CN112464818A - Kitchen supervision method, device, equipment and storage medium - Google Patents
Kitchen supervision method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a kitchen supervision method, a kitchen supervision device, kitchen supervision equipment and a storage medium, wherein the kitchen supervision device comprises the following steps: identifying image frames in the collected kitchen video stream through a machine learning model to obtain current violation image frames, wherein the current violation image frames comprise violation behaviors; determining a target catering unit corresponding to the current violation image frame, and counting the number of violation image frames corresponding to the target catering unit, wherein the corresponding violation image frames comprise the current violation image frame; determining a dispatching end of the current violation image frame according to the number of violation image frames corresponding to the target catering unit; and sending the current violation image frame to the sending end, and receiving processing information fed back by the sending end according to the current violation image frame. According to the kitchen supervision method, on one hand, the current violation image frame can be identified by using a machine learning technology, so that the labor cost is saved, and the identification accuracy is improved; on the other hand, a closed-loop processing mode from finding the illegal action to processing the illegal action can be realized, and the supervision effect is improved.
Description
Technical Field
The embodiment of the invention relates to the field of video processing, in particular to a kitchen supervision method, a kitchen supervision device, kitchen supervision equipment and a storage medium.
Background
With the development of the times and the progress of science and technology, the living standard of people is gradually improved, the pace of life is gradually accelerated, and with the change of diet of people, a plurality of problems of unqualified online ordering and sanitation, black take-out workshops, fresh materials and the like are exposed to the eye of people, and more people begin to pay attention to the problem of food safety. Therefore, the national food and drug administration has introduced bright kitchen and lighting stove engineering, and mainly through the construction of transparent kitchens and video kitchens, consumers can visually see the cooking of dishes, the processing of cold and uncooked food, the disinfection of tableware and tableware, and the like. The food safety monitoring system aims to enable the kitchen and food processing process of the food service unit to go from the back to the front, eliminate the obstacle of asymmetric information among the food service unit, the food processing enterprise and the public, guide the public to directly participate in food safety, realize the supervision of the whole people and finally realize the fundamental improvement of the food safety. How to utilize bright kitchen range engineering is very important to realize supervision on a kitchen.
At present, most catering units are provided with camera monitoring equipment, a kitchen video stream can be collected through the camera monitoring equipment, image frames in the kitchen video stream are identified by using a machine learning technology, and the image frames containing illegal behaviors are marked.
However, the current supervision method can only mark the image frame containing the violation, does not realize the closed-loop processing of the violation, and has a poor supervision effect.
Disclosure of Invention
The invention provides a kitchen supervision method, a kitchen supervision device, equipment and a storage medium, and aims to solve the technical problem of poor supervision effect caused by the fact that the existing kitchen supervision method cannot carry out closed-loop processing on illegal behaviors.
In a first aspect, an embodiment of the present invention provides a kitchen supervision method, including:
identifying image frames in the collected kitchen video stream through a preset machine learning model to obtain current violation image frames; wherein the current violation image frame includes a violation;
determining a target catering unit corresponding to the current violation image frame, and counting the number of violation image frames corresponding to the target catering unit; the violation image frame corresponding to the target catering unit comprises the current violation image frame;
determining a sending end of the current violation image frame according to the number of violation image frames corresponding to the target catering unit;
and sending the current violation image frame to the sending end, and receiving processing information fed back by the sending end according to the current violation image frame.
In a second aspect, an embodiment of the present invention provides a kitchen monitoring apparatus, including:
the first determining module is used for identifying the image frames in the collected kitchen video stream through a preset machine learning model to obtain the current violation image frames; wherein the current violation image frame includes a violation;
the second determining module is used for determining a target catering unit corresponding to the current violation image frame and counting the number of violation image frames corresponding to the target catering unit; the violation image frame corresponding to the target catering unit comprises the current violation image frame;
the third determining module is used for determining a sending end of the current violation image frame according to the number of the violation image frames corresponding to the target catering unit;
and the sending and receiving module is used for sending the current violation image frame to the sending end and receiving processing information fed back by the sending end according to the current violation image frame.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a kitchen supervision method as provided in the first aspect.
