CN114629934A - Intelligent kitchen monitoring system and kitchen pest tracking method - Google Patents
Intelligent kitchen monitoring system and kitchen pest tracking method Download PDFInfo
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- CN114629934A CN114629934A CN202111665267.9A CN202111665267A CN114629934A CN 114629934 A CN114629934 A CN 114629934A CN 202111665267 A CN202111665267 A CN 202111665267A CN 114629934 A CN114629934 A CN 114629934A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 46
- 241000607479 Yersinia pestis Species 0.000 title claims abstract description 41
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- 238000004891 communication Methods 0.000 claims abstract description 17
- WSFSSNUMVMOOMR-UHFFFAOYSA-N Formaldehyde Chemical compound O=C WSFSSNUMVMOOMR-UHFFFAOYSA-N 0.000 claims description 9
- 230000007613 environmental effect Effects 0.000 claims description 7
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- 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
- G08B21/18—Status alarms
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/40—Arrangements in telecontrol or telemetry systems using a wireless architecture
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
Abstract
The invention discloses an intelligent kitchen monitoring system and a kitchen pest tracking method, and aims to solve the problem that the prior art cannot monitor the behavior of people and track and position pests. In addition, a pest tracking method is also disclosed, the location of the nest can be inferred through the motion track of the pest tracking method, and the effect of more accurate and effective killing is achieved. Whether the operation of the worker is in compliance or not can be identified through the analysis module; the communication module is connected with the cloud platform, so that the real-time remote monitoring of background management personnel can be realized, and the improvement suggestion can be conveniently provided. The nest can be positioned by analyzing the harmful organism track through the analysis module, the working strength of the worker for killing can be reduced, and accurate killing is realized.
Description
Technical Field
The invention relates to an intelligent system, in particular to the field of intelligent monitoring of a kitchen.
Background
At present, the concern of general consumers on food safety is increasing day by day, food customers who are caused by food safety are bound to board the leader board of each news once the food customers suffer from a threatening event, the food safety of places such as school canteens and the like is concerned widely, various operators in a kitchen directly contact with food, the people in the kitchen are complex to enter and exit, many existing kitchen management systems cannot effectively monitor the behaviors of the operators and the operating environment, in addition, the environment in the kitchen is complex, dirt and various food residues are easy to store, various harmful organisms such as mice, flies, cockroaches and the like are easy to cause, and the existing management systems are also not capable of distinguishing the tracks of the organisms and positioning nests of the organisms so that the relevant people can kill the harmful organisms.
For example, a central kitchen management system disclosed in chinese patent literature, whose publication number CN213842412U, includes a frying area, a vegetable cooling area, a vegetable frying area, a steaming area, and a fruit washing area in a cooking operation area of a central kitchen, and thermal imaging temperature measuring cameras are respectively arranged in the respective areas to effectively monitor persons entering the respective areas, separate the respective areas to avoid cross movement of the persons, and avoid mutual interference of the persons in the respective areas.
Disclosure of Invention
The invention mainly solves the problem that the prior art can not monitor the behavior of people and track and position harmful organisms; an intelligent kitchen monitoring system and a kitchen pest tracking method are provided.
The technical problem of the invention is mainly solved by the following technical scheme:
the video data acquisition and analysis system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring video data input by video equipment and sensing data input by sensing equipment and sending data information to an analysis module; the analysis module is used for receiving the data information of the data acquisition module and analyzing the data, and the analysis module is used for analyzing the movement track of the kitchen pests, the behaviors of people and the sensing data; harmful organism nests are analyzed and positioned through the harmful organism tracks in the kitchen, so that related personnel can find harmful organism hiding places in time and eliminate harmful organisms at a time; a communication module: the system comprises a data acquisition unit, a cloud platform, a background management personnel and an alarm module, wherein the data acquisition unit is used for acquiring sensor data of the data acquisition unit and an output signal of the alarm module; an alarm module: and outputting a warning signal when the analysis module analyzes the abnormal condition.
Preferably, the sensing data comprises dry humidity, gas concentration, temperature and formaldehyde concentration. Through the direct monitoring to various environmental indexes in the kitchen, can in time report to the police when environmental index is unusual, improve the sanitary condition in back-office.
