An intelligent monitoring system for hazardous waste

Abstract

The present invention provides an intelligent monitoring system for hazardous waste, which relates to the field of intelligent monitoring technology. The system includes a data acquisition module for collecting data in real time using RFID technology, sensors, and video devices; a status acquisition module for evaluating the current status of hazardous waste; a state prediction module for inputting first data into a trained state prediction model to estimate the future state change trend of the hazardous waste, and transmitting the data to a cloud service platform using a data transmission module; an alarm module for determining whether the current hazardous waste assessment result poses a safety risk and issuing an alarm signal; and analyzing the future state change trend of the hazardous waste to achieve intelligent and safe management of hazardous waste. By evaluating the status of hazardous waste data collected in real time, an alarm is immediately issued and emergency processing is carried out if an anomaly is found. Otherwise, the future change trend of the hazardous waste is estimated and analyzed to prevent accidents in advance, thereby achieving intelligent and safe monitoring and management of hazardous waste.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring system for hazardous waste. Background Art

[0002] Nowadays, while the country's economy is developing rapidly, a large amount of hazardous waste is also generated. Due to the long-term lack of a scientific monitoring and management system and supporting treatment technology, the problem of hazardous waste leakage leading to environmental pollution is becoming increasingly serious. Phenomena such as improper disposal of hazardous waste and unauthorized discharge of hazardous waste water are rampant.

[0003] To address the above issues, the present invention provides an intelligent hazardous waste monitoring system that can comprehensively monitor the status of hazardous waste in real time, accurately predict future status change trends of hazardous waste, and implement targeted management measures, thereby avoiding accidents in advance and improving the intelligent, scientific, and safe management level of hazardous waste. Summary of the Invention

[0004] The present invention provides an intelligent monitoring system for hazardous waste, which is used to evaluate the status of hazardous waste data collected in real time. If any abnormality is found, an alarm is immediately issued and emergency treatment is carried out. Otherwise, future trends of hazardous waste changes are predicted and analyzed to prevent accidents in advance, thereby realizing intelligent and safe monitoring and management of hazardous waste.

[0005] The present invention provides an intelligent monitoring system for hazardous waste, comprising:

[0006] Data acquisition module: used to scan the electronic tag attached to the container carrying the hazardous waste using RFID technology to obtain first information, and then collect the current liquid level, pressure and posture data of the hazardous waste collected in real time using the liquid level sensor, pressure sensor and video device to obtain the first data;

[0007] A status acquisition module is configured to evaluate the current status of hazardous waste based on the first data and obtain a first evaluation result;

[0008] A state prediction module is used to train a state prediction model using key information of hazardous waste and corresponding historical state data, and then combine the first data to estimate the future state change trend of the hazardous waste;

[0009] Data transmission module: used for transmitting the first assessment result and the future status change trend of the corresponding hazardous waste to the cloud service platform;

[0010] Alarm module: used to immediately issue a first alarm signal when the cloud service platform determines that there is a safety risk based on the first assessment result of the current hazardous waste;

[0011] Then, based on the analysis of the future status change trend of the hazardous waste by the cloud service platform, a response plan is obtained to achieve intelligent safety management of hazardous waste.

[0012] Preferably, the data acquisition module includes:

[0013] An information identification unit is configured to simultaneously use the ground inventory robot to inventory the labels on the lower layer of hazardous waste containers and activate the ceiling electronic label reading device within its range to read the electronic labels attached to the top and inside of the corresponding hazardous waste containers, and then aggregate the data scanned by the two types of devices to obtain the first information;

[0014] A data acquisition unit is configured to acquire first pressure data in real time using a pressure sensor installed at a hazardous waste residue storage location in the hazardous waste;

[0015] Using a liquid level sensor at a storage location of hazardous waste water in hazardous waste to collect first liquid level data in real time;

[0016] The system uses cameras at preset locations in hazardous waste storage to monitor hazardous waste in real time. A detector containing an object detection algorithm then identifies hazardous waste containers in the images captured by the camera in real time, frames them, and locates them. The system then estimates the pose key points of each frame, ultimately obtaining pose data for the hazardous waste.

[0017] Finally, the first pressure data, the first liquid level data, and the posture data are aggregated with the first information to obtain first data, which is then transmitted to the state acquisition module.

[0018] Preferably, the electronic tag contains the name of the hazardous waste, the hazardous waste code, the volume of the hazardous waste, the optimal storage conditions of the hazardous waste, and the information of the unit that produces the hazardous waste.

[0019] Preferably, the status acquisition module includes:

[0020] A state evaluation unit is configured to analyze the first data, and if the first pressure data is less than a preset pressure threshold of the corresponding hazardous waste residue, evaluate that the hazardous waste residue is currently leaking; otherwise, determine that the current state is normal and output it as one of the contents of the first evaluation result;

[0021] If the first liquid level data is lower than the preset liquid level threshold of the corresponding hazardous wastewater, it is assessed that the hazardous wastewater is currently leaking; otherwise, it is determined that the current state is normal and output as one of the contents of the first assessment result;

[0022] The posture data is matched with preset hazardous waste posture data, and then based on the matching result, it is evaluated whether the current posture of the hazardous waste is abnormal, and the abnormality is output as a content of the first evaluation result.

