CN112333655A - Dangerous chemical risk identification early warning method and system based on artificial intelligence - Google Patents

Dangerous chemical risk identification early warning method and system based on artificial intelligence Download PDF

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CN112333655A
CN112333655A CN202011190504.6A CN202011190504A CN112333655A CN 112333655 A CN112333655 A CN 112333655A CN 202011190504 A CN202011190504 A CN 202011190504A CN 112333655 A CN112333655 A CN 112333655A
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data
early warning
artificial intelligence
wireless sensor
concentration
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王伟宗
黄炳裕
王孝文
林文国
洪章阳
吴迎晖
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Evecom Information Technology Development Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/12—Alarms for ensuring the safety of persons responsive to undesired emission of substances, e.g. pollution alarms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/122—Shortest path evaluation by minimising distances, e.g. by selecting a route with minimum of number of hops
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership

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  • General Health & Medical Sciences (AREA)
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  • Business, Economics & Management (AREA)
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Abstract

The invention provides a dangerous chemical risk identification early warning method based on artificial intelligence, which comprises the following steps: s1, acquiring monitoring data of the hazardous chemical substances, and transmitting the monitoring data to a cloud server; s2, acquiring the monitoring data from the cloud service, inputting the monitoring data into a trained neural network model, and judging whether the state of the hazardous chemical substance is abnormal or not through the neural network model; and S3, if the state of the dangerous chemical substance is abnormal, sending an early warning prompt to related workers. On the other hand, the invention provides an artificial intelligence-based dangerous chemical risk identification and early warning system, which is used for an artificial intelligence-based dangerous chemical risk identification and early warning method. The invention realizes the real-time monitoring of dangerous chemicals. Compared with the traditional manual regular inspection, the method and the system can find the abnormal state of the hazardous chemical substances in time and inform related workers of processing in time.