In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the kitchen supervision method as provided in the first aspect.
The embodiment of the invention provides a kitchen supervision method, a kitchen supervision device, kitchen supervision equipment and a storage medium, wherein the method comprises the following steps: identifying image frames in the collected kitchen video stream through a preset machine learning model to obtain current violation image frames, wherein the current violation image frames comprise violation behaviors; determining a target catering unit corresponding to the current violation image frame, and counting the number of violation image frames corresponding to the target catering unit, wherein the violation image frames corresponding to the target catering unit comprise the current violation image frame; determining a dispatching end of the current violation image frame according to the number of violation image frames corresponding to the target catering unit; and sending the current violation image frame to the sending end, and receiving processing information fed back by the sending end according to the current violation image frame. According to the kitchen supervision method, on one hand, the current violation image frame including the violation can be identified by using a machine learning technology, so that the labor cost is saved, and the identification accuracy is improved; on the other hand, a closed-loop processing mode from finding the illegal action to processing the illegal action can be realized, and the supervision effect is improved; on the other hand, the number of the illegal image frames corresponding to the target catering unit can be based on, so that differentiated supervision is realized, the supervision effect is further ensured, and the supervision resource is saved.
Drawings
FIG. 1 is a flow chart of a kitchen supervision method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a kitchen supervision method according to another embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a kitchen monitoring device according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a kitchen monitoring device according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1 is a schematic flow chart of a kitchen supervision method according to an embodiment of the present invention. The embodiment is suitable for a scene of supervising the kitchen of a catering unit. The present embodiment may be performed by a kitchen supervision apparatus, which may be implemented by means of software and/or hardware, which may be integrated in a computer device. As shown in fig. 1, the kitchen supervision method provided by this embodiment includes the following steps:
step 101: and identifying the image frames in the collected kitchen video stream through a preset machine learning model to obtain the current violation image frames.
Wherein the current violation image frame includes a violation.
Specifically, the computer device in this embodiment may be a server. The machine learning model in this embodiment is a model that is trained in advance and is capable of identifying an illegal action in an image frame. For example, the machine learning model in this embodiment may be a model trained based on an algorithm such as a Convolutional Neural Network (CNN) algorithm, a cyclic Convolutional Neural network (RCNN) algorithm, and a training sample set.
The illegal action in the image frame of the present embodiment refers to an action that does not comply with a regulation, for example, a standard regulation, a regulatory regulation, or a regulation. Illustratively, the above-mentioned violation may include the following types: people's identity is not in line with, people wear is not normal, people's action is not normal, unidentified object moves etc. The person identity inconsistency can include that a person without authority enters a restricted area. The person wearing the irregularity may include an unworn mask. The non-normative personnel behavior may include non-normative ways of washing food materials. The unknown object movement may include object movement of a mouse, cockroach, and the like.
The kitchen in this embodiment refers to a kitchen of a dining establishment. The catering units in this embodiment can be classified into the following categories based on the business state and scale of the operator: restaurants, fast food restaurants, snack bars, beverage stores, canteens, and the like. The kitchen in this embodiment refers to a kitchen area of a catering establishment. Illustratively, the cook top in the present embodiment may include: cooking area, cutting and matching area, cleaning area, storage area, garbage temporary storage area and the like.
The video stream of the kitchen in this embodiment is a video stream collected by a video collecting device disposed in a corresponding area of the kitchen. The number of the video acquisition devices can be multiple, and accordingly, multiple kitchen video streams can be acquired. The machine learning model in this embodiment can simultaneously realize the recognition of the image frames in the N kitchen video streams. N is an integer greater than or equal to 1. When the number M of the video acquisition devices is greater than N, a plurality of machine learning models may be set to simultaneously recognize image frames of the kitchen video stream acquired by the M video acquisition devices.
