CN116227897A - Food sample detection data processing method and system - Google Patents
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Abstract
The invention relates to the technical field of food detection, and particularly discloses a food sample detection data processing method and system. The invention identifies the food area by carrying out the identification of the food area; performing periodic vending record and classification; sampling planning is carried out, and standard sampling planning data and nonstandard sampling planning data are generated; sampling and detecting a standard food area and a nonstandard food area respectively to generate standard food detection data and nonstandard food detection data; and generating and displaying food detection information of the unmanned food supermarket. The automatic food sampling and detecting device can sample and plan through periodical vending records and classification, generates standard sampling planning data and nonstandard sampling planning data, respectively samples and detects a standard food area and a nonstandard food area, generates and displays food detection information of an unmanned food supermarket, and utilizes the automatic loading and unloading and commodity identification functions of the unmanned food supermarket to automatically sample and detect food of the unmanned food supermarket, so that food with potential safety hazards is avoided.
Description
Technical Field
The invention belongs to the technical field of food detection, and particularly relates to a food sample detection data processing method and system.
Background
Food detection is divided into two aspects of generalized food detection and narrow food detection, specifically, the generalized food detection refers to research and evaluation of quality and change of food according to related knowledge and specific technology in physics, chemistry and biology; the narrow food inspection refers to inspection of food for internal and external packaging, food identification, quality, appearance characteristics of the final commodity, physical and chemical indicators, and other aspects of hygiene.
For a common food supermarket, normally, supermarket staff regularly carries out inner and outer packaging and food identification management on food on an upper shelf, and regularly checks and processes the quality guarantee period of the food, and for an unmanned food supermarket, as the upper shelf, selling and lower shelf of the food are all automatically carried out, in the prior art, the food sample detection technology for the unmanned food supermarket is not available, and the effective processing of food sample detection data of the unmanned food supermarket cannot be carried out, so that food with potential safety hazards possibly exists.
Disclosure of Invention
The embodiment of the invention aims to provide a food sample detection data processing method and system, which aim to solve the problems in the background technology.
In order to achieve the above object, the embodiment of the present invention provides the following technical solutions:
a method for processing food sample detection data, the method specifically comprising the steps of:
acquiring upper frame record data of an unmanned food supermarket, identifying a food region, and determining a standard food region and a nonstandard food region;
carrying out periodic selling record and classification on the unmanned food supermarket to generate standard food selling data and nonstandard food selling data;
sampling planning is carried out according to the standard food selling data and the nonstandard food selling data, and standard sampling planning data and nonstandard sampling planning data are generated;
according to the standard sampling planning data and the nonstandard sampling planning data, sampling and detecting the standard food area and the nonstandard food area respectively to generate standard food detection data and nonstandard food detection data;
and integrating the standard food detection data and the nonstandard food detection data to generate and display food detection information of the unmanned food supermarket.
By further limiting the technical scheme of the embodiment of the invention, the method for acquiring the on-shelf record data of the unmanned food supermarket, carrying out the identification of the food region and determining the standard food region and the nonstandard food region specifically comprises the following steps:
acquiring the on-shelf record data of an unmanned food supermarket;
classifying the racking record data to obtain standard food racking data and nonstandard food racking data;
identifying and determining a standard food area according to the standard food shelf data;
and identifying and determining a nonstandard food area according to the nonstandard food shelf data.
By further limiting the technical scheme of the embodiment of the invention, the method for carrying out periodic selling record and classification on the unmanned food supermarket to generate standard food selling data and nonstandard food selling data specifically comprises the following steps:
receiving a set sample detection period;
according to the sample detection period, carrying out periodic selling record on the unmanned food supermarket to obtain selling record data;
and classifying the vending record data to obtain standard food vending data and nonstandard food vending data.
As a further limitation of the technical solution of the embodiment of the present invention, the sampling planning is performed according to the standard food vending data and the nonstandard food vending data, and the generating standard sampling planning data and nonstandard sampling planning data specifically includes the following steps:
according to the standard food selling data, selling and arranging a plurality of standard foods to generate standard food arrangement data;
sampling planning is carried out according to the standard food arrangement data, and standard sampling planning data are generated;
according to the nonstandard food vending data, vending and arranging a plurality of nonstandard foods to generate nonstandard food arrangement data;
and carrying out sampling planning according to the nonstandard food arrangement data to generate nonstandard sampling planning data.