Preferably, the person behavior analysis monitors and analyzes clothing and operation programs of kitchen staff. In addition, other abnormal behaviors can be warned, such as whether garbage is correctly put into the garbage can, whether mature operation is in compliance, and the like.
Preferably, the communication module is based on a MESH network, and data reporting times of the communication node devices in the communication module are staggered with each other. Because the traditional message reporting mechanism is easy to have the phenomena of packet loss and data loss, the networking construction scale is limited, the time difference is uniformly calibrated by using the MESH network, and the time is used as the resource balanced distribution, so that each Internet of things equipment node has a reporting time window belonging to the node, the data reported by the message can have enough communication bandwidth, and the data reporting is guaranteed.
Also disclosed is a kitchen pest tracking method, comprising the steps of:
s1: the monitoring dead angle in the monitoring area is partitioned, the monitoring dead angle is not a place which cannot be observed due to a monitoring angle in the traditional sense, but refers to a place which cannot be observed due to the fact that various devices such as a gas stove, a table, an air conditioner cabinet, a refrigerator and the like can be hidden by mice and the like; initializing the presence and absence values of each area; the number of appearance of a certain pest in each area is referred to as the appearance number, and the initial appearance number of each area is zero.
S2: the analysis module is used for rendering and generating a motion track of the pest when the motion of the pest is detected through the analysis of the video stream information;
s3: increasing the appearance value of the monitoring dead angle at the first end and the last end of the motion track by 1; the higher the appearance value is, the higher the possibility that the creature is hidden in the area is, and then the hidden location positioning service can be provided based on the appearance value.
S4: generating the statistics of the presence and absence values of each region in real time;
s5: classifying the environmental characteristics of each region, and taking a weight value of 1.2 for the type I region; for the II type area, the weight value is 1; for class III regions, the weighting value is 0.8; multiplying the values of all the areas by the weight to obtain final values;
s6: a statistical map of the presence/absence values of the respective regions is generated, and the statistical map is updated in real time based on the real-time analysis by the analysis module in step S2.
Preferably, in step S2, an alarm module alarms when pest movement is detected. The alarming module can inform relevant personnel of the pollution risk in the kitchen so as to take action in time.
Preferably, the rendering to generate the motion trail of the harmful organism comprises the following steps:
s21, establishing a coordinate system and recording the coordinates of pests in each frame of picture; the position where the harmful organisms are present can be quantified and accurately recorded through the coordinates.
S22: and connecting the coordinates at each moment and fitting the coordinates with the monitoring picture to generate a track curve.
Preferably, in step S3, if the monitored dead angle region is the beginning and end of two or more motion tracks at the same time within a preset time range, the calculation is performed as adding 1 to the default value. The preset time is the time which can be modified according to the reality, and if the pests enter the monitoring dead angle area and come out of the monitoring dead angle area within the time, the pests are judged to pass through the monitoring dead angle area, so that the emergence value is increased by only 1, and the positions of the nests can be judged more accurately.
The invention has the beneficial effects that:
1. whether the operation of the worker is in compliance or not can be identified through the analysis module;
2. the communication module is connected with the cloud platform, so that the real-time remote monitoring of background management personnel can be realized, and the improvement opinions can be conveniently provided.
3. The nest can be positioned by analyzing the harmful organism track through the analysis module, the working strength of the worker for killing can be reduced, and accurate killing is realized.
Drawings
Fig. 1 is a schematic diagram of the structure of the intelligent kitchen management system of the present invention.
In the figure, 1 is a data acquisition module, 2 is an analysis module, 3 is a communication module, and 4 is an alarm module.
Detailed Description
Example (b):
the intelligence kitchen monitored control system of this embodiment, as shown in fig. 1, including data acquisition module 1, data acquisition module 1 includes surveillance camera head, sensor, and the surveillance camera head is data acquisition module 1 input video data, and the sensor includes dry humidity transducer, gas sensor, temperature sensor and formaldehyde sensor etc. and various sensors integrate the data of gathering separately to data acquisition module 1 on, are integrated by data acquisition module 1. The data acquisition module 1 integrates various collected data and then sends the data to the analysis unit 2, and meanwhile the data are sent to the cloud platform through the communication unit 3, the cloud platform comprises various data platforms, can be connected with a government supervision platform, can also be connected with an intelligent client, and a user can observe information published through the cloud platform on various terminal platforms. In the application scene of school canteens, as the food safety of schools involves many links, including rough processing areas, washing and disinfecting areas, cooking areas, pastry areas, private rooms and the like, high-definition monitoring video monitoring of all areas needs to be fully covered, after video stream information is uploaded through the communication module 3, users such as government supervisors and parents of each party can check monitoring videos on line in real time on a terminal platform with access authority so as to realize multi-channel and multi-organization joint participation and common management.
