An intelligent kitchen monitoring system and kitchen pest tracking method

Abstract

The present invention discloses an intelligent kitchen monitoring system and a method for tracking kitchen pests. In order to overcome the problem that the prior art cannot monitor personnel behavior and track and locate pests, the present invention includes a data acquisition module, an analysis module, a communication module and an alarm module. The data is analyzed by the analysis module and then sent to a cloud platform by the communication module, so that remote supervision by backstage managers can be achieved. In addition, a method for tracking pests is also disclosed, which can infer the location of the nest through its movement trajectory to achieve a more accurate and effective killing effect. The analysis module can be used to identify whether the operation of the staff is compliant; the connection between the communication module and the cloud platform can realize real-time remote monitoring by the backstage managers, which is convenient for proposing rectification suggestions. The analysis of the trajectory of the pests by the analysis module can locate the nest, reduce the work intensity of the staff in disinfecting, and achieve accurate disinfecting.

Description

Technical Field

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

[0002] Nowadays, consumers are paying more and more attention to food safety. Once an incident occurs that threatens the health of diners due to food safety, it will surely top the list of major news. Food safety in places such as school canteens has attracted widespread attention. Since various operators in the kitchen are in direct contact with food, and the entry and exit of personnel in the kitchen is also relatively complicated, many existing kitchen management systems cannot meet the effective monitoring of operator behavior and operating environment. In addition, the environment in the kitchen is relatively complex, which is more likely to hide dirt and various food residues, and it is easy to attract various harmful organisms such as rats, flies, cockroaches, etc. The existing management system also does not have the ability to identify the trajectories of these organisms and locate their nests so that relevant personnel can kill the harmful organisms.

[0003] For example, a "central kitchen management system" disclosed in Chinese patent documents, with announcement number CN213842412U, has a frying area, a cold dish area, a stir-fry area, a steaming area, and a fruit washing area in the cooking operation area of ​​the central kitchen, and a thermal imaging temperature measurement camera is set in each area to effectively monitor the people entering each area, separate the areas to avoid cross-movement of people, and avoid mutual interference between people in different areas. The thermal imaging temperature measurement camera can monitor the body temperature of operators in each area in real time. At the same time, the thermal imaging temperature measurement camera is electrically connected to the alarm module, which can promptly detect people with abnormal body temperature and isolate them. Summary of the invention

[0004] The present invention mainly solves the problem that the prior art cannot monitor human behavior and track and locate harmful organisms; and provides an intelligent kitchen monitoring system and a kitchen harmful organism tracking method.

[0005] The above technical problems of the present invention are mainly solved by the following technical solutions:

[0006] The present invention includes a data acquisition module for collecting video data input by a video device and sensor data input by a sensor device, and sending the data information to an analysis module; and an analysis module for receiving data information from the data acquisition module and analyzing the data, wherein the analysis module analyzes movement trajectories of harmful organisms in the back kitchen, human behavior, and sensor data; the harmful organism nests are located by analyzing the trajectories of harmful organisms in the back kitchen, so that relevant personnel can find out the hiding places of harmful organisms in time and eliminate the harmful organisms in one fell swoop; a communication module: used for sending the sensor data of the data acquisition unit and the output signal of the alarm module to a cloud platform, and after uploading the relevant data to the cloud platform, the backstage management personnel can realize remote supervision and management, process and correct abnormal data in time, and order rectification in time; an alarm module: outputs a warning signal when the analysis module analyzes an abnormal situation.

[0007] Preferably, the sensor data includes humidity, gas concentration, temperature, and formaldehyde concentration. By directly monitoring various environmental indicators in the kitchen, an alarm can be given in time when an environmental indicator is abnormal, thereby improving the hygiene of the kitchen.

[0008] Preferably, the personnel behavior analysis monitors and analyzes the clothing and operating procedures of the kitchen staff. In addition, other abnormal behaviors can also be warned, such as whether the garbage is put into the garbage bin correctly, whether the raw and cooked operations are compliant, etc.