[0023] Preferably, the state assessment unit includes:

[0024] Comparison block: Based on the posture data of the hazardous waste, and using different posture key points of each hazardous waste container, six different feature planes are constructed and a number of first feature edge vectors are extracted therefrom;

[0025] With the center of the bottom of the hazardous waste container as the origin of the spatial coordinates, and the x-axis and y-axis both in the horizontal direction, the first eigenvector is compared with the corresponding standard edge vector obtained based on the preset hazardous waste posture data to obtain a difference value. The formula for calculating the difference value is as follows:

[0026]

[0027]

[0028] Among them, Y xj It is expressed as the difference between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, and the value range is [0, 1]; B x (θ j ) is represented by the cosine value of the inner product angle between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, and the value range is [0, 1]; θ j It is expressed as the inner product angle between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container; T xj It is represented as the first characteristic edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container; D xj It is represented as the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, where j = 1, 2, 3, ..., m; α is the weight factor of the influence of the cosine value on the difference value; β is the weight factor of the influence of the rotation amplitude of the hazardous waste container on the difference value;

[0029] All the difference values ​​obtained for each hazardous waste are aggregated to obtain several difference groups;

[0030] If all the difference values ​​in the difference values ​​are less than the preset upper threshold, and the proportion of difference values ​​less than the preset lower threshold is not less than 0.7, then it is assessed that the current state of the hazardous waste is normal;

[0031] Otherwise, it is assessed that the current hazardous waste posture is abnormal;

[0032] The current posture assessment result of the hazardous waste is output as the first assessment result.

[0033] Preferably, the prediction status module includes:

[0034] Model building unit: used to train a state prediction model using key information of hazardous waste and corresponding historical state data;

[0035] Estimation unit: used to input the first data within a preset time period into the state prediction model to obtain the liquid level value change trend of hazardous waste water, the pressure value change trend of hazardous waste residue, and the posture data change trend of hazardous waste.

[0036] Preferably, the model building unit includes:

[0037] Data processing block: used to perform dimensionless processing on the extracted historical state data of the preset quantity using the extreme value normalization method to obtain the target historical data;

[0038] Indicator weighting block: It is used to regard liquid level, pressure and posture as evaluation indicators, determine the importance ratio of the weights between two evaluation indicators, and then use the subjective weight formula to obtain the subjective weight coefficient, thereby obtaining the subjective weight of the corresponding evaluation indicator;

[0039] Estimate the information entropy value of each subjective weight, obtain the corresponding objective weight coefficient, and then combine the entropy weight method to obtain the objective weight of the corresponding evaluation indicator;

[0040] The obtained subjective weights are combined with the objective weights to obtain the comprehensive weight value of the evaluation index. The formula for calculating the comprehensive weight value is as follows:

[0041]

[0042] Among them, Q i It is expressed as the comprehensive weight value of the i-th evaluation index, where i = {1, 2, 3, ..., n}; X i Expressed as the subjective weight of the i-th evaluation indicator; Y i Expressed as the objective weight of the i-th evaluation indicator;

[0043] Model building block: It is used to build a state prediction model based on the key information of hazardous waste, using the target historical data as training samples, and then taking the evaluation indicators and the corresponding comprehensive weight values ​​as input.

[0044] Preferably, the alarm module includes:

[0045] Alarm unit: used to analyze the first evaluation result using the cloud service platform. If it is determined that there is a safety risk of leakage or abnormal posture, it will immediately issue a first alarm signal and call the first information of the corresponding hazardous waste to send it to the accident handling personnel for processing;

[0046] If the cloud service platform finds no safety risk after analyzing the first evaluation result, it obtains and analyzes the future state change trend of the corresponding hazardous waste to obtain the initial time when the safety risk will appear for all hazardous wastes in the future and the initial time difference from the current time;

[0047] Determine the current environment of each hazardous waste, and when the current environment is associated with dynamic interference, retrieve several matching environments consistent with the current environment from the historical environment database, and determine the dynamic interference vector for each matching environment;

[0048] Calculate the first interference probability of the dynamic interference vector of each matching environment corresponding to the current environment;

[0049]

[0050] Among them, n i01 represents the number of dynamic interference factors involved in the i01th dynamic interference vector; d i01 G0 represents the number of factors that actually interfere with the corresponding hazardous waste in the i01th dynamic interference vector; i01 represents the first interference probability of the i01th dynamic interference vector;

[0051] Calculating the dynamic interference probability of the corresponding hazardous waste based on all first interference probabilities;

[0052]

[0053] Among them, U1 represents the total number of dynamic interference vectors in the matching environment corresponding to the current environment; p0 i01 It represents the reference value coefficient of the production source of hazardous waste produced in the i01th matching environment corresponding to the current environment; G1 represents the dynamic interference probability;

[0054] When the dynamic interference probability is less than the preset interference probability, retaining the initial moment at which the corresponding hazardous waste will have a safety risk in the future and the initial time difference from the current moment as the corresponding first moment and first time difference;

[0055] When the dynamic interference probability is greater than or equal to the preset interference probability, determining the occurrence probability and concentrated occurrence time of each dynamic interference factor in all corresponding dynamic interference vectors;

[0056] Based on the environment-vector-probability-occurrence time-difference mapping table, the possible interference occurrence time of each dynamic interference factor is matched, and then the earliest interference occurrence time is obtained;

[0057] If the first interference occurrence time is before the initial moment in the future, taking the first interference occurrence time as the first moment and obtaining a first time difference from the current moment;

[0058] Otherwise, continue to use the corresponding future initial moment as the first moment;

[0059] A plan determination unit is configured to compare the obtained first time difference with a preset time threshold. If the time difference is less than the preset time threshold, the corresponding hazardous waste is designated as a primary treatment target, and emergency accident treatment is performed in sequence according to the hazardous waste-time difference sequence obtained by sorting the obtained first time differences from smallest to largest.