The analysis module 2 can realize the analysis of the movement locus of the kitchen harmful organisms, the analysis of the operation behaviors of workers, the analysis of environmental data and the like; the alarm module 4 comprises audio devices arranged in the corresponding area of the kitchen. The system shoots, intelligently analyzes and identifies the condition that a food processing worker wears a working coat, hat and mouth mask or the working coat, hat and mouth mask is not clean; the cook and the uncooked food processing personnel wash hands and disinfect before food operation, and simultaneously carry out oral reminding on the relevant personnel through the audio device after detecting the relevant conditions. When harmful organisms such as mice and cockroaches appear in the monitoring picture, an alarm can be triggered immediately, the audio device can notify related conditions to workers in a kitchen and send the related conditions to the cloud platform through the communication module, and managers in a background can receive alarm information through the cloud platform so as to take actions. The analysis module 2 can deduce the position of the nest by analyzing the motion track, thereby conveniently adopting effective means to destroy the creature.
The communication module 3 adopts the Bluetooth Mesh ad hoc network technology to greatly reduce wired networks. During construction, the use amount of network basic equipment and wires is reduced, the problems of instability, short extension distance and poor signal wall penetrating capability of a wireless network WIFI technology are solved, and the use amount of the network basic equipment and the wires is greatly reduced. The traditional message reporting mechanism is easy to have the phenomena of packet loss and data loss, and limits the networking construction scale, but the MESH network is used for uniformly calibrating time difference, and then time is used as resource balanced distribution, so that each Internet of things equipment node has a reporting time window, and thus, the data reported by the message can have enough communication bandwidth, and the data reporting is guaranteed.
In another aspect of this embodiment, a method for kitchen pest tracking is disclosed, comprising the steps of: s1: the analysis module 2 is used for partitioning the monitoring dead angle in the monitoring area and initializing the presence value and the absence value of each area; the monitoring dead angle in the method is not a place which cannot be observed due to a monitoring angle in the traditional sense, but refers to a place which cannot be observed due to the fact that various devices such as a gas stove, a table, an air-conditioning cabinet, a refrigerator and the like can be hidden by mice and the like, for example, the gas stove is set to be an area 1, and the air-conditioning cabinet is set to be an area 2; the presence value is the number of times that a certain pest appears in each area, the initial presence value of each area is zero, and the presence values of the areas are stored respectively, and are also stored respectively for different pests.
S2: the analysis module 2 analyzes the video stream information, and when the pest movement is detected, renders the motion trail of the pest, and the steps are as follows: and establishing a coordinate system, recording the coordinates of the pests in each frame of picture, connecting the coordinates at all times, and fitting the connected coordinates with the monitoring picture to generate a track curve. Because the angle of the monitoring camera is fixed after the monitoring camera is installed, the position of each blind spot area in the monitoring picture is relatively fixed, after a coordinate system is established, the coordinate range of each area in the monitoring picture can be preset, a motion track is generated according to coordinate points, the coordinate point where pests firstly appear in the picture is taken as the head end of the track, the disappeared coordinate point is taken as the tail end, and the area to which the pests belong can be judged according to the coordinate points at the head end and the tail end. The position where the harmful organisms are present can be quantified and accurately recorded through the coordinates.
S3: increasing the emergence value of the monitoring dead angle areas at the first end and the last end of the motion trail by 1; if the monitoring dead angle area simultaneously comprises coordinate points at the head end or the tail end of the two tracks within a certain time, the calculation is that the value is added with 1. This time is a time that can be modified according to practice; that is, if the pest enters the monitoring dead angle region and comes out of the monitoring dead angle region at the time, it is determined that the track only passes through the monitoring dead angle region, so that the occurrence value is increased by only 1, and the position of the nest can be more accurately determined.