As a further limitation of the technical solution of the embodiment of the present invention, the sampling and detecting the standard food area and the nonstandard food area according to the standard sampling planning data and the nonstandard sampling planning data, respectively, to generate standard food detection data and nonstandard food detection data specifically includes the following steps:
generating a standard sampling planning instruction according to the standard sampling planning data;
sampling and detecting the standard food area according to the standard sampling planning instruction to generate standard food detection data;
generating a non-standard sampling planning instruction according to the non-standard sampling planning data;
according to the nonstandard sampling planning instruction, carrying out food sampling on the nonstandard food region to obtain a plurality of nonstandard food samples;
and acquiring nonstandard food detection data obtained by detecting a plurality of nonstandard food samples.
As further defined by the technical solution of the embodiment of the present invention, the step of generating and displaying food detection information of an unmanned food supermarket by integrating the standard food detection data and the nonstandard food detection data specifically includes the following steps:
acquiring a food detection result according to the standard food detection data and the nonstandard food detection data;
generating food detection information according to the food detection result;
and displaying the food detection information in an unmanned food supermarket.
A food sample detection data processing system, the system comprising a food region identification unit, a sales record classification unit, a food sampling planning unit, a sampling detection processing unit and a detection information display unit, wherein:
the food area identification unit is used for acquiring the on-shelf record data of the unmanned food supermarket, carrying out food area identification and determining a standard food area and a nonstandard food area;
the vending record classification unit is used for carrying out periodic vending record and classification on the unmanned food supermarket and generating standard food vending data and nonstandard food vending data;
the food sampling planning unit is used for carrying out sampling planning according to the standard food selling data and the nonstandard food selling data to generate standard sampling planning data and nonstandard sampling planning data;
the sampling detection processing unit is used for respectively carrying out sampling detection on the standard food area and the nonstandard food area according to the standard sampling planning data and the nonstandard sampling planning data to generate standard food detection data and nonstandard food detection data;
the detection information display unit is used for synthesizing the standard food detection data and the nonstandard food detection data to generate and display food detection information of the unmanned food supermarket.
As a further limitation of the technical solution of the embodiment of the present invention, the food area identifying unit specifically includes:
the data acquisition module is used for acquiring the on-shelf record data of the unmanned food supermarket;
the record classification processing module is used for classifying the on-shelf record data to obtain standard food on-shelf data and nonstandard food on-shelf data;
the standard area identification module is used for identifying and determining a standard food area according to the standard food shelf data;
and the nonstandard area identification module is used for identifying and determining nonstandard food areas according to the nonstandard food shelf data.
As a further limitation of the technical solution of the embodiment of the present invention, the sales record classification unit specifically includes:
the device comprises a setting receiving module, a sampling detection module and a sampling detection module, wherein the setting receiving module is used for receiving a set sample detection period;
the vending record module is used for carrying out periodic vending record on the unmanned food supermarket according to the sample detection period to obtain vending record data;
and the selling classification processing module is used for classifying the selling record data to obtain standard food selling data and nonstandard food selling data.
As a further limitation of the technical solution of the embodiment of the present invention, the food sampling planning unit specifically includes:
the standard food vending arrangement module is used for vending and arranging a plurality of standard foods according to the standard food vending data to generate standard food arrangement data;
the standard sampling planning module is used for carrying out sampling planning according to the standard food arrangement data to generate standard sampling planning data;
the nonstandard food selling arrangement module is used for selling and arranging a plurality of nonstandard foods according to the nonstandard food selling data to generate nonstandard food arrangement data;
and the nonstandard sampling planning module is used for carrying out sampling planning according to the nonstandard food arrangement data to generate nonstandard sampling planning data.
Compared with the prior art, the invention has the beneficial effects that:
according to the embodiment of the invention, the food region is identified; performing periodic vending record and classification; sampling planning is carried out, and standard sampling planning data and nonstandard sampling planning data are generated; sampling and detecting a standard food area and a nonstandard food area respectively to generate standard food detection data and nonstandard food detection data; and generating and displaying food detection information of the unmanned food supermarket. The automatic food sampling and detecting device can sample and plan through periodical vending records and classification, generates standard sampling planning data and nonstandard sampling planning data, respectively samples and detects a standard food area and a nonstandard food area, generates and displays food detection information of an unmanned food supermarket, and utilizes the automatic loading and unloading and commodity identification functions of the unmanned food supermarket to automatically sample and detect food of the unmanned food supermarket, so that food with potential safety hazards is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 shows a flowchart of a method provided by an embodiment of the present invention.