[0060] Otherwise, the corresponding hazardous waste will be taken as the secondary treatment target, and the corresponding first information will be called to determine the corresponding chemical characteristics, so as to set different hazard indexes. After combining with the corresponding first time difference to obtain a priority treatment list, the corresponding accident handling measures will be taken to handle them in turn.

[0061] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0062] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 This is a structural diagram of an intelligent monitoring system for hazardous waste in an embodiment of the present invention;

[0065] Figure 2 Schematic diagram of a dynamic interference factor on a time axis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0067] The embodiment of the present invention provides a hazardous waste intelligent monitoring system, such as Figure 1 As shown, including:

[0068] Data acquisition module: used to scan the electronic tag attached to the container carrying the hazardous waste using RFID technology to obtain first information, and then collect the current liquid level, pressure and posture data of the hazardous waste collected in real time using the liquid level sensor, pressure sensor and video device to obtain the first data;

[0069] A status acquisition module is configured to evaluate the current status of hazardous waste based on the first data and obtain a first evaluation result;

[0070] A state prediction module is used to train a state prediction model using key information of hazardous waste and corresponding historical state data, and then combine the first data to estimate the future state change trend of the hazardous waste;

[0071] Data transmission module: used for transmitting the first assessment result and the future status change trend of the corresponding hazardous waste to the cloud service platform;

[0072] Alarm module: used to immediately issue a first alarm signal when the cloud service platform determines that there is a safety risk based on the first assessment result of the current hazardous waste;

[0073] Then, based on the analysis of the future status change trend of the hazardous waste by the cloud service platform, a response plan is obtained to achieve intelligent safety management of hazardous waste.

[0074] In this embodiment, hazardous waste refers to hazardous wastewater and hazardous waste residue; RFID technology refers to a type of automatic identification technology that uses wireless radio frequency to read and write electronic tags to achieve the purpose of target identification and data exchange, wherein the electronic tag is a data carrier; the first information includes the name of the hazardous waste, the hazardous waste code, the volume of the hazardous waste, the optimal storage conditions of the hazardous waste, and the information of the hazardous waste generating unit, which is obtained by scanning the electronic tag attached to the container carrying the hazardous waste using RFID technology.

[0075] In this embodiment, the liquid level sensor is used to collect the liquid level data of hazardous waste water in real time; the pressure sensor is used to collect the pressure data of hazardous waste residue in real time; and the video device is used to obtain the posture data of all hazardous waste in the hazardous waste storage in real time, wherein the posture data refers to the posture information of the hazardous waste container, such as shell expansion, end cover depression, and the number, relative position and angle of posture key points; the first data is mainly composed of the liquid level data of hazardous waste water, the pressure data of hazardous waste residue and the posture data of hazardous waste.

[0076] In this embodiment, the first evaluation result refers to a determination of whether the current state of hazardous waste poses a safety risk, obtained by evaluating data collected in real time by the liquid level sensor, pressure sensor, and video device; the key information primarily refers to the name and code of the hazardous waste; the historical state data refers to a preset amount of historical data of hazardous waste that has previously experienced abnormal states, such as historical posture data immediately before the abnormal state was confirmed; and the state prediction model is a model trained using historical state data for estimating future state change trends of hazardous waste, wherein the future state change trends of hazardous waste primarily refer to future changes in liquid level data or pressure data, as well as posture data, of the current hazardous waste.

[0077] In this embodiment, the cloud service platform refers to a platform used to receive and analyze the current assessment results of hazardous waste transmitted by the data transmission module and the corresponding future state change trends to achieve remote control; the first alarm signal is when the cloud service platform determines that the current assessment results of the hazardous waste status pose a safety risk, combined with the signal sent by the alarm module, to facilitate a rapid response by accident handling personnel; the response plan refers to a treatment plan formulated based on the analysis of the future state change trends of hazardous waste to achieve intelligent and safe monitoring and management of hazardous waste.

[0078] The beneficial effects of the above technical solution are: by evaluating the status of hazardous waste data collected in real time, if there is an abnormality, an alarm will be immediately issued and emergency treatment will be carried out. Otherwise, the future trend of hazardous waste changes will be predicted and analyzed to prevent accidents in advance, thereby realizing intelligent and safe monitoring and management of hazardous waste.

[0079] An embodiment of the present invention provides an intelligent monitoring system for hazardous waste, wherein the data acquisition module includes:

[0080] An information identification unit is configured to simultaneously use the ground inventory robot to inventory the labels on the lower layer of hazardous waste containers and activate the ceiling electronic label reading device within its range to read the electronic labels attached to the top and inside of the corresponding hazardous waste containers, and then aggregate the data scanned by the two types of devices to obtain the first information;

[0081] A data acquisition unit is configured to acquire first pressure data in real time using a pressure sensor installed at a hazardous waste residue storage location in the hazardous waste;

[0082] Using a liquid level sensor at a storage location of hazardous waste water in hazardous waste to collect first liquid level data in real time;

[0083] The system uses cameras at preset locations in hazardous waste storage to monitor hazardous waste in real time. A detector containing an object detection algorithm then identifies hazardous waste containers in the images captured by the camera in real time, frames them, and locates them. The system then estimates the pose key points of each frame, ultimately obtaining pose data for the hazardous waste.

[0084] Finally, the first pressure data, the first liquid level data, and the posture data are aggregated with the first information to obtain first data, which is then transmitted to the state acquisition module.