[0009] Preferably, the communication module is based on a MESH network, and the data reporting time of each communication node device in the communication module is staggered. Since the traditional message reporting mechanism is prone to packet loss and data loss, which limits the scale of networking construction, the MESH network is used to uniformly calibrate the time difference, and then the time is evenly distributed as a resource, so that each IoT device node has its own reporting time window, so that the data reported by the message can have sufficient communication bandwidth, and the data reporting is guaranteed.

[0010] A method for tracking harmful organisms in a kitchen is also disclosed, comprising the following steps:

[0011] S1: Divide the blind spots in the monitoring area into different areas. Blind spots are not places that cannot be observed due to the monitoring angle in the traditional sense, but places that cannot be observed due to various equipment such as gas stoves, tables, air-conditioning cabinets, refrigerators, etc., which can be used by mice to hide; initialize the presence value of each area; the presence value refers to the number of times a certain pest appears in each area, and the initial presence value of each area is zero.

[0012] S2: The analysis module analyzes the video stream information and renders and generates the movement trajectory of the harmful organisms when the movement of harmful organisms is detected;

[0013] S3: Increase the presence value of the blind spots at the beginning and end of the movement trajectory by 1; the beginning and end of the movement trajectory are the possible hiding places of the creature. The higher the presence value, the higher the possibility that the creature is hiding in the area. This can be used as a basis for providing hiding place positioning services in the future.

[0014] S4: Generates the occurrence value statistics of each area in real time;

[0015] S5: Classify the environmental characteristics of each area. For Class I areas, the weight is 1.2; for Class II areas, the weight is 1; for Class III areas, the weight is 0.8; multiply the occurrence value of each area by the weight to obtain the final occurrence value;

[0016] S6: Generate a statistical graph of the occurrence value of each area, and update the statistical graph in real time according to the real-time analysis of the analysis module in step S2.

[0017] Preferably, when the movement of harmful organisms is detected in step S2, an alarm is generated through an alarm module, which can inform relevant personnel that there is a risk of contamination in the kitchen so that timely action can be taken.

[0018] Preferably, the rendering and generating of the movement trajectory of harmful organisms comprises the following steps:

[0019] S21: Establish a coordinate system to record the coordinates of the harmful organisms in each frame; the coordinates can be used to quantify and accurately record the locations where the harmful organisms appear.

[0020] S22: Connect the coordinates at each time and fit them with the monitoring screen to generate a trajectory curve.

[0021] Preferably, in step S3, if the monitoring blind spot area simultaneously becomes the beginning and end of two or more movement tracks within the preset time range, the calculation is the appearance value plus 1. The preset time is a time that can be modified according to the actual situation. During this time, if the harmful organism enters the monitoring blind spot area and then exits the monitoring blind spot area, it is judged that the track only passes through the monitoring blind spot area, so the appearance value is only increased by 1, which can more accurately determine the location of the nest.

[0022] The beneficial effects of the present invention are:

[0023] 1. The analysis module can be used to identify whether the staff's operations are compliant;

[0024] 2. Through the connection between the communication module and the cloud platform, back-end management personnel can realize real-time remote monitoring, making it convenient to put forward rectification suggestions.

[0025] 3. The analysis module can locate the nests of harmful organisms by analyzing their trajectories, which can reduce the workload of pest control workers and achieve precise pest control. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a structural schematic diagram of the intelligent kitchen management system of the present invention.

[0027] In the figure, 1. data acquisition module, 2. analysis module, 3. communication module, 4. alarm module. DETAILED DESCRIPTION

[0028] Example:

[0029] An intelligent kitchen monitoring system of this embodiment, such as Figure 1 As shown, it includes a data acquisition module 1, which includes a monitoring camera and a sensor. The monitoring camera inputs video data for the data acquisition module 1. The sensors include a dry and wet sensor, a gas sensor, a temperature sensor, and a formaldehyde sensor, etc. Various sensors integrate the data collected by each sensor into the data acquisition module 1, and the data acquisition module 1 integrates the data. The data acquisition module 1 integrates the various collected data and sends them to the analysis unit 2, and at the same time sends them to the cloud platform through the communication unit 3. The cloud platform includes various data platforms, which can be connected to the government's supervision platform and can also be connected to the smart client. Users can observe the information released by the cloud platform on various terminal platforms. In the application scenario of the school cafeteria, since school food safety involves many links, including rough processing areas, washing and disinfection areas, cooking areas, pastry areas, special rooms, etc., it is necessary to achieve full coverage of high-definition surveillance video surveillance in each area. After the video stream information is uploaded through the communication module 3, users from all parties, such as government regulators and parents, can view the surveillance video online in real time on the terminal platform with access rights to achieve multi-channel and multi-organizational joint participation and joint management.

[0030] The analysis module 2 can realize the movement trajectory analysis of harmful organisms in the back kitchen, the operation behavior analysis of the staff, and the environmental data analysis, etc.; the alarm module 4 includes an audio device arranged in the corresponding area of ​​the back kitchen. The system captures and intelligently analyzes and identifies the situation that food processing personnel wear untidy work clothes, hats and masks or unclean work clothes, hats and masks; chefs and raw food processing personnel wash their hands and disinfect before handling food, and verbally remind relevant personnel through the audio device after detecting relevant situations. When harmful organisms such as rats and cockroaches appear in the monitoring screen, an alarm will be triggered immediately. The audio device will inform the staff in the back kitchen of the relevant situation and send it to the cloud platform through the communication module. The backstage management personnel can receive the alarm information through the cloud platform to take action. The analysis module 2 can infer the location of the nest by analyzing the movement trajectory, so as to facilitate the adoption of effective means to eliminate the modified organisms.

[0031] The Bluetooth Mesh self-organizing network technology is used in the communication module 3 to greatly reduce the wired network. The amount of network infrastructure equipment and wire materials used during construction, while avoiding the problems of unstable wireless network WIFI technology, short extension distance, and poor signal wall penetration, greatly reducing the amount of network infrastructure equipment and wire materials used. The traditional message reporting mechanism is prone to packet loss and data loss, which limits the scale of network construction. However, the MESH network is used to uniformly calibrate the time difference, and then the time is evenly allocated as a resource, so that each IoT device node has its own reporting time window, so that the message reported data can have sufficient communication bandwidth, and data reporting is guaranteed.

[0032] Another aspect of the present embodiment discloses a method for tracking pests in the back kitchen, comprising the following steps: S1: the analysis module 2 partitions the monitoring blind spots in the monitoring area and initializes the presence value of each area; the so-called monitoring blind spots in the present method are not places that cannot be observed due to the monitoring angle in the traditional sense, but refer to places that cannot be observed by monitoring due to various equipment such as gas stoves, tables, air-conditioning cabinets, refrigerators, etc., which can be used for mice to hide. For example, the gas stove is set to area 1 and the air-conditioning cabinet is set to area 2; the presence value refers to the number of times a certain pest appears in each area, the initial presence value of each area is zero, and the presence value of each area is stored separately, and different pests are also stored separately.

[0033] S2: Analysis module 2 analyzes the video stream information. When the movement of harmful organisms is detected, the movement trajectory of harmful organisms is rendered. The steps are as follows: establish a coordinate system, record the coordinates of the harmful organisms in each frame, connect the coordinates at each moment, and then fit the monitoring screen to generate a trajectory curve. Since the monitoring camera has a fixed angle after installation, the position of each blind spot area in the monitoring screen is relatively fixed. After the coordinate system is established, the coordinate range of each area in the monitoring screen can be pre-set, and the movement trajectory is generated according to the coordinate points. The coordinate point where the harmful organism first appears in the screen is used as the beginning of the trajectory, and the coordinate point where it disappears is used as the end. The coordinate points at the beginning and end can be used to determine which area it belongs to. The coordinates can be used to quantify and accurately record the locations where harmful organisms appear.

[0034] S3: Increase the presence value of the blind spot area at the beginning and end of the movement trajectory by 1; if the blind spot area includes the coordinate points of the beginning or end of the two trajectories at the same time within a certain period of time, the presence value is calculated as 1. This time is based on the actual time that can be modified; that is, if the pest enters the blind spot area and then exits the blind spot area during this period of time, it is judged that the trajectory only passes through the blind spot area, so the presence value is only increased by 1, which can more accurately determine the location of the nest.