Description

Dangerous chemical risk identification early warning method and system based on artificial intelligence
Technical Field
The invention relates to the field of early warning, in particular to a dangerous chemical risk identification early warning method and system based on artificial intelligence.
Background
Hazardous chemicals refer to highly toxic chemicals and other chemicals which have the properties of toxicity, corrosion, explosion, combustion supporting and the like and are harmful to human bodies, facilities and the environment. Because of the danger of hazardous chemicals, careful storage and management of hazardous chemicals are required. In the prior art, dangerous chemicals stored in a warehouse are monitored generally in a manual regular inspection mode, and the dangerous chemicals are not easy to find out in time.
Disclosure of Invention
In view of the foregoing problems, the present invention provides a dangerous chemical risk identification and early warning method and system based on artificial intelligence.
On the one hand, the invention provides a dangerous chemical risk identification and early warning method based on artificial intelligence, which comprises the following steps:
s1, acquiring monitoring data of the hazardous chemical substances, and transmitting the monitoring data to a cloud server;
s2, acquiring the monitoring data from the cloud service, inputting the monitoring data into a trained neural network model, and judging whether the state of the hazardous chemical substance is abnormal or not through the neural network model;
and S3, if the state of the dangerous chemical substance is abnormal, sending an early warning prompt to related workers.
Preferably, the acquiring of monitoring data of the hazardous chemical comprises:
and acquiring monitoring data of the hazardous chemical substances through a wireless sensor network and a monitoring camera which are arranged in the warehouse.
Preferably, the monitoring data comprises gas concentration data, fire fighting data and monitoring data; the gas concentration data comprises the concentration of combustible gas and the concentration of toxic gas in a hazardous chemical storage environment; the fire fighting data comprises the temperature, the humidity and the smoke concentration of the storage environment of the hazardous chemical; the monitoring data comprises monitoring videos of the storage environment of the hazardous chemicals.
Preferably, the combustible gas concentration includes a hydrogen concentration, an acetylene concentration, an ethylene concentration, and a hydrogen sulfide concentration; the toxic gas concentration includes formaldehyde concentration, hydrogen chloride concentration, and sulfur dioxide concentration.
Preferably, the monitoring data of the hazardous chemical substances are obtained through a wireless sensor network and a monitoring camera which are arranged in the warehouse, and the method comprises the following steps:
acquiring gas concentration data and fire fighting data through the wireless sensor network; and acquiring monitoring data through the monitoring camera.
Preferably, the wireless sensor network comprises wireless sensor nodes and base stations;
the wireless sensor node is used for acquiring gas concentration data and fire-fighting data and transmitting the gas concentration data and the fire-fighting data to the base station;
the base station is used for receiving the gas concentration data and the fire fighting data and sending the gas concentration data and the fire fighting data to a cloud server.
Preferably, the base station is further configured to divide the wireless sensor nodes into member nodes and cluster head nodes with a fixed time period;
the member nodes are used for acquiring the gas concentration data and the fire fighting data and sending the gas concentration data and the fire fighting data to the cluster head nodes, and the cluster head nodes are used for sending the gas concentration data and the fire fighting data to the base station.
On the other hand, the invention provides an artificial intelligence based dangerous chemical risk identification and early warning system, which is used for realizing the artificial intelligence based dangerous chemical risk identification and early warning method.
Compared with the prior art, the invention has the advantages that:
the monitoring data of the hazardous chemical substance is acquired through the wireless sensor network and the monitoring camera, the monitoring data is transmitted to the cloud server, the monitoring data is acquired from the cloud server, whether the hazardous chemical substance is abnormal or not is judged, and real-time monitoring of the hazardous chemical substance is achieved. Compared with the traditional manual regular inspection, the method and the system can find the abnormal state of the hazardous chemical substances in time and inform related workers of processing in time.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram illustrating an exemplary embodiment of a risk identification and early warning method for hazardous chemical substances based on artificial intelligence according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The invention provides a dangerous chemical risk identification early warning method and system based on artificial intelligence.
On the one hand, the invention provides a dangerous chemical risk identification and early warning method based on artificial intelligence, which comprises the following steps:
s1, acquiring monitoring data of the hazardous chemical substances, and transmitting the monitoring data to a cloud server;
s2, acquiring the monitoring data from the cloud service, inputting the monitoring data into a trained neural network model, and judging whether the state of the hazardous chemical substance is abnormal or not through the neural network model;
and S3, if the state of the dangerous chemical substance is abnormal, sending an early warning prompt to related workers.
In one embodiment, acquiring monitoring data of hazardous chemicals comprises:
and acquiring monitoring data of the hazardous chemical substances through a wireless sensor network and a monitoring camera which are arranged in the warehouse.
In one embodiment, the monitoring data includes gas concentration data, fire data, and monitoring data; the gas concentration data comprises the concentration of combustible gas and the concentration of toxic gas in a hazardous chemical storage environment; the fire fighting data comprises the temperature, the humidity and the smoke concentration of the storage environment of the hazardous chemical; the monitoring data comprises monitoring videos of the storage environment of the hazardous chemicals.