Fig. 2 shows a flowchart of food area identification in the method according to the embodiment of the present invention.
Fig. 3 shows a flowchart of a periodic vending record and classification in a method according to an embodiment of the present invention.
Fig. 4 shows a flowchart of food sampling planning in the method according to the embodiment of the present invention.
Fig. 5 shows a flowchart of food sampling detection in the method according to the embodiment of the present invention.
Fig. 6 shows a flowchart of generating display food detection information in the method according to the embodiment of the present invention.
Fig. 7 shows an application architecture diagram of a system provided by an embodiment of the present invention.
Fig. 8 shows a block diagram of a food region identification unit in the system according to an embodiment of the present invention.
Fig. 9 is a block diagram of a vending record classification unit in a system according to an embodiment of the present invention.
Fig. 10 shows a block diagram of a food sampling planning unit in the system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It can be understood that for a general food supermarket, usually supermarket staff regularly performs inner and outer packaging and food identification management on food on an upper shelf, and regularly checks and processes the quality guarantee period of the food, while for an unmanned food supermarket, since the upper shelf, selling and lower shelf of the food are all performed automatically, in the prior art, food sample detection technology for the unmanned food supermarket is not generally available, effective processing of food sample detection data of the unmanned food supermarket cannot be performed, and thus food with potential safety hazards may exist.
In order to solve the problems, the embodiment of the invention performs food area identification by acquiring the on-shelf record data of the unmanned food supermarket; performing periodic vending record and classification; sampling planning is carried out, and standard sampling planning data and nonstandard sampling planning data are generated; sampling and detecting a standard food area and a nonstandard food area respectively to generate standard food detection data and nonstandard food detection data; and generating and displaying food detection information of the unmanned food supermarket. The automatic food sampling and detecting device can sample and plan through periodical vending records and classification, generates standard sampling planning data and nonstandard sampling planning data, respectively samples and detects a standard food area and a nonstandard food area, generates and displays food detection information of an unmanned food supermarket, and utilizes the automatic loading and unloading and commodity identification functions of the unmanned food supermarket to automatically sample and detect food of the unmanned food supermarket, so that food with potential safety hazards is avoided.
Fig. 1 shows a flowchart of a method provided by an embodiment of the present invention.
In particular, in a preferred embodiment provided by the invention, a food sample detection data processing method specifically comprises the following steps:
step S101, acquiring the on-shelf record data of the unmanned food supermarket, identifying the food region, and determining the standard food region and the nonstandard food region.
In the embodiment of the invention, an automatic racking mechanical arm or an automatic racking robot is arranged in an unmanned food supermarket to automatically rack food commodities, in the racking process of the food commodities, the commodities are identified and recorded, racking record data are stored, the racking record data of the unmanned food supermarket are obtained, classification processing is carried out on the racking record data according to the types of the food commodities (specifically including standard foods and nonstandard foods) to obtain standard food racking data and nonstandard food racking data, the racking position corresponding to the standard food racking data is determined, the standard food area in the unmanned food supermarket is marked, the racking position corresponding to the nonstandard food racking data is determined, and the nonstandard food area in the unmanned food supermarket is marked.
It is understood that the unmanned food supermarket can be a mini-supermarket with a store for unmanned food selling; or can be a small-sized sales counter such as a cabinet, a refrigerator and the like for food unmanned sales.
It will be appreciated that standard food is a food commodity with standard packaging, a regular manufacturer, for example: cola, potato chips, biscuits, etc.; nonstandard food is a food commodity without standard packaging, such as: vegetables, fruits, instant bread, etc.
Specifically, fig. 2 shows a flowchart of food area identification in the method provided by the embodiment of the invention.
In the preferred embodiment provided by the invention, the method for acquiring the on-shelf record data of the unmanned food supermarket, carrying out the food region identification, and determining the standard food region and the nonstandard food region specifically comprises the following steps:
and S1011, acquiring the on-shelf record data of the unmanned food supermarket.
Step S1012, classifying the shelf record data to obtain standard food shelf data and nonstandard food shelf data.
Step S1013, identifying and determining standard food areas according to the standard food shelf data.
Step S1014, identifying and determining non-standard food areas according to the non-standard food shelf data.