[0085] In this embodiment, the ground inventory robot refers to a ground device that can autonomously locate and quickly identify electronic tags, and is used to identify the lower label of the hazardous waste container, where the lower label of the hazardous waste container contains the name of the hazardous waste and the hazardous waste code information; the electronic tags attached to the top and inside of the hazardous waste container contain the volume of the hazardous waste, the optimal storage conditions of the hazardous waste, and the information of the hazardous waste generating unit; the ceiling electronic tag reading device refers to a reading device suspended from the ceiling of the storage warehouse, which can improve the accuracy and efficiency of inventory by identifying electronic tags together with the ground inventory robot; the first information includes the name of the hazardous waste, the hazardous waste code, the hazardous waste volume, the optimal storage conditions of the hazardous waste, and the information of the hazardous waste generating unit.

[0086] In this embodiment, the first pressure data refers to the hazardous waste residue pressure value collected in real time; the first liquid level data refers to the hazardous waste water level value collected in real time; the preset position refers to a pre-set position that can fully and accurately monitor all hazardous waste; the target detection algorithm is used to find hazardous waste containers from the images collected in real time by the camera; the target frame is a frame output based on the target monitoring algorithm, which helps to locate the hazardous waste container.

[0087] In this embodiment, the posture key points are used to describe the posture information of the hazardous waste container. The relative positions and angles of the posture key points can be used to infer the posture of the hazardous waste container. The posture data mainly refers to the posture information of the hazardous waste container and the number, relative positions, and angles of the posture key points. The first data refers to the first liquid level data or first pressure data combined with the corresponding hazardous waste posture data and the first hazardous waste information, which is used to evaluate the status of the hazardous waste.

[0088] The beneficial effects of the above technical solution are: by using the ground inventory robot and the ceiling electronic tag reading device to identify the electronic tag affixed to the hazardous waste container, the corresponding first information is obtained; by using sensors and video devices to collect the liquid level, pressure and posture data of the hazardous waste in real time, the first data is obtained; the first information and the first data are transmitted to the status acquisition module to provide data support for the evaluation of the current status of the hazardous waste.

[0089] An embodiment of the present invention provides an intelligent monitoring system for hazardous waste, wherein the status acquisition module includes:

[0090] A state evaluation unit is configured to analyze the first data, and if the first pressure data is less than a preset pressure threshold of the corresponding hazardous waste residue, evaluate that the hazardous waste residue is currently leaking; otherwise, determine that the current state is normal and output it as one of the contents of the first evaluation result;

[0091] If the first liquid level data is lower than the preset liquid level threshold of the corresponding hazardous wastewater, it is assessed that the hazardous wastewater is currently leaking; otherwise, it is determined that the current state is normal and output as one of the contents of the first assessment result;

[0092] The posture data is matched with preset hazardous waste posture data, and then based on the matching result, it is evaluated whether the current posture of the hazardous waste is abnormal, and the abnormality is output as a content of the first evaluation result.

[0093] In this embodiment, the preset pressure threshold and the preset liquid level threshold are set in advance based on the optimal aggregation storage volume of different hazardous waste residues and the optimal aggregation storage volume of different hazardous waste waters, respectively.

[0094] In this embodiment, there are first pressure data a1 and a2, where a1 is less than the preset pressure threshold and a2 is greater than the preset pressure threshold. At this time, it is determined that the hazardous waste residue corresponding to the first pressure data a1 has leaked, and the hazardous waste residue corresponding to the first pressure data a2 is in normal condition, and is output as the first evaluation result.

[0095] In this embodiment, the preset hazardous waste posture data is set in advance based on the distance between hazardous waste containers and the size of hazardous waste containers, such as the preset posture key point angles; the posture data refers to the posture information of the hazardous waste container and the number, relative positions and angles of the posture key points.

[0096] The beneficial effect of the above technical solution is: by evaluating the current state of hazardous waste based on the first data, a judgment result of whether there is a leak and whether the posture is normal is obtained and output as the first evaluation result, providing a basis for whether to issue an alarm subsequently, which is conducive to realizing intelligent and safe monitoring and management of hazardous waste.

[0097] An embodiment of the present invention provides an intelligent monitoring system for hazardous waste, wherein the status assessment unit includes:

[0098] Comparison block: Based on the posture data of the hazardous waste, and using different posture key points of each hazardous waste container, six different feature planes are constructed and a number of first feature edge vectors are extracted therefrom;

[0099] With the center of the bottom of the hazardous waste container as the origin of the spatial coordinates, and the x-axis and y-axis both in the horizontal direction, the first eigenvector is compared with the corresponding standard edge vector obtained based on the preset hazardous waste posture data to obtain a difference value. The formula for calculating the difference value is as follows:

[0100]

[0101]

[0102] Among them, Y xj It is expressed as the difference between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, and the value range is [0, 1]; B x (θ j ) is represented by the cosine value of the inner product angle between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, and the value range is [0, 1]; θ j It is expressed as the inner product angle between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container; T xj It is represented as the first characteristic edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container; D xj It is represented as the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, where j = 1, 2, 3, ..., m; α is the weight factor of the influence of the cosine value on the difference value; β is the weight factor of the influence of the rotation amplitude of the hazardous waste container on the difference value;

[0103] All the difference values ​​obtained for each hazardous waste are aggregated to obtain several difference groups;

[0104] If all the difference values ​​in the difference values ​​are less than the preset upper threshold, and the proportion of difference values ​​less than the preset lower threshold is not less than 0.7, then it is assessed that the current state of the hazardous waste is normal;

[0105] Otherwise, it is assessed that the current hazardous waste posture is abnormal;

[0106] The current posture assessment result of the hazardous waste is output as the first assessment result.