In one embodiment, the combustible gas concentration includes a hydrogen concentration, an acetylene concentration, an ethylene concentration, and a hydrogen sulfide concentration; the toxic gas concentration includes formaldehyde concentration, hydrogen chloride concentration, and sulfur dioxide concentration.
In one embodiment, acquiring monitoring data of hazardous chemical substances through a wireless sensor network and a monitoring camera arranged in a warehouse comprises:
acquiring gas concentration data and fire fighting data through the wireless sensor network; and acquiring monitoring data through the monitoring camera.
In one embodiment, the wireless sensor network comprises a wireless sensor node and a base station;
the wireless sensor node is used for acquiring gas concentration data and fire-fighting data and transmitting the gas concentration data and the fire-fighting data to the base station;
the base station is used for receiving the gas concentration data and the fire fighting data and sending the gas concentration data and the fire fighting data to a cloud server.
In one embodiment, the base station is further configured to divide the wireless sensor nodes into member nodes and cluster head nodes with a fixed time period;
the member nodes are used for acquiring the gas concentration data and the fire fighting data and sending the gas concentration data and the fire fighting data to the cluster head nodes, and the cluster head nodes are used for sending the gas concentration data and the fire fighting data to the base station.
The clustering of the wireless sensor nodes is not constant, and clustering is performed again at intervals of a preset time period, so that the situation that some sensor nodes consume energy in advance due to the fact that the forwarding tasks are too heavy is avoided.
In one embodiment, the sending of the warning prompt to the related staff comprises:
and carrying out early warning prompt on related workers in a text prompt and sound prompt mode.
In one embodiment, the base station divides the wireless sensor nodes into member nodes and cluster head nodes by:
the base station broadcasts a clustering message to the wireless sensor nodes;
the base station receives a feedback message of the wireless sensor node;
the base station calculates the competitiveness of each sensor node according to the feedback message;
the base station selects ctnum wireless sensor nodes with the competitive ranking as candidate nodes;
optimizing the candidate nodes to obtain cluster head nodes, taking the wireless sensor nodes except the cluster head nodes as member nodes, and dividing the member nodes into clusters where the cluster head nodes are located according to the principle of minimum distance;
and the base station broadcasts the clustering result to all the wireless sensor nodes.
In one embodiment, the feedback message includes a remaining energy of the wireless sensor node, an initial energy, a set of neighboring nodes, an average number of hops to communicate with the base station, and coordinates of the wireless sensor node.
In one embodiment, the base station calculates the competitiveness of each wireless sensor node by:
Figure DEST_PATH_IMAGE001
let the wireless sensor node be a, in the formula, compaDenotes the competitiveness of a, α1And alpha2Representing a preset weight control parameter, alpha1∈(0,1)，csnlaAnd leftnlaRespectively representing the initial energy and the residual energy of a, timeaIndicates the continuous working time of a, jultbsaIndicates the average hop count of communication between the wireless sensor node a and the base station, neiU indicates the collection of other wireless sensor nodes within the maximum communication radius of the wireless sensor node a, jultbsbRepresenting the average hop count of communication between the wireless sensor node b in the neiU and the base station; neinaIndicates the total number of elements in the neiU, txraAnd j udge (a) is a judgment function, if a is already used as a cluster head node in the previous round of clustering, j udge (a) ═ cs, cs is a preset constant parameter, and if a is not used as a cluster head node in the previous round of clustering, j udge (a) ═ cs.
In the above embodiment of the present invention, the competitiveness of the wireless sensor node is calculated according to the initial energy of the wireless sensor node, the continuous operating time of the remaining energy, the average hop count for communication between the base stations, the distribution of other wireless sensor nodes within the maximum communication radius, and other parameters, which is favorable for selecting the neighboring nodes with lower power consumption and closer to the base stations and with densely distributed neighboring nodes, and the cluster head node is not used in the previous round of clustering, and the wireless sensor nodes with the neighbor nodes which are integrally far away from the base station are used as cluster head nodes, so that the wireless sensor nodes with less cluster head nodes can be used for covering more member nodes, the energy of all the wireless sensor nodes can be utilized uniformly on the whole, the phenomenon that the single wireless sensor node consumes the energy quickly due to the fact that the transmission task is too heavy is avoided, the service life of a wireless sensor node network can be prolonged, and the coverage range of the wireless sensor node network can be ensured.
In one embodiment, the parameter ctnum is determined by:
recording the area of the hazardous chemical storage area as ccSThe theoretical maximum ctnum of ctnum is calculated by the following formulama：
Figure DEST_PATH_IMAGE002
Wherein maR represents the maximum communication radius of the wireless sensor node;
setting the candidate value of the parameter ctnum as rand, wherein rand belongs to [2, ctnumma×β]Rand is an integer, beta represents an adjustment parameter, and beta is greater than 1;
dividing the wireless sensor nodes in the nU into rand clusters, randomly determining the cluster head node of each cluster, dividing the wireless sensor nodes into each cluster according to the principle of minimum distance,
when the division index is made to obtain the maximum value, the value of rand is obtained, and the value of rand at the moment is used as the value of the parameter ctnum; the division index is calculated as follows:
Figure DEST_PATH_IMAGE003
where aveks denotes a partitioning index, numofN denotes a total number of wireless sensor nodes, nU denotes a set of all wireless sensor nodes, custU denotes a total number of wireless sensor nodeskRepresents the set of all wireless sensor nodes except the wireless sensor node k in the cluster to which the wireless sensor node k in the nU belongs, ncustUkRepresents custUkThe total number of wireless sensor nodes in (d), dist (k, i) represents the wireless sensor nodes k and custUkThe euclidean distance between the wireless sensor nodes i in (a),