[0035] S4: Generate the statistic of the haunting value of each area in real time; store and count the haunting value after it is updated in step S3 each time.

[0036] S5: Classify each area according to its environmental characteristics. If it is a Class I area, the weight is calculated as 1.2; if it is a Class II area, the weight is 1; if it is a Class III area, the weight is 0.8; the product of the occurrence value of each area and the weight is obtained to obtain the actual occurrence value; environmental characteristics refer to whether the environment of each area meets the characteristics of the pest nest. For example, under the table, if it is not against the wall on all four sides and the space is small, it belongs to the Class III area. If the stove is against the wall and there is a lot of space that cannot be cleaned frequently, it belongs to the Class I area. After weighted calculation of the occurrence value of each area, the accuracy of nest positioning can be improved.

[0037] S6: Generate a statistical chart of the occurrence values ​​of each area. This statistical chart will be updated every time the monitoring detects the activity of harmful organisms. Based on the statistical chart, the staff can intuitively observe the occurrence values ​​of various harmful organisms in each area. Based on the statistical chart, they can carry out targeted disinfection and plugging of holes in the relevant areas to fundamentally eliminate the four pests.

Claims

1. A method for tracking harmful organisms in a kitchen, characterized in that: The following steps are involved: S1: Divide the blind spots in the monitoring area into different areas and initialize the presence / absence values ​​of each area; S2: The analysis module analyzes the video stream information and renders and generates the movement trajectory of the harmful organisms when the movement of harmful organisms is detected; S3: Increase the presence value of the monitoring blind spot area at the beginning and end of the movement trajectory by 1 to determine the location of the nest; S4: Generate the occurrence value statistics of each area in real time; S5: Classify the environmental characteristics of each area. For Class I areas, the weight is 1.2; for Class II areas, the weight is 1; for Class III areas, the weight is 0.8; multiply the occurrence value of each area by the weight to obtain the final occurrence value; S6: Generate a statistical graph of the occurrence values ​​of each area; In step S3, if the monitoring blind spot area simultaneously becomes the beginning and end of two or more motion trajectories within a preset time range, it is calculated as the appearance value plus 1.

2. A kitchen pest tracking method according to claim 1, characterized in that: When the movement of harmful organisms is detected in step S2, an alarm is generated through the alarm module.

3. A kitchen pest tracking method according to claim 1, characterized in that: The rendering and generation of the motion trajectory of harmful organisms in step S2 includes the following steps: S21: Establish a coordinate system to record the coordinates of the harmful organisms in each frame; S22: Connect the coordinates at each time and fit them with the monitoring screen to generate a trajectory curve.

4. An intelligent kitchen monitoring system, based on a kitchen pest tracking method according to any one of claims 1 to 3, characterized in that: include: Data acquisition module: used to collect video data input by video equipment and sensor data input by sensor equipment, and send the data information to the analysis module; Analysis module: receiving data information from the data acquisition module, and analyzing the data, including analysis of movement trajectories of harmful organisms in the kitchen, analysis of personnel behavior, and analysis of sensor data; locating the nests of harmful organisms through analysis of movement trajectories of harmful organisms in the kitchen; Communication module: used to send the sensor data of the data acquisition unit and the output signal of the alarm module to the cloud platform; Alarm module: outputs a warning signal when the analysis module analyzes an abnormal situation.

5. The intelligent kitchen monitoring system according to claim 4, characterized in that: The sensor data include humidity, gas concentration, temperature, and formaldehyde concentration.

6. The intelligent kitchen monitoring system according to claim 4, characterized in that: The personnel behavior analysis monitors and analyzes the clothing and operating procedures of the kitchen staff.

7. The intelligent kitchen monitoring system according to claim 4, characterized in that: The communication module is based on a MESH network, and the data reporting times of the various communication node devices in the communication module are staggered.

Images