[0107] In this embodiment, the feature plane is a plane constructed based on the posture key points in the manner of forming a plane with three points; the first feature edge vector corresponds to the vector of the feature plane; the standard edge vector is a vector of the feature plane obtained by forming a plane with three points based on the posture key points in the preset hazardous waste posture data; the cosine value refers to the value of the cosine of the angle between the inner product of the first feature edge vector and the standard edge vector, which can be used to judge the similarity of the displacement of the hazardous waste container; the difference group refers to the set of difference values ​​between the first feature edge vector and the corresponding standard vector corresponding to all feature planes of the hazardous waste.

[0108] In this embodiment, for example, there are hazardous wastes 1 and 2, and their corresponding difference groups are C1 and C2, respectively. It is known that in difference group C1, there is one difference value greater than the preset high threshold, and the rest are less than the preset high threshold; in difference group C2, there is no difference value greater than the preset high threshold, and the proportion of difference values ​​less than the preset low threshold is 0.6. At this time, it is assessed that the postures of hazardous wastes 1 and 2 are both abnormal, and this is output as the first assessment result.

[0109] The beneficial effect of the above technical solution is: by using the feature plane similarity matching method to compare the hazardous waste posture data with the preset hazardous waste posture data to evaluate the current hazardous waste status, it is effectively realized to accurately judge whether there is any abnormality in the current hazardous waste container posture, thereby realizing intelligent management of hazardous waste.

[0110] An embodiment of the present invention provides an intelligent monitoring system for hazardous waste, wherein the prediction status module includes:

[0111] Model building unit: used to train a state prediction model using key information of hazardous waste and corresponding historical state data;

[0112] Estimation unit: used to input the first data within a preset time period into the state prediction model to obtain the liquid level value change trend of hazardous waste water, the pressure value change trend of hazardous waste residue, and the posture data change trend of hazardous waste.

[0113] In this embodiment, the key information refers to the name and code of the hazardous waste; the historical status data refers to a preset amount of historical data of hazardous waste that has produced abnormal status, such as historical posture data at the moment before the abnormal status is confirmed; the preset time period is set in advance based on the historical status data and the size of the first data volume; and the status prediction model is used to estimate the future status change trend of the current hazardous waste.

[0114] The beneficial effect of the above technical solution is: by inputting the first data into the state prediction model trained using historical state data, and then combining it with the key information of hazardous waste, the future state change trend of the hazardous waste is estimated, thereby avoiding accidents in advance and realizing intelligent management of hazardous waste.

[0115] An embodiment of the present invention provides an intelligent monitoring system for hazardous waste, wherein the model building unit includes:

[0116] Data processing block: used to perform dimensionless processing on the extracted historical state data of the preset quantity using the extreme value normalization method to obtain the target historical data;

[0117] Indicator weighting block: It is used to regard liquid level, pressure and posture as evaluation indicators, determine the importance ratio of the weights between two evaluation indicators, and then use the subjective weight formula to obtain the subjective weight coefficient, thereby obtaining the subjective weight of the corresponding evaluation indicator;

[0118] Estimate the information entropy value of each subjective weight, obtain the corresponding objective weight coefficient, and then combine the entropy weight method to obtain the objective weight of the corresponding evaluation indicator;

[0119] The obtained subjective weights are combined with the objective weights to obtain the comprehensive weight value of the evaluation index. The formula for calculating the comprehensive weight value is as follows:

[0120]

[0121] Among them, Q i It is expressed as the comprehensive weight value of the i-th evaluation index, where i = {1, 2, 3, ..., n}; X i Expressed as the subjective weight of the i-th evaluation indicator; Y i Expressed as the objective weight of the i-th evaluation indicator;

[0122] Model building block: It is used to build a state prediction model based on the key information of hazardous waste, using the target historical data as training samples, and then taking the evaluation indicators and the corresponding comprehensive weight values ​​as input.

[0123] In this embodiment, the purpose of using extreme value normalization to process historical status data is to perform dimensionless processing to avoid calculation errors due to different data types and dimensions; the target historical data is obtained based on extreme value normalization of historical status data of preset quantities; the preset quantities are set in advance.

[0124] In this embodiment, liquid level, pressure and posture are regarded as evaluation indicators because the state estimation of hazardous waste is the result of the joint action of three factors: liquid level, pressure and posture; information entropy is used to solve the problem of information quantification, and the information entropy value can be obtained by calculating the originally vague information concept; the entropy weight method is to calculate the entropy weight of each evaluation indicator based on the degree of variation of each evaluation indicator using information entropy, and then correct the weight of each evaluation indicator by entropy weight to obtain the objective weight; the comprehensive weight value is obtained by combining subjective weight with objective weight, which can make up for the shortcomings of single weighting and make the result more scientific, which is beneficial to the accurate prediction of the state prediction model.

[0125] The beneficial effects of the above technical solution are: based on the key information of hazardous waste, and using the historical status data after extreme value standardization as training samples, the comprehensive weight value obtained by weighting the evaluation indicators with the combination of subjective and objective weights and the corresponding evaluation indicators are used as input to establish a status prediction model, which provides support for the subsequent accurate estimation of the future status change trend of the current hazardous waste.