The kitchen supervision device in this embodiment can acquire the kitchen video stream from the video acquisition device. After the kitchen video stream is obtained, frames are extracted or decoded frame by frame to obtain image frames, and the image frames are identified through a machine learning model. The machine learning model inputs image frames and outputs the current violation image frames. Optionally, the kitchen supervisor may store the current violation image frame.
Step 102: and determining a target catering unit corresponding to the current violation image frame, and counting the number of violation image frames corresponding to the target catering unit.
And the violation image frames corresponding to the target catering units comprise the current violation image frames.
Specifically, after the current violation image frame is determined, a target dining unit corresponding to the current violation image frame needs to be determined.
In one implementation, after the video capture device captures the kitchen video stream, corresponding dining unit information is added to each image frame of the video stream. The kitchen supervision device can determine a target catering unit corresponding to the current violation image frame based on the catering unit information in the current violation image frame.
In another implementation manner, the target catering unit corresponding to the current violation image frame can be determined according to the target video acquisition device corresponding to the current violation image frame and the mapping relationship between the video acquisition device and the catering unit.
The mapping relation between the video acquisition device and the catering units can be pre-stored in the kitchen supervision device. In the implementation mode, a target video acquisition device corresponding to the current violation image frame is determined, wherein the target video acquisition device means a video acquisition device for acquiring a kitchen video stream including the current violation image frame; and finding out the target catering unit corresponding to the target video acquisition device from the mapping relation. The implementation mode does not need the video acquisition device to add corresponding catering unit information in each image frame of the video stream, and the efficiency is high.
After the target catering unit is determined, the kitchen supervision device counts the number of violation image frames corresponding to the target catering unit. The violation image frame here includes the determined current violation image frame. In other words, after the target catering unit is determined, the kitchen supervision device counts the number of violation image frames corresponding to the target catering unit, including the current violation image frame. The statistics means recounting, i.e., recalculating the violation image frames corresponding to the target dining unit, or the statistics means updating, i.e., updating the number of the corresponding violation image frames determined last time.
Further, in step 102, after the target dining unit corresponding to the current violation image frame is determined, the kitchen supervision method provided in this embodiment further includes: determining other attribute information except for the target catering unit in the information corresponding to the current violation image frame, wherein the other attribute information comprises: at least one item of information of the address of the target catering unit, the type of the violation and the occurrence time of the violation; and displaying the current violation image frame, the target catering unit corresponding to the current violation image frame and at least one item of other attribute information corresponding to the current violation image frame.
When the address of the target catering unit corresponding to the current violation image frame is determined, the address of the target catering unit corresponding to the current violation image frame can be determined according to the target video acquisition device corresponding to the current violation image frame and the mapping relation between the video acquisition device and the address of the catering unit.
Optionally, in addition to identifying the current violation image frame, the machine learning model in this embodiment may also identify the type of violation in the current violation image frame. Therefore, the type of violation in the current violation image frame may be determined from the output of the machine learning model.
When the violation occurrence time corresponding to the current violation image frame is determined, the timestamp of the target video acquisition device in the current violation image frame can be added to determine the violation occurrence time corresponding to the current violation image frame.
By displaying the current violation image frame, the target catering unit corresponding to the current violation image frame and at least one item of other attribute information corresponding to the current violation image frame, the current violation image frame and related information can be observed conveniently, and user experience is improved.
Further, in order to achieve finer supervision, the number of violation image frames corresponding to the target dining unit is counted in step 102, which may be the number of violation image frames in the violation image frames corresponding to the target dining unit, where the type of violation included in the violation image frames is the same as the type of violation included in the current violation image frame. Such an implementation may enable fine-grained to violation type policing.
Step 103: and determining a sending end of the current violation image frame according to the number of the violation image frames corresponding to the target catering unit.
Specifically, in step 103, a sending end of the current violation image frame may be determined based on a size relationship between the number of violation image frames corresponding to the target dining unit and a preset threshold.
The dispatch end in this embodiment refers to a device or a client that the current violation image frame needs to reach.