S4: generating a sum value of the presence and absence values of each area in real time; the count is stored and counted each time the missing value is updated in step S3.
S5: classifying each region according to the environmental characteristics of each region, and if the region is a type I region, calculating the weight to be 1.2; if the region is a type II region, the weight is 1; if the region is a type III region, the weight is 0.8; obtaining actual values by multiplying the values of the areas by the weight values; the environmental characteristics refer to whether the environment of each area conforms to the characteristics of the pest nest, for example, under a table, if the four sides of the table are not close to the wall and the space is small, the table belongs to the type III area, and if the cooking bench is close to the wall and the space is large, the table belongs to the type I area. After weighted calculation is carried out on the appearance values and the non-appearance values of all the areas, the accuracy of nest positioning can be improved.
S6: and generating a statistical graph of the presence and absence values of each region. This statistical map is updated each time pest activity is detected by the monitoring. The worker can visually observe the pest emergence value of each region according to the statistical chart, and can kill and block holes of related regions in a targeted manner according to the statistical chart so as to fundamentally eliminate four pests.
Claims (8)
1. An intelligent kitchen monitoring system, comprising: a data acquisition module: the system comprises an analysis module, a data processing module and a data processing module, wherein the analysis module is used for acquiring video data input by video equipment and sensing data input by sensing equipment and sending the data information to the analysis module;
an analysis module: receiving data information of a data acquisition module, and analyzing data, including kitchen pest movement track analysis, personnel behavior analysis and sensing data analysis; locating pest nests by the kitchen pest trajectory analysis;
a communication module: the alarm module is used for sending the sensor data of the data acquisition unit and the output signal of the alarm module to the cloud platform;
an alarm module: and outputting a warning signal when the analysis module analyzes the abnormal condition.
2. An intelligent kitchen monitoring system according to claim 1, wherein said sensory data includes humidity, gas concentration, temperature, formaldehyde concentration.
3. An intelligent kitchen monitoring system according to claim 1, wherein said personnel behavior analysis monitors and analyzes clothing and operating procedures of kitchen personnel.
4. The intelligent kitchen monitoring system of claim 1, wherein the communication module is based on a MESH network, and data reporting times of the communication node devices in the communication module are staggered with each other.
5. A kitchen pest tracking method, dependent on an intelligent kitchen monitoring system as claimed in claims 1 to 4, comprising the steps of:
s1: partitioning the monitoring dead angles in the monitoring area, and initializing the appearance value of each area;
s2: the analysis module analyzes the video stream information, and when the pest movement is detected, renders and generates a movement track of the pest;
s3: increasing the emergence value of the monitoring dead angle areas at the first end and the last end of the motion trail by 1;
s4: generating a sum value of the presence and absence values of each area in real time;
s5: classifying the environmental characteristics of each region, and taking a weight value of 1.2 for the type I region; for the II type area, the weight value is 1; for class III regions, the weighting value is 0.8; multiplying the values of all the areas by the weight to obtain final values;
s6: and generating a statistical graph of the presence and absence values of each region.
6. The kitchen pest tracking method according to claim 5, wherein an alarm module gives an alarm when pest movement is detected in step S2.
7. The kitchen pest tracking method of claim 5, wherein said rendering the motion trajectory of the generated pest in step S2 comprises the steps of:
s21, establishing a coordinate system and recording the coordinates of pests in each frame of picture;
s22: and connecting the coordinates at each moment and fitting the coordinates with the monitoring picture to generate a track curve.
8. The kitchen pest tracking method according to claim 5, wherein in step S3, if the monitoring dead angle region is the beginning and end of two or more movement tracks at the same time within a preset time range, the value is calculated as the sum of the occurrence value and the non-occurrence value plus 1.
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| CN111474880A (en) * | 2020-04-13 | 2020-07-31 | 佛山科学技术学院 | College kitchen intelligent supervision system |
| CN112037087A (en) * | 2020-09-09 | 2020-12-04 | 范玲珍 | Catering health safety intelligent monitoring management system based on big data |
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2021
- 2021-12-31 CN CN202111665267.9A patent/CN114629934A/en active Pending
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