[0126] An embodiment of the present invention provides an intelligent monitoring system for hazardous waste, wherein the alarm module includes:

[0127] Alarm unit: used to analyze the first evaluation result using the cloud service platform. If it is determined that there is a safety risk of leakage or abnormal posture, it will immediately issue a first alarm signal and call the first information of the corresponding hazardous waste to send it to the accident handling personnel for processing;

[0128] If the cloud service platform finds no safety risk after analyzing the first evaluation result, it obtains and analyzes the future state change trend of the corresponding hazardous waste to obtain the initial time when the safety risk will appear for all hazardous wastes in the future and the initial time difference from the current time;

[0129] Determine the current environment of each hazardous waste, and when the current environment is associated with dynamic interference, retrieve several matching environments consistent with the current environment from the historical environment database, and determine the dynamic interference vector for each matching environment;

[0130] Calculate the first interference probability of the dynamic interference vector of each matching environment corresponding to the current environment;

[0131]

[0132] Among them, n i01 represents the number of dynamic interference factors involved in the i01th dynamic interference vector; d i01 G0 represents the number of factors that actually interfere with the corresponding hazardous waste in the i01th dynamic interference vector; i01 represents the first interference probability of the i01th dynamic interference vector;

[0133] Calculating the dynamic interference probability of the corresponding hazardous waste based on all first interference probabilities;

[0134]

[0135] Among them, U1 represents the total number of dynamic interference vectors in the matching environment corresponding to the current environment; p0 i01 It represents the reference value coefficient of the production source of hazardous waste produced in the i01th matching environment corresponding to the current environment; G1 represents the dynamic interference probability;

[0136] When the dynamic interference probability is less than the preset interference probability, retaining the initial moment at which the corresponding hazardous waste will have a safety risk in the future and the initial time difference from the current moment as the corresponding first moment and first time difference;

[0137] When the dynamic interference probability is greater than or equal to the preset interference probability, the occurrence probability and concentrated occurrence time of each dynamic interference factor in all corresponding dynamic interference vectors are determined, wherein the schematic diagram of the dynamic interference factor on the time axis is as follows: Figure 2 As shown, each circle is a corresponding dynamic interference factor, and the position based on the time axis is the corresponding occurrence time;

[0138] Based on the environment-vector-probability-occurrence time-difference mapping table, the possible interference occurrence time of each dynamic interference factor is matched, and then the earliest interference occurrence time is obtained;

[0139] If the first interference occurrence time is before the initial moment in the future, taking the first interference occurrence time as the first moment and obtaining a first time difference from the current moment;

[0140] Otherwise, continue to use the corresponding future initial moment as the first moment;

[0141] A plan determination unit is configured to compare the obtained first time difference with a preset time threshold. If the time difference is less than the preset time threshold, the corresponding hazardous waste is designated as a primary treatment target, and emergency accident treatment is performed in sequence according to the hazardous waste-time difference sequence obtained by sorting the obtained first time differences from smallest to largest.

[0142] Otherwise, the corresponding hazardous waste will be taken as the secondary treatment target, and the corresponding first information will be called to determine the corresponding chemical characteristics, so as to set different hazard indexes. After combining with the corresponding first time difference to obtain a priority treatment list, the corresponding accident handling measures will be taken to handle them in turn.

[0143] In this embodiment, the cloud service platform refers to a platform for receiving and analyzing the current evaluation results of hazardous waste transmitted by the data transmission module and the corresponding future state change trends to achieve remote control; the first evaluation result refers to the judgment result of whether there is a safety risk in the current state of the hazardous waste, obtained by evaluating the data collected in real time by the liquid level sensor, pressure sensor, and video device; the first time difference is the difference between the time when the first evaluation result of the current hazardous waste is obtained and the time when the state of the hazardous waste becomes abnormal in the future; the hazardous waste-time difference sequence is constructed by the hazardous waste name and the corresponding first time difference.

[0144] In this embodiment, the current environment refers to the current location of the hazardous waste, for example, it can be an abandoned factory, or next to a river, or a waste recycling station, etc. Due to the different environments in which hazardous waste is located, there will be inevitable dynamic interference. For example, if a person accidentally touches the hazardous waste, causing it to fall to the ground, there will be a safety risk. For example, next to a river, the container of liquid hazardous waste may be filled due to rain or other weather conditions, resulting in liquid leakage, etc. Therefore, to obtain the dynamic interference factors that exist in the same historical situation as the scenario, whether it is intentional or unintentional, any factors that may eventually cause the current situation of the hazardous waste to change are regarded as dynamic interference factors, that is, they are regarded as being related to dynamic interference.

[0145] In this embodiment, the historical environment database includes interference vectors for hazardous wastes under different environments, and the interference vectors are composed of several different dynamic interference factors.

[0146] In this embodiment, the preset interference probability value is 0.5.

[0147] In this embodiment, the occurrence probability refers to the ratio of the number of occurrences of the same dynamic interference factor in the dynamic interference vectors corresponding to several matching environments to the number of dynamic interference vectors, and the same dynamic interference factor appears at most once in each dynamic interference vector.

[0148] For example, dynamic interference vectors 1, 2, 3, 4, and 5, among which dynamic interference factor 1 appears in dynamic interference vectors 1 and 2, but not in the other vectors. At this time, the probability of occurrence is: 2 / 5.

[0149] In this embodiment, the concentrated appearance time refers to the appearance time of each dynamic interference factor in the corresponding dynamic interference vector, and the time when the dynamic interference factors appear together is the concentrated appearance time.

[0150] For example, draw a time point diagram of the appearance time of the same dynamic interference factor in different dynamic interference vectors, and take the average value of the time of the concentrated part as the concentrated appearance time, such as Figure 2 As shown, the q1 part represents the time range of concentrated occurrence, and the average value of all times within the range is calculated to obtain the concentrated occurrence time.

[0151] In this embodiment, the environment-vector-probability-occurrence time-difference mapping table includes: the current environment, the actual interference vector of the matching environment corresponding to the current environment, the probability of occurrence of each dynamic interference factor, the concentrated occurrence time, and the possible interference time of the matching dynamic interference factor.