One possible implementation manner is that when the number of violation image frames corresponding to a target catering unit is greater than a preset threshold value, a mobile terminal of a first processing person is determined as a sending terminal of the current violation image frame; and when the number of the violation image frames corresponding to the target catering unit is less than or equal to a preset threshold value, determining the moving end of the second processing personnel as the sending end of the current violation image frame.
It should be noted that the mobile terminal herein may refer to a mobile device, and may also refer to a client.
More specifically, the first treating person is a supervisor of the target catering unit, and the second treating person is a responsible person of the target catering unit. The supervisor of the target catering unit in this embodiment refers to a person of a supervision department of the target catering unit, for example, a worker of a health department. The person responsible for the target dining unit in this embodiment refers to a person responsible for the target dining unit, for example, a legal person representative of the target dining unit.
That is, when the number of the violation image frames corresponding to the target catering unit is greater than the preset threshold, it indicates that the number of occurrences of the violation behaviors of the target catering unit is large, and the moving end of the supervision personnel of the target catering unit can be determined as the sending end of the current violation image frame, which needs to be paid attention by the supervision department. When the number of the violation image frames corresponding to the target catering unit is smaller than or equal to the preset threshold, the number of times of violation behaviors of the target catering unit is small, and the moving end of a person responsible for the target catering unit can be determined as the sending end of the current violation image frame through self-checking improvement. Through the mode, the differential supervision can be realized, on one hand, when the number of the illegal behaviors of the target catering unit is more, the supervision strength can be improved, the target catering unit is supervised to correct the illegal behaviors, and on the other hand, when the number of the illegal behaviors is less, the illegal behaviors are corrected through self-checking, so that the supervision resources are saved.
Step 104: and sending the current violation image frame to the sending end, and receiving processing information fed back by the sending end according to the current violation image frame.
Specifically, after the dispatch end is determined, the current violation image frame may be sent to the dispatch end.
Optionally, at least one of the current violation image frame, the target dining unit corresponding to the current violation image frame, and other attribute information corresponding to the current violation image frame may be sent to the dispatch end.
After receiving the current violation image frame, the sending end can correct the violation in the current violation image frame in different ways.
When the sending end is a moving end of the supervising personnel of the target catering unit, the supervising personnel of the target catering unit can correct the violation in the current violation image frame through the measures of field investigation, policy declaration and the like to form processing information.
When the sending end is a mobile end of a person responsible for the target catering unit, the person responsible for the target catering unit can correct the violation in the current violation image frame by taking measures such as on-site correction, system specification and the like to form processing information.
The processing information in this embodiment may be at least one of text, video, audio, and image.
Illustratively, the processing information may be a picture, a video, or a rule based on the violation, etc., after having been corrected.
Furthermore, in order to improve the supervision effect, the kitchen supervision method provided by the embodiment further includes the following steps: and when the fact that the processing information fed back by the dispatching end is not received within the preset time length is determined, sending reminding information to the dispatching end. To supervise and urge the processing personnel at the sending end to process as soon as possible and improve the supervision effect.
According to the kitchen supervision method provided by the embodiment, after the current violation image frames including the violation are identified, the corresponding target catering units can be determined, the number of the violation image frames corresponding to the target catering units is counted, the sending end of the current violation image frames is determined based on the number of the violation image frames corresponding to the target catering units, the current violation image frames are sent to the sending end, and the processing information fed back by the sending end is received.
The embodiment provides a kitchen supervision method, which comprises the following steps: identifying image frames in the collected kitchen video stream through a preset machine learning model to obtain current violation image frames, wherein the current violation image frames comprise violation behaviors; determining a target catering unit corresponding to the current violation image frame, and counting the number of violation image frames corresponding to the target catering unit, wherein the violation image frames corresponding to the target catering unit comprise the current violation image frame; determining a dispatching end of the current violation image frame according to the number of violation image frames corresponding to the target catering unit; and sending the current violation image frame to the sending end, and receiving processing information fed back by the sending end according to the current violation image frame. According to the kitchen supervision method, on one hand, the current violation image frame including the violation can be identified by using a machine learning technology, so that the labor cost is saved, and the identification accuracy is improved; on the other hand, a closed-loop processing mode from finding the illegal action to processing the illegal action can be realized, and the supervision effect is improved; on the other hand, the number of the illegal image frames corresponding to the target catering unit can be based on, so that differentiated supervision is realized, the supervision effect is further ensured, and the supervision resource is saved.