Figure DEST_PATH_IMAGE004
ecpUrset of all wireless sensor nodes representing the r-th cluster except the cluster where k is located, r ∈ [1, rand-1],necpUrRepresents ecpUrTotal number of wireless sensor nodes in (d), dist (k, j) wireless sensor nodes k and ecpUrIs the euclidean distance between wireless sensor nodes j in (a).
Prior ArtFor example, in a low-power-consumption adaptive cluster hierarchical protocol, the number of the selected cluster head nodes is random, and the appropriate number of cluster heads cannot be selected well according to actual needs, so that the number of cluster heads is easily excessive, the coverage of monitoring is affected, and meanwhile, energy waste of a wireless sensor network is easily caused. In the above embodiment of the present invention, the theoretical number of cluster head nodes is calculated according to the area of the hazardous chemical storage area and the maximum communication radius of the wireless sensor node, however, since the communication range of the sensor node is generally circular, the theoretical number of cluster head nodes needs to be multiplied by an adjustment parameter to adjust, so as to determine the maximum value of the theoretical number of cluster head nodes, and then the interval [2, ctnum ] is adjusted in a traversal mannerma×β]And assigning the integer values to rand one by one, calculating the division index, and selecting the rand value which enables the maximum value of the division index to be used as the value of the parameter ctnum, so that the determined value of the parameter ctnum is more representative, and the wireless sensor network is divided into a plurality of clusters with proper number. On the calculation of the division index, the mean value of the distances between k and other members of the cluster to which k belongs is considered, the smaller the mean value is, the more accurate the clustering is, and meanwhile, the mean value of the distances between k and other clusters is also considered, the larger the value is, the more accurate the clustering is, so that the larger the division index is, the more reasonable the clustering number is, and the reasonable value of the parameter ctnum is determined. Compared with the traditional clustering algorithm, the embodiment of the invention is undoubtedly more adaptive, and can adaptively determine the reasonable cluster head number according to the distribution of the wireless sensor nodes. Therefore, the coverage monitoring of the storage environment of the hazardous chemical substances is ensured while the energy consumption is reduced.
In an embodiment, optimizing the candidate node to obtain the cluster head node includes:
storing the candidate nodes by using a set hxU, and dividing the wireless sensor nodes serving as member nodes into clusters where the candidate nodes are located according to a minimum distance principle;
the candidate nodes are ranked according to Euclidean distances between the candidate nodes and the base station, and optimization processing is carried out from the candidate node which is farthest away from the base station:
recording the candidate node subjected to optimization processing as hs, judging whether the total number of other candidate nodes in the maximum communication radius of the candidate node hs is smaller than a preset judgment threshold pdhtre, if so, not performing optimization processing, and if not, further judging;
the further determining comprises: judging whether other candidate nodes in the maximum communication radius of the candidate node hs meet the following judgment conditions, if so, not performing optimization, and if not, performing optimization:
δ1percyda×numcy＜ynumhs×perhsda＜δ2percyda×numcy
where numhs represents the total number of candidate nodes in the set HS, HS represents the set of other candidate nodes within the maximum communication radius of HS plus the candidate node HS, pershda represents the amount of data that can be received per unit time for each candidate node, numcy represents the total number of all member nodes in the cluster to which the candidate node in HS belongs, percyda represents the amount of data generated per unit time for each member node, δ2＞2δ1，δ1And delta2Is a preset proportion parameter, and is a preset proportion parameter,
the optimization processing comprises the following steps:
if delta1If the percyda × numcy is larger than or equal to ynumhs × perhsda, selecting the member node with the maximum competitiveness from the overlapping area of the candidate nodes except the candidate node HS in the candidate node HS and the HS as the member node added into the candidate node set hxU;
if ynumhs is multiplied by perhsda is more than or equal to delta2Percyda x numcy, then selecting the one with the smallest competitiveness from HS
Figure DEST_PATH_IMAGE005
A candidate node, deleting the candidate node from the candidate node set hxU, phi is a preset proportion parameter, and phi belongs to [0.1,0.5 ]]；
The candidate node finally remaining in the candidate node set hxU is taken as the cluster head node.
The above embodiment of the present invention is an optimization of cluster head node distribution, and if the number of other candidate nodes is small within the maximum communication radius of a candidate node, it indicates that the candidate node is an optimal selection nearby, and it is suitable for serving as a cluster head node. If the number of cluster head nodes in a certain area cannot meet the requirement of the data volume, adding the cluster head nodes, so that enough cluster head nodes can timely receive and forward the data generated by the member nodes; if the number of candidate nodes in a certain area is too large, obviously, the distribution mode is not reasonable, and therefore, the number of candidate nodes in the area needs to be reasonably reduced. Therefore, the distribution of the cluster heads is more reasonable, the energy waste caused by too many cluster head nodes distributed in a narrow area is avoided, meanwhile, the situation that the quantity of the cluster head nodes is insufficient and the data generated by the member nodes cannot be received and forwarded in time can also be avoided.
On the other hand, the invention provides an artificial intelligence based dangerous chemical risk identification and early warning system, which is used for realizing the artificial intelligence based dangerous chemical risk identification and early warning method.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (8)