[0152] For example, the possible interference time of the obtained dynamic interference factor 1 is time u01. At this time, time u01 is before the initial time when the security risk is predicted to occur in the future, and time u01 is taken as the first time.

[0153] In this embodiment, the preset time threshold is set in advance based on the optimal pre-processing time period for hazardous waste.

[0154] In this embodiment, for example, there are hazardous wastes 1, 2, and 3, and the corresponding first time differences are t1, t2, and t3, respectively. At this time, the first time differences t1, t2, and t3 are compared with the preset time thresholds in sequence, and it is obtained that the first time differences t1 and t2 are less than the preset time thresholds, and the first time difference t1 is less than t2, and the first time difference t3 is greater than the preset time threshold. Therefore, at this time, hazardous wastes 1 and 2 are taken as primary treatment targets, and hazardous waste 1 is treated first before hazardous waste 2, and hazardous waste 3 is taken as a secondary treatment target.

[0155] In this embodiment, the hazard index is set based on the chemical characteristics of different hazardous wastes, where the chemical characteristics refer to the generation of other substances in the hazardous wastes that are the secondary treatment targets, such as color change, release of harmful gases, and sometimes accompanied by energy changes, such as heat release and luminescence. The priority treatment list is obtained by combining the first time difference with the hazard index of the corresponding hazardous waste and sorting them in ascending order.

[0156] The beneficial effects of the above technical solution are: by using the cloud service platform to analyze the first evaluation results, if there is an abnormality, an alarm signal will be issued to alarm, and the accident handling personnel will immediately deal with it urgently. If there is no abnormality, the future state change trend of the hazardous waste will be analyzed, and the possible dangerous moments will be dynamically analyzed according to the dynamic interference factors that may exist in the environment where the hazardous waste is located, effectively ensuring the timely treatment of hazardous waste, and taking targeted measures to deal with them in sequence according to the obtained priority list of hazardous waste accidents, effectively realizing the intelligent and safe monitoring and management of hazardous waste.

[0157] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An intelligent monitoring system for hazardous waste, characterized in that: include: Data acquisition module: used to scan the electronic tag attached to the container carrying the hazardous waste using RFID technology to obtain first information, and then collect the current liquid level, pressure and posture data of the hazardous waste collected in real time using the liquid level sensor, pressure sensor and video device to obtain the first data; A status acquisition module is configured to evaluate the current status of hazardous waste based on the first data and obtain a first evaluation result; A state prediction module is used to train a state prediction model using key information of hazardous waste and corresponding historical state data, and then combine the first data to estimate the future state change trend of the hazardous waste; Data transmission module: used for transmitting the first assessment result and the future status change trend of the corresponding hazardous waste to the cloud service platform; Alarm module: used to immediately issue a first alarm signal when the cloud service platform determines that there is a safety risk based on the first assessment result of the current hazardous waste; Then, based on the analysis of the future status change trend of the hazardous waste by the cloud service platform, a response plan is obtained to achieve intelligent safety management of hazardous waste; The key information refers to the name and code of hazardous waste; The state acquisition module includes a state evaluation unit; The state assessment unit comprises: Comparison module: Based on the posture data of the hazardous waste and using different posture key points of each hazardous waste container, six different feature planes are constructed and a plurality of first feature edge vectors are extracted therefrom; With the center of the bottom of the hazardous waste container as the origin of the spatial coordinates, and the x-axis and y-axis both in the horizontal direction, the first characteristic edge vector is compared with the corresponding standard edge vector obtained based on the preset hazardous waste posture data to obtain a difference value. The formula for calculating the difference value is as follows: ； ； Among them, Y xj It is expressed as the difference between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, and the value range is [0, 1]; B x (θ j ) is represented by the cosine value of the inner product angle between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, and the value range is [0, 1]; θ j It is expressed as the inner product angle between the first characteristic edge vector and the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container; T xj It is represented as the first characteristic edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container; D xj It is represented as the standard edge vector corresponding to the j-th characteristic plane of the x-th hazardous waste container, where j = 1, 2, 3, ..., m; α is the weight factor of the influence of the cosine value on the difference value; β is the weight factor of the influence of the rotation amplitude of the hazardous waste container on the difference value; All the difference values ​​obtained for each hazardous waste are aggregated to obtain several difference groups; If all the difference values ​​in the difference values ​​are less than the preset upper threshold, and the proportion of difference values ​​less than the preset lower threshold is not less than 0.7, then it is assessed that the current state of the hazardous waste is normal; Otherwise, it is assessed that the current hazardous waste posture is abnormal; The current posture assessment result of the hazardous waste is output as the first assessment result.

2. The intelligent monitoring system for hazardous waste according to claim 1, characterized in that: The data acquisition module includes: An information identification unit is configured to simultaneously use the ground inventory robot to inventory the labels on the lower layer of hazardous waste containers and activate the ceiling electronic label reading device within its range to read the electronic labels attached to the top and inside of the corresponding hazardous waste containers, and then aggregate the data scanned by the two types of devices to obtain the first information; A data acquisition unit is configured to acquire first pressure data in real time using a pressure sensor installed at a hazardous waste residue storage location in the hazardous waste; Using a liquid level sensor at a hazardous waste water storage location in the hazardous waste to collect first liquid level data in real time; The system uses cameras at preset locations in hazardous waste storage to monitor hazardous waste in real time. A detector containing an object detection algorithm then identifies hazardous waste containers in the images captured by the camera in real time, frames them, and locates them. The system then estimates the pose key points of each frame, ultimately obtaining pose data for the hazardous waste. Finally, the first pressure data, the first liquid level data, and the posture data are aggregated with the first information to obtain first data, which is then transmitted to the state acquisition module.