Fig. 2 is a schematic flow chart of a kitchen supervision method according to another embodiment of the present invention. The embodiment of the present invention provides a detailed description of other steps included in the kitchen supervision method based on the embodiment shown in fig. 1 and various optional implementation schemes. As shown in fig. 2, the kitchen supervision method provided by this embodiment includes the following steps:
step 201: and identifying the image frames in the collected kitchen video stream through a preset machine learning model to obtain the current violation image frames.
Wherein the current violation image frame includes a violation.
Step 202: and determining a target catering unit corresponding to the current violation image frame, and counting the number of violation image frames corresponding to the target catering unit.
And the violation image frames corresponding to the target catering units comprise the current violation image frames.
Step 203: and determining a sending end of the current violation image frame according to the number of the violation image frames corresponding to the target catering unit.
Step 204: and sending the current violation image frame to the sending end, and receiving processing information fed back by the sending end according to the current violation image frame.
The implementation processes and technical principles of step 201 and step 101, step 202 and step 102, step 203 and step 103, and step 204 and step 104 are similar and will not be described herein again.
Step 205: and sending processing information to the processing equipment corresponding to the auditor, and receiving audit result information fed back by the processing equipment corresponding to the auditor according to the processing information.
Wherein the audit result information is used to indicate that the violation included in the current violation image frame has been corrected or that the violation included in the current violation image frame has not been corrected.
Specifically, the auditor in this embodiment may be a person in a higher-level unit of the regulatory unit of the target dining unit, or another person in the regulatory unit of the target dining unit. The processing device of the auditor can be a mobile terminal or a computer device.
After the processing device of the auditor receives the processing information, the auditor can audit the processing information by combining the current violation image frame. And the processing equipment of the auditor generates audit result information according to the audit result and feeds the audit result information back to the kitchen supervision device.
Step 206: when it is determined that the audit result information indicates that the violation included in the current violation image frame has been corrected, it is determined that the current image frame processing is ended.
Step 207: and when the auditing result information indicates that the violation behaviors included in the current violation image frame have not been corrected, returning to performing the step of sending the current violation image frame to the sending end.
In step 206, when it is determined that the violation included in the current violation image frame has been corrected, it is determined that the current image frame processing is ended; in step 207, when it is determined that the violation included in the current violation image frame has not been corrected, execution returns to step 204.
The kitchen supervision method provided by the embodiment can further realize the auditing of the processing information fed back by the dispatching end, and when the violation behaviors in the current violation image frame are determined not to be corrected, the current violation image frame is dispatched again to ensure that the violation behaviors in the current violation image frame are corrected, so that the supervision effect is further improved.
Fig. 3 is a schematic structural diagram of a kitchen supervision apparatus according to an embodiment of the present invention. As shown in fig. 3, the kitchen supervision device provided by this embodiment includes the following modules: a first determining module 31, a second determining module 32, a third determining module 33 and a transmitting and receiving module 34.
The first determining module 31 is configured to identify an image frame in the acquired kitchen video stream through a preset machine learning model, and obtain a current violation image frame.
Wherein the current violation image frame includes a violation.
And the second determining module 32 is configured to determine a target dining unit corresponding to the current violation image frame, and count the number of violation image frames corresponding to the target dining unit.
And the violation image frames corresponding to the target catering units comprise the current violation image frames.
Specifically, in terms of determining the target dining unit corresponding to the current violation image frame, the second determining module 32 is specifically configured to: and determining a target catering unit corresponding to the current violation image frame according to the target video acquisition device corresponding to the current violation image frame and the mapping relation between the video acquisition device and the catering unit.