1. A dangerous chemical risk identification early warning method based on artificial intelligence is characterized by comprising the following steps:
s1, acquiring monitoring data of the hazardous chemical substances, and transmitting the monitoring data to a cloud server;
s2, acquiring the monitoring data from the cloud service, inputting the monitoring data into a trained neural network model, and judging whether the state of the hazardous chemical substance is abnormal or not through the neural network model;
and S3, if the state of the dangerous chemical substance is abnormal, sending an early warning prompt to related workers.
2. The method for risk identification and early warning of dangerous chemicals based on artificial intelligence as claimed in claim 1, wherein obtaining monitoring data of dangerous chemicals comprises:
and acquiring monitoring data of the hazardous chemical substances through a wireless sensor network and a monitoring camera which are arranged in the warehouse.
3. The artificial intelligence based dangerous chemical risk identification and early warning method according to claim 2, wherein the monitoring data comprises gas concentration data, fire fighting data and monitoring data;
the gas concentration data comprises the concentration of combustible gas and the concentration of toxic gas in a hazardous chemical storage environment;
the fire fighting data comprises the temperature, the humidity and the smoke concentration of the storage environment of the hazardous chemical;
the monitoring data comprises monitoring videos of the storage environment of the hazardous chemicals.
4. The artificial intelligence based dangerous chemical risk identification and early warning method according to claim 3, wherein the combustible gas concentration comprises hydrogen concentration, acetylene concentration, ethylene concentration and hydrogen sulfide concentration;
the toxic gas concentration includes formaldehyde concentration, hydrogen chloride concentration, and sulfur dioxide concentration.
5. The dangerous chemical risk identification and early warning method based on artificial intelligence as claimed in claim 3, wherein the acquiring of monitoring data of dangerous chemical by wireless sensor network and monitoring camera in warehouse comprises:
acquiring gas concentration data and fire fighting data through the wireless sensor network; and acquiring monitoring data through the monitoring camera.
6. The artificial intelligence based risk identification and early warning method for dangerous chemicals according to claim 5, wherein the wireless sensor network comprises wireless sensor nodes and base stations;
the wireless sensor node is used for acquiring gas concentration data and fire-fighting data and transmitting the gas concentration data and the fire-fighting data to the base station;
the base station is used for receiving the gas concentration data and the fire fighting data and sending the gas concentration data and the fire fighting data to a cloud server.
7. The artificial intelligence based risk identification and early warning method for dangerous chemicals according to claim 6, wherein the base station is further configured to divide the wireless sensor nodes into member nodes and cluster head nodes in a fixed time period;
the member nodes are used for acquiring the gas concentration data and the fire fighting data and sending the gas concentration data and the fire fighting data to the cluster head nodes, and the cluster head nodes are used for sending the gas concentration data and the fire fighting data to the base station.
8. An artificial intelligence based risk identification and early warning system for dangerous chemicals, which is used for implementing the artificial intelligence based risk identification and early warning method for dangerous chemicals according to any one of claims 1-7.
CN202011190504.6A 2020-10-30 2020-10-30 Dangerous chemical risk identification early warning method and system based on artificial intelligence Withdrawn CN112333655A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113313926A (en) * 2021-05-27 2021-08-27 深圳市双佳医疗科技有限公司 Intelligent monitoring system for running state of medical equipment
CN115175126A (en) * 2022-09-02 2022-10-11 长沙银河众创科技信息有限公司 Intelligent park rapid fire-fighting emergency treatment method and system based on Internet of things

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113313926A (en) * 2021-05-27 2021-08-27 深圳市双佳医疗科技有限公司 Intelligent monitoring system for running state of medical equipment
CN115175126A (en) * 2022-09-02 2022-10-11 长沙银河众创科技信息有限公司 Intelligent park rapid fire-fighting emergency treatment method and system based on Internet of things
CN115175126B (en) * 2022-09-02 2023-02-17 长沙银河众创科技信息有限公司 Intelligent park rapid fire-fighting emergency treatment method and system based on Internet of things

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Application publication date: 20210205