3. The intelligent monitoring system for hazardous waste according to claim 1, characterized in that: The electronic tag contains the name of the hazardous waste, the hazardous waste code, the volume of the hazardous waste, the optimal storage conditions of the hazardous waste, and the information of the unit that produces the hazardous waste.

4. The intelligent monitoring system for hazardous waste according to claim 2, characterized in that: The state evaluation unit is configured to analyze the first data and, if the first pressure data is less than a preset pressure threshold corresponding to the hazardous waste residue, evaluate that leakage of the hazardous waste residue has occurred; otherwise, determine that the current state is normal and output the result as one of the first evaluation results; If the first liquid level data is lower than the preset liquid level threshold of the corresponding hazardous wastewater, it is assessed that the hazardous wastewater is currently leaking; otherwise, it is determined that the current state is normal and output as one of the contents of the first assessment result; The posture data is matched with preset hazardous waste posture data, and then based on the matching result, it is evaluated whether the current posture of the hazardous waste is abnormal, and the abnormality is output as a content of the first evaluation result.

5. The intelligent monitoring system for hazardous waste according to claim 1, characterized in that: The prediction status module includes: Model building unit: used to train a state prediction model using key information of hazardous waste and corresponding historical state data; Estimation unit: used to input the first data within a preset time period into the state prediction model to obtain the liquid level value change trend of hazardous waste water, the pressure value change trend of hazardous waste residue, and the posture data change trend of hazardous waste.

6. The intelligent monitoring system for hazardous waste according to claim 5, characterized in that: The model building unit includes: Data processing module: used to perform dimensionless processing on the extracted historical state data of the preset quantity using the extreme value normalization method to obtain the target historical data; Indicator weighting module: It is used to regard liquid level, pressure and posture as evaluation indicators, determine the importance ratio between each evaluation indicator, and then use the subjective weight formula to obtain the subjective weight coefficient, thereby obtaining the subjective weight of the corresponding evaluation indicator; Estimate the information entropy value of each subjective weight, obtain the corresponding objective weight coefficient, and then combine the entropy weight method to obtain the objective weight of the corresponding evaluation indicator; The obtained subjective weights are combined with the objective weights to obtain the comprehensive weight value of the evaluation index. The formula for calculating the comprehensive weight value is as follows: ； Among them, Q i It is expressed as the comprehensive weight value of the i-th evaluation index, where i = {1, 2, 3, ..., n}; X i Expressed as the subjective weight of the i-th evaluation indicator; Y i Expressed as the objective weight of the i-th evaluation indicator; Model building module: It is used to build a state prediction model based on the key information of hazardous waste, using the target historical data as training samples, and then taking the evaluation indicators and the corresponding comprehensive weight values ​​as input.

7. The intelligent monitoring system for hazardous waste according to claim 1, characterized in that: The alarm module includes: An alarm unit is configured to analyze the first assessment result using a cloud service platform. If it is determined that there is a safety risk of leakage or abnormal posture, a first alarm signal is immediately issued, and the first information of the corresponding hazardous waste is retrieved and sent to the accident handling personnel for processing; If the cloud service platform finds no safety risk after analyzing the first assessment result, it obtains and analyzes the future state change trend of the corresponding hazardous waste to obtain the initial time when the safety risk of the hazardous waste will appear in the future and the initial time difference from the current time; Determine the current environment of each hazardous waste, and when the current environment is associated with dynamic interference, retrieve several matching environments consistent with the current environment from the historical environment database, and determine the dynamic interference vector for each matching environment; Calculate the first interference probability of the dynamic interference vector of each matching environment corresponding to the current environment; ； Among them, n i01 represents the number of dynamic interference factors involved in the i01th dynamic interference vector; d i01 G0 represents the number of factors that actually interfere with the corresponding hazardous waste in the i01th dynamic interference vector; i01 represents the first interference probability of the i01th dynamic interference vector; Calculating the dynamic interference probability of the corresponding hazardous waste based on all first interference probabilities; ； Among them, U1 represents the total number of dynamic interference vectors in the matching environment corresponding to the current environment; p0 i01 Indicates the reference value coefficient of the production source of hazardous waste produced in the i01th matching environment corresponding to the current environment; G1 represents the dynamic interference probability; When the dynamic interference probability is less than the preset interference probability, retaining the initial moment at which the corresponding hazardous waste will have a safety risk in the future and the initial time difference from the current moment as the corresponding first moment and first time difference; When the dynamic interference probability is greater than or equal to the preset interference probability, determining the occurrence probability and concentrated occurrence time of each dynamic interference factor in all corresponding dynamic interference vectors; Based on the environment-vector-probability-occurrence time-difference mapping table, the possible interference occurrence time of each dynamic interference factor is matched, and then the earliest interference occurrence time is obtained; If the first interference occurrence time is before the initial moment in the future, taking the first interference occurrence time as the first moment and obtaining a first time difference from the current moment; Otherwise, continue to use the corresponding future initial moment as the first moment; A plan determination unit is configured to compare the obtained first time difference with a preset time threshold. If the time difference is less than the preset time threshold, the corresponding hazardous waste is designated as a primary treatment target, and emergency accident treatment is performed in sequence according to the hazardous waste-time difference sequence obtained by sorting the obtained first time differences from smallest to largest. Otherwise, the corresponding hazardous waste will be taken as the secondary treatment target, and the corresponding first information will be called to determine the corresponding chemical characteristics, so as to set different hazard indexes. After combining with the corresponding first time difference to obtain a priority treatment list, the corresponding accident handling measures will be taken to handle them in turn.

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