And the third determining module 33 is configured to determine a sending end of the current violation image frame according to the number of violation image frames corresponding to the target dining unit.
Specifically, the third determining module 33 is specifically configured to: when the number of the violation image frames corresponding to the target catering unit is larger than a preset threshold value, determining a moving end of a first processing person as a sending end of the current violation image frame; and when the number of the violation image frames corresponding to the target catering unit is less than or equal to a preset threshold value, determining the moving end of the second processing personnel as the sending end of the current violation image frame.
Optionally, the first processing person is a supervisor of the target dining unit, and the second processing person is a responsible person of the target dining unit.
And the sending and receiving module 34 is configured to send the current violation image frame to the sending end, and receive processing information fed back by the sending end according to the current violation image frame.
Optionally, the apparatus further comprises: a sixth determining module and a display module.
And the sixth determining module is used for determining other attribute information except the target catering unit in the information corresponding to the current violation image frame. Wherein the other attribute information includes: at least one item of information of the address of the target catering unit, the type of the violation and the occurrence time of the violation.
And the display module is used for displaying the current violation image frame, the target catering unit corresponding to the current violation image frame and at least one item of other attribute information corresponding to the current violation image frame.
Further, the sending and receiving module 34 is further configured to: and when the fact that the processing information fed back by the dispatching end is not received within the preset time length is determined, sending reminding information to the dispatching end.
The kitchen supervision device provided by the embodiment of the invention can execute the kitchen supervision method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 4 is a schematic structural diagram of a kitchen supervision apparatus according to another embodiment of the present invention. The kitchen supervision device provided by the embodiment of the present invention is based on the kitchen supervision device provided by the embodiment shown in fig. 3 and various optional implementation manners, and other modules included in the kitchen supervision device are explained in detail. As shown in fig. 4, the kitchen supervision device provided in this embodiment further includes the following modules: a fourth determination module 41 and a fifth determination module 42.
The sending and receiving module 34 is further configured to send processing information to the processing device corresponding to the auditor, and receive audit result information fed back by the processing device corresponding to the auditor according to the processing information.
Wherein the audit result information is used to indicate that the violation included in the current violation image frame has been corrected or that the violation included in the current violation image frame has not been corrected.
A fourth determining module 41, configured to determine that the current image frame processing is finished when it is determined that the audit result information indicates that the violation included in the current violation image frame has been corrected.
A fifth determining module 42, configured to return to performing the step of sending the current violation image frame to the dispatching end when it is determined that the review result information indicates that the violation included in the current violation image frame has not been corrected.
The kitchen supervision device provided by the embodiment of the invention can execute the kitchen supervision method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention. As shown in fig. 5, the computer device comprises a processor 50 and a memory 51. The number of the processors 50 in the computer device may be one or more, and one processor 50 is taken as an example in fig. 5; the processor 50 and the memory 51 of the computer device may be connected by a bus or other means, as exemplified by the bus connection in fig. 5.
The memory 51 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions and modules corresponding to the kitchen supervision method in the embodiment of the present invention (for example, the first determining module 31, the second determining module 32, the third determining module 33, and the transmitting and receiving module 34 in the kitchen supervision apparatus). The processor 50 executes various functional applications of the computer device and the kitchen supervision method by running software programs, instructions and modules stored in the memory 51, i.e. implements the kitchen supervision method described above.
The memory 51 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 51 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 51 may further include memory located remotely from the processor 50, which may be connected to a computer device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The present invention also provides a storage medium containing computer-executable instructions which, when executed by a computer processor, perform a method of kitchen supervision, the method comprising:
identifying image frames in the collected kitchen video stream through a preset machine learning model to obtain current violation image frames; wherein the current violation image frame includes a violation;
determining a target catering unit corresponding to the current violation image frame, and counting the number of violation image frames corresponding to the target catering unit; the violation image frame corresponding to the target catering unit comprises the current violation image frame;
determining a sending end of the current violation image frame according to the number of violation image frames corresponding to the target catering unit;
and sending the current violation image frame to the sending end, and receiving processing information fed back by the sending end according to the current violation image frame.