Further, the food sample detection data processing method further comprises the following steps:
step S102, carrying out periodic selling record and classification on the unmanned food supermarket to generate standard food selling data and nonstandard food selling data.
In the embodiment of the invention, the vending record of the unmanned food supermarket is periodically tidied according to the sample detection period by receiving the set sample detection period, vending record data of one sample detection period is obtained, and the vending record data is classified according to standard food and nonstandard food to obtain standard food vending data and nonstandard food vending data.
Specifically, fig. 3 shows a flowchart of periodic vending records and classification in the method provided by the embodiment of the present invention.
In the preferred embodiment provided by the invention, the method for recording and classifying the periodic vending of the unmanned food supermarket and generating the standard food vending data and the nonstandard food vending data specifically comprises the following steps:
in step S1021, a set sample detection period is received.
Step S1022, according to the sample detection period, carrying out periodic selling record on the unmanned food supermarket to obtain selling record data.
Step S1023, classifying the vending record data to obtain standard food vending data and nonstandard food vending data.
Further, the food sample detection data processing method further comprises the following steps:
and step S103, carrying out sampling planning according to the standard food vending data and the nonstandard food vending data, and generating standard sampling planning data and nonstandard sampling planning data.
In the embodiment of the invention, the selling number of the plurality of standard foods corresponding to the standard foods in an unmanned food supermarket and a sample detection period is determined by analyzing the selling data of the standard foods, the plurality of standard foods are sold and arranged according to the plurality of standard foods and the corresponding selling number of the standard foods to generate standard food arrangement data, further five final standard foods which are arranged in the last are screened out from the plurality of standard foods according to the standard food arrangement data, marked as standard sampling commodities, and sampling planning is performed according to the plurality of standard sampling commodities to generate standard sampling planning data; and the nonstandard food vending data are analyzed to determine the vending quantity corresponding to a plurality of nonstandard foods in an unmanned food supermarket, the nonstandard foods are vended and arranged according to the nonstandard foods and the corresponding vending quantity thereof, nonstandard food arrangement data are generated, the final ten nonstandard foods are screened out from the nonstandard foods according to the nonstandard food arrangement data, the final nonstandard foods are marked as nonstandard sampling commodities, and then sampling planning is carried out according to the nonstandard sampling commodities to generate nonstandard sampling planning data.
Specifically, fig. 4 shows a flowchart of food sampling planning in the method provided by the embodiment of the invention.
In a preferred embodiment of the present invention, the sampling plan is performed according to the standard food vending data and the nonstandard food vending data, and the generating standard sampling plan data and nonstandard sampling plan data specifically includes the following steps:
step S1031, according to the standard food vending data, vending and arranging the plurality of standard foods to generate standard food arrangement data.
And S1032, carrying out sampling planning according to the standard food arrangement data to generate standard sampling planning data.
And step S1033, according to the nonstandard food vending data, vending and arranging a plurality of nonstandard foods to generate nonstandard food arrangement data.
And step S1034, carrying out sampling planning according to the nonstandard food arrangement data, and generating nonstandard sampling planning data.
Further, the food sample detection data processing method further comprises the following steps:
and step S104, respectively carrying out sampling detection on the standard food area and the nonstandard food area according to the standard sampling planning data and the nonstandard sampling planning data to generate standard food detection data and nonstandard food detection data.
According to the embodiment of the invention, according to standard sampling planning data, the sampling positions of five standard foods are determined in a standard food area to generate a standard sampling planning instruction, and then the standard sampling planning instruction is sent to an automatic racking mechanical arm or an automatic racking robot, so that the automatic racking mechanical arm or the automatic racking robot sequentially samples at the sampling positions of the five standard foods according to the standard sampling planning instruction, and sequentially identifies the production date, appearance form and the like of the five standard foods, detects and judges whether the problems of expiration, breakage, air leakage and the like of the foods exist, records the problems, and generates standard food detection data; according to the nonstandard sampling planning data, ten sampling positions of nonstandard foods are determined in a nonstandard food area, nonstandard sampling planning instructions are generated, and then the nonstandard sampling planning instructions are sent to an automatic racking mechanical arm or an automatic racking robot, so that the automatic racking mechanical arm or the automatic racking robot sequentially samples at the ten sampling positions of the nonstandard foods according to the nonstandard sampling planning instructions, corresponding nonstandard food samples are obtained, and then sent to sample detection positions, sample detection is carried out through sample detection equipment, and further nonstandard food detection data obtained through sample detection are obtained.
