxygen concentration in the landfill area has dropped significantly, and the oxygen attenuation rate is recorded as regulatory data. The oxygen attenuation rate refers to the maximum value of the oxygen attenuation rate in the landfill area when there is no risk, which can provide a basis for judging whether the oxygen attenuation rate is regulatory data.

[0035] When the supervision data is identified, the risk supervision level and risk supervision measures of the landfill area can be formulated according to the amount of supervision data. The risk supervision level is used to indicate the severity of the environmental pollution risk of hazardous waste to the landfill area, and the risk supervision measures are used to indicate the measures to optimize and avoid the environmental pollution risk of the landfill area. The risk supervision level includes the first-level risk level and the second-level risk level; the risk severity corresponding to the first-level risk level is less than the risk severity corresponding to the second-level risk level; The steps for establishing the primary and secondary risk levels are as follows: The number of regulatory data is counted. When the number of regulatory data is 1 or 2, it means that the number of regulatory data in the landfill area is small, and a first-level risk level is formulated; When the number of regulatory data is 3, 4, or 5, it means that the number of regulatory data in the landfill area is large, and a secondary risk level is formulated.

[0036] Risk management measures include building anti-penetration shielding walls, biochemical soil remediation, soil excavation and replacement, and complete closure of landfill areas; The steps for establishing an impermeable barrier wall, biochemical soil remediation, soil excavation and replacement, and complete closure of the landfill area are as follows: When the risk supervision level is the first-level risk level, reasonable supervision optimization measures are required for the landfill area, and measures such as building anti-seepage shielding walls, biochemical remediation measures for the soil, and excavation and replacement measures for the soil are formulated; When the risk supervision level is the secondary risk level, the landfill area needs to be closed, and measures to completely close the landfill area are formulated.

[0037] Specifically, when measures are formulated to establish an anti-seepage shielding wall, a barrier extending into the soil along the outer boundary of the landfill area and having an anti-seepage protection function can be constructed, which can isolate the soil in the landfill area from the soil in other areas and prevent it from seeping in. When measures are formulated to carry out biochemical remediation of the soil, the soil can be repaired by planting plants in the landfill area for biodegradation and applying chemical agents in the landfill area for chemical degradation. When measures are formulated to excavate and replace the soil, the soil in the landfill area can be excavated, transported and refilled with new soil, which can play a role in soil renewal and replacement. When measures are formulated to completely close the landfill area, the landfill area needs to be closed, and the hazardous waste must be optimized and re-landfilled, which can eliminate risks in the landfill area.

[0038] In this embodiment, by extracting hazard characteristics from basic hazard information, calculating the hazard coefficient of hazardous waste, and formulating an assessment and supervision cycle for hazardous waste, the corresponding pollution risk assessment time can be accurately formulated according to the pollution risk of hazardous wastes of different types and properties to the environment, thereby providing an accurate and reasonable time span limit for the subsequent collection of pollution risk data of hazardous wastes, ensuring that sufficient required data can be collected within a reasonable time range, and by collecting risk summary data of landfill areas within the assessment and supervision cycle, the collected risk summary data is input into the risk assessment model to predict the landfill risk status, which can be combined with the AI ​​artificial intelligence model to evaluate hazardous waste. The real-time environmental pollution status of the landfill area can be accurately and quickly predicted, thereby realizing multi-dimensional and comprehensive risk assessment and early warning operations in the landfill area, avoiding the limitations of data with fewer dimensions in risk assessment, and greatly improving the accuracy of risk assessment results. At the same time, by formulating corresponding risk supervision levels and risk supervision measures, the landfill areas with pollution risks can be accurately divided into risk levels, and effective risk optimization and avoidance measures can be provided, so that the hazardous waste pollution risk in the landfill area can be timely optimized and avoided, preventing hazardous waste from causing more serious pollution to the landfill area and its surrounding areas, which is conducive to the information-based assessment and supervision of hazardous waste.

[0039] Example 2: Please refer to Figure 2 As shown, the part not described in detail in this embodiment is described in the first embodiment, and a hazardous waste information risk assessment and supervision method based on AI artificial intelligence is provided, which is applied to a risk supervision platform and is implemented through a hazardous waste information risk assessment and supervision system based on AI artificial intelligence, including: S1: Collect basic hazard information of hazardous wastes and extract hazard characteristics from the basic hazard information. The hazard characteristics include landfill type, hazard attributes, safety issues and landfill specifications; S2: Calculate the hazard coefficient of hazardous wastes based on hazard characteristics and develop an assessment and supervision cycle for hazardous wastes; S3: Identify the landfill area of ​​hazardous waste and collect risk summary data of the landfill area during the assessment and supervision cycle. The risk summary data includes pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen decay rate; S4: Input the collected risk summary data into the risk assessment model to predict the landfill risk status, which includes a risk-free state and a risky state, and determine whether to issue a risk warning prompt; S5: If a risk warning is issued, identify the regulatory data of the landfill area and formulate corresponding risk supervision levels and risk supervision measures.

[0040] Example 3: Please refer to Figure 3 As shown, this embodiment discloses an electronic device, including a processor and a memory; Wherein, the memory stores a computer program that can be called by the processor; The processor executes the AI-based hazardous waste information risk assessment and supervision method by calling the computer program stored in the memory.

[0041] Since the electronic device introduced in this embodiment is an electronic device used to implement the hazardous waste information risk assessment and supervision method based on AI artificial intelligence in Example 2 of this application, based on the hazardous waste information risk assessment and supervision method based on AI artificial intelligence introduced in the embodiment of this application, the technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be introduced in detail here. As long as the technical personnel in this field implement the electronic device used in the hazardous waste information risk assessment and supervision method based on AI artificial intelligence in the embodiment of this application, it belongs to the scope of protection of this application.

[0042] Example 4: Please refer to Figure 4 As shown, this embodiment discloses a computer-readable storage medium on which a rewritable computer program is stored; When the computer program is run, it implements the hazardous waste information risk assessment and supervision method based on AI artificial intelligence.

[0043] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The AI-based hazardous waste information risk assessment and supervision system is applied to the risk supervision platform, which is characterized by: include: The feature extraction module is used to collect basic hazard information of hazardous waste and extract hazard features from the basic hazard information. The hazard features include landfill type, hazard attributes, safety issues and landfill specifications; The cycle formulation module is used to calculate the hazard coefficient of hazardous wastes according to the hazard characteristics and formulate the assessment and supervision cycle of hazardous wastes; The data collection module is used to identify the landfill area of ​​hazardous waste and collect risk summary data of the landfill area during the assessment and supervision cycle. The risk summary data includes pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen decay rate; The intelligent prediction module is used to input the collected risk summary data into the risk assessment model to predict the landfill risk status, which includes a risk-free state and a risky state, and determine whether to issue a risk warning prompt; The risk supervision module is used to identify the supervision data of the landfill area and formulate corresponding risk supervision levels and risk supervision measures.

2. The AI-based hazardous waste information risk assessment and supervision system according to claim 1 is characterized in that: The steps for extracting landfill types, hazard attributes, safety issues and landfill specifications are as follows: All basic hazard information of hazardous waste before landfill is queried through the database, and the hazard semantics of all basic hazard information are identified one by one through natural language processing technology; The basic hazard information with hazard semantics of type, attribute, matter and specification is recorded as first data, second data, third data and fourth data respectively; The recording moments of all the first data, the second data, the third data and the fourth data are identified one by one through the timestamps, and recorded as the first moment, the second moment, the third moment and the fourth moment respectively; The first moment, the second moment, the third moment and the fourth moment are arranged in sequence according to the chronological order, and the first data, the second data, the third data and the fourth data corresponding to the first moment, the second moment, the third moment and the fourth moment at the last position are filtered out and recorded as the landfill type, hazard attribute, safety matter and landfill specification respectively.

3. The AI-based hazardous waste information risk assessment and supervision system according to claim 2 is characterized in that: The steps for calculating the hazard factor are as follows: The numerical parts of landfill type, hazard attribute, safety matter and landfill specification are separated by word segmentation technology to obtain type value, attribute value, matter value and specification value; Split the specification value into an integer part and a decimal part, and perform scaling comparison on the decimal part; When the decimal part of the specification value is greater than 0.45, add 1 to the integer part of the specification value and remove the decimal part of the specification value to obtain the corrected specification value; When the decimal part of the specification value is less than or equal to 0.45, the decimal part of the specification value is eliminated to obtain the corrected specification value; The type value, attribute value, event value and modified specification value are assigned corresponding proportional coefficients in turn to calculate the hazard coefficient; The expression of the hazard coefficient is: ； In the formula, is the hazard factor, is a type value, is the attribute value, is the item value, is the revised specification value, , , , All of them are proportional coefficients greater than 0.

4. The AI-based hazardous waste information risk assessment and supervision system according to claim 3 is characterized in that: The steps for developing an assessment supervision cycle are as follows: Multiply the hazard factor by the standard assessment duration to generate the assessment supervision cycle; The expression for evaluating the supervision cycle is: ； In the formula, To evaluate the regulatory cycle, This is the standard assessment duration.

5. The AI-based hazardous waste information risk assessment and supervision system according to claim 4 is characterized in that: The steps for collecting acid-base fluctuation values ​​are as follows: The closed line of the landfill area is divided equally according to the four directions of top, bottom, left and right to obtain the upper boundary, lower boundary, left boundary and right boundary respectively; The minimum distance from the left boundary to the right boundary is measured horizontally by a scale to obtain the left-right distance value; The minimum distance from the upper boundary to the lower boundary is measured along the vertical direction by using a scale to obtain the upper and lower distance values; Compare the left-right distance value with the up-down distance value, and record one-third of the smallest value after comparison as the standard length; The center point of the landfill area is identified by computer vision technology, and a circle is drawn with the center point as the base point and the standard length as the radius to generate a standard circle; Mark the boundaries of the standard circle at equal distances. detection points and measure them separately through pH sensors The pH value of each test point at the current moment and after an evaluation and supervision cycle is obtained. The original pH value and Real-time pH value; Will The original pH value and The real-time pH values ​​are accumulated and averaged, and the difference between the average values ​​is taken to get the absolute value to generate the pH fluctuation value; The expression of acid-base fluctuation value is: ； In the formula, is the pH fluctuation value, For the Real-time pH value of each detection point, For the The original pH value of each test point.

6. The AI-based hazardous waste information risk assessment and supervision system according to claim 5 is characterized in that: The steps for collecting the concentration rise rate are as follows: Based on the preset evaluation duration, mark B evaluation moments in the evaluation supervision cycle; The VOCs sensor collects the The concentration of volatile organic compounds at each detection point is obtained. concentration value, The concentration values ​​are accumulated and averaged to obtain B real-time concentration values; The expression of real-time concentration value is: ； In the formula, For the The real-time concentration value at the evaluation moment, =1,2...B, For the The first evaluation moment concentration value; The first The real-time concentration value at the evaluation moment is After subtracting the real-time concentration values ​​at the evaluation time, we can obtain concentration difference, and The concentration differences are accumulated and averaged to obtain the sub-climbing value; The expression of the sub-climb value is: ； In the formula, is the sub-climb value, For the Real-time concentration value at each evaluation moment; After comparing the sub-climb value with the preset evaluation time, the concentration climb rate is obtained; The expression of concentration climbing rate is: ； In the formula, is the concentration climbing rate, The preset duration.

7. The AI-based hazardous waste information risk assessment and supervision system according to claim 6 is characterized in that: The steps for collecting oxygen decay rate are as follows: The oxygen concentration sensor is used to collect The oxygen concentration value of each detection point at the current moment and after an evaluation and supervision cycle is obtained. The original concentration value and End concentration value; Will The original concentration value and The final concentration values ​​are accumulated and averaged, and the difference between the two average values ​​is used to generate the oxygen attenuation value; The expression of oxygen attenuation value is: ； In the formula, is the oxygen attenuation value, For the The original concentration value of each detection point, For the The end concentration value of each detection point; Compare the oxygen decay value with the duration corresponding to the assessment supervision cycle to obtain the oxygen decay rate; The expression of oxygen decay rate is: ； In the formula, is the oxygen decay rate, The duration corresponding to the assessment supervision cycle.

8. The AI-based hazardous waste information risk assessment and supervision system according to claim 7 is characterized in that: The training steps of the risk assessment model are as follows: Collect multiple sets of risk summary data in advance when the landfill area is in a risk-free state or a risky state; Mark each group of risk summary data as a training feature, annotate the landfill risk status of each group of training features, convert the risk-free status to 0, and convert the risky status to 1, divide the annotated training features into a training set and a test set, use the training set to train the risk assessment model, and use the test set to test the risk assessment model, preset an error threshold, and output the risk assessment model when the mean of the prediction errors of all training features in the test set is less than the error threshold; The steps to determine whether to issue a risk warning are as follows: When the output of the risk assessment model is 0, the landfill risk status is a risk-free status, and it is determined that no risk warning prompt will be issued; When the output of the risk assessment model is 1, the landfill risk status is a risky state, and a risk warning prompt is issued.

9. The AI-based hazardous waste information risk assessment and supervision system according to claim 8 is characterized in that: The steps for identifying regulatory data are as follows: The abnormalities of pH fluctuation value, concentration climbing rate, temperature value, humidity value and oxygen decay rate were compared in turn; When the acid-base fluctuation value is greater than the acid-base fluctuation threshold, the acid-base fluctuation value is recorded as regulatory data; When the concentration climbing rate is greater than the concentration climbing threshold, the concentration climbing rate is recorded as regulatory data; When the temperature value is not within the temperature standard range, the temperature value is recorded as regulatory data; When the humidity value is not within the humidity standard range, the humidity value is recorded as regulatory data; When the oxygen decay rate is greater than the oxygen decay threshold, the oxygen decay rate is recorded as regulatory data.

10. The AI-based hazardous waste information risk assessment and supervision system according to claim 9 is characterized in that: The risk supervision level includes the primary risk level and the secondary risk level; The steps for establishing the primary and secondary risk levels are as follows: Count the number of regulatory data, and when the number of regulatory data is 1 or 2, formulate a first-level risk level; When the number of regulatory data is 3, 4, or 5, a secondary risk level is developed; Risk management measures include building anti-penetration shielding walls, biochemical soil remediation, soil excavation and replacement, and complete closure of landfill areas; The steps for establishing an impermeable barrier wall, biochemical soil remediation, soil excavation and replacement, and complete closure of the landfill area are as follows: When the risk supervision level is the first-level risk level, measures for building anti-seepage shielding walls, biochemical remediation of soil, and excavation and replacement of soil are formulated; When the risk supervision level is the secondary risk level, measures shall be formulated to completely close the landfill area.

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