Of course, the embodiment of the present invention provides a storage medium containing computer-executable instructions, and the computer-executable instructions are not limited to the operation of the method described above, and can also perform related operations in the kitchen supervision method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a computer device, or a network device) to execute the kitchen supervision method according to the embodiments of the present invention.
It should be noted that, in the embodiment of the kitchen supervision apparatus, the units and modules included in the embodiment are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method of kitchen supervision, the method comprising:
identifying image frames in the collected kitchen video stream through a preset machine learning model to obtain current violation image frames; wherein the current violation image frame includes a violation;
determining a target catering unit corresponding to the current violation image frame, and counting the number of violation image frames corresponding to the target catering unit; the violation image frame corresponding to the target catering unit comprises the current violation image frame;
determining a sending end of the current violation image frame according to the number of violation image frames corresponding to the target catering unit;
and sending the current violation image frame to the sending end, and receiving processing information fed back by the sending end according to the current violation image frame.
2. The method of claim 1, wherein after receiving processing information fed back by the dispatch terminal from the current violation image frame, the method further comprises:
sending the processing information to processing equipment corresponding to an auditor, and receiving audit result information fed back by the processing equipment corresponding to the auditor according to the processing information; wherein the audit result information is used to indicate that the violation included in the current violation image frame has been corrected, or that the violation included in the current violation image frame has not been corrected;
determining that the current image frame processing is finished when it is determined that the audit result information indicates that a violation included in the current violation image frame has been corrected;
when it is determined that the review result information indicates that the violation included in the current violation image frame has not been corrected, returning to performing the step of sending the current violation image frame to the dispatch terminal.
3. The method of claim 1, wherein determining the dispatch end of the current violation image frame according to the number of violation image frames corresponding to the target dining unit comprises:
when the number of the violation image frames corresponding to the target catering unit is larger than a preset threshold value, determining a moving end of a first processing person as an issuing end of the current violation image frame;
and when the number of the violation image frames corresponding to the target catering unit is smaller than or equal to the preset threshold, determining a mobile terminal of a second processing person as a sending terminal of the current violation image frame.
4. The method of claim 3, wherein the first processing person is a supervisory person of the target dining entity and the second processing person is a responsible person of the target dining entity.
5. The method of claim 1, wherein the determining a target dining unit corresponding to the current violation image frame comprises:
and determining a target catering unit corresponding to the current violation image frame according to the target video acquisition device corresponding to the current violation image frame and the mapping relation between the video acquisition device and the catering unit.
6. The method of any of claims 1-5, wherein after determining a target dining unit to which the current violation image frame corresponds, the method further comprises:
determining other attribute information except the target catering unit in the information corresponding to the current violation image frame; wherein the other attribute information includes: at least one item of information of the address of the target catering unit, the type of the violation and the occurrence time of the violation;
and displaying at least one of the current violation image frame, the target catering unit corresponding to the current violation image frame and other attribute information corresponding to the current violation image frame.
7. The method of any of claims 1-5, wherein after sending the current violation image frame to the dispatch origin, the method further comprises:
and sending reminding information to the sending end when the fact that the processing information fed back by the sending end is not received within the preset time length is determined.
8. A kitchen supervision apparatus, comprising:
the first determining module is used for identifying the image frames in the collected kitchen video stream through a preset machine learning model to obtain the current violation image frames; wherein the current violation image frame includes a violation;
the second determining module is used for determining a target catering unit corresponding to the current violation image frame and counting the number of violation image frames corresponding to the target catering unit; the violation image frame corresponding to the target catering unit comprises the current violation image frame;
the third determining module is used for determining a sending end of the current violation image frame according to the number of the violation image frames corresponding to the target catering unit;
and the sending and receiving module is used for sending the current violation image frame to the sending end and receiving processing information fed back by the sending end according to the current violation image frame.
9. A computer device, characterized in that the computer device comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the cook supervision method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of kitchen supervision according to any one of claims 1-7.
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