Specifically, fig. 5 shows a flowchart of food sampling detection in the method provided by the embodiment of the invention.
In a preferred embodiment of the present invention, the sampling and detecting the standard food area and the nonstandard food area according to the standard sampling and planning data and the nonstandard sampling and planning data, respectively, to generate standard food detection data and nonstandard food detection data specifically includes the following steps:
step S1041, generating a standard sampling plan instruction according to the standard sampling plan data.
Step S1042, according to the standard sampling plan instruction, sampling and detecting the standard food area to generate standard food detection data.
Step S1043, generating a non-standard sampling planning instruction according to the non-standard sampling planning data.
Step S1044, performing food sampling on the non-standard food area according to the non-standard sampling planning instruction, so as to obtain a plurality of non-standard food samples.
Step S1045, obtaining non-standard food detection data obtained by detecting a plurality of non-standard food samples.
Further, the food sample detection data processing method further comprises the following steps:
step S105, integrating the standard food detection data and the nonstandard food detection data to generate and display food detection information of the unmanned food supermarket.
In the embodiment of the invention, corresponding detection conclusions in standard food detection data and nonstandard food detection data are extracted, and summarized and recorded to obtain food detection results, the food detection results are represented according to a preset detection information frame, food detection information is generated, and then the generated food detection information is sent to a food supervision display screen of an unmanned food supermarket, and the food detection information is displayed in the food supervision display screen.
Specifically, fig. 6 shows a flowchart of generating display food detection information in the method provided by the embodiment of the invention.
In the preferred embodiment provided by the invention, the step of generating and displaying food detection information of the unmanned food supermarket by integrating the standard food detection data and the nonstandard food detection data specifically comprises the following steps:
step S1051, obtaining a food detection result according to the standard food detection data and the nonstandard food detection data.
Step S1052, generating food detection information according to the food detection result.
And step S1053, displaying the food detection information in an unmanned food supermarket.
Further, fig. 7 shows an application architecture diagram of the system provided by the embodiment of the present invention.
In another preferred embodiment of the present invention, a food sample detection data processing system includes:
the food area identifying unit 101 is configured to acquire the on-shelf record data of the unmanned food supermarket, identify the food area, and determine the standard food area and the nonstandard food area.
In the embodiment of the invention, an automatic racking mechanical arm or an automatic racking robot is arranged in an unmanned food supermarket to automatically rack food commodities, in the racking process of the food commodities, the commodities are identified and recorded, racking record data are stored, a food area identification unit 101 is used for carrying out classification processing on the racking record data according to the types of the food commodities (specifically including standard foods and nonstandard foods) by acquiring the racking record data of the unmanned food supermarket, obtaining the standard food racking data and nonstandard food racking data, further marking a standard food area in the unmanned food supermarket by determining a racking position corresponding to the standard food racking data, marking a racking position corresponding to the nonstandard food racking data, and marking a nonstandard food area in the unmanned food supermarket.
Specifically, fig. 8 shows a block diagram of the structure of the food region identifying unit 101 in the system according to the embodiment of the present invention.
In a preferred embodiment provided by the present invention, the food area identifying unit 101 specifically includes:
the data acquisition module 1011 is used for acquiring the on-shelf record data of the unmanned food supermarket.
And the record classification processing module 1012 is used for classifying the shelf record data to obtain standard food shelf data and nonstandard food shelf data.
And the standard area identifying module 1013 is configured to identify and determine a standard food area according to the standard food shelf data.
The nonstandard area identifying module 1014 is configured to identify and determine a nonstandard food area according to the nonstandard food shelf data.
Further, the food sample detection data processing system further comprises:
the vending record classification unit 102 is configured to perform periodic vending record and classification on the unmanned food supermarket, and generate standard food vending data and nonstandard food vending data.
In the embodiment of the present invention, the vending record classifying unit 102 periodically sorts the vending record of the unmanned food supermarket according to the sample detection period by receiving the set sample detection period, to obtain vending record data of one sample detection period, and classifies the vending record data according to the standard food and the nonstandard food to obtain the standard food vending data and the nonstandard food vending data.
Specifically, fig. 9 shows a block diagram of a vending record classification unit 102 in the system according to an embodiment of the present invention.
In a preferred embodiment of the present invention, the sales record classification unit 102 specifically includes:
a receiving module 1021 is configured to receive a set sample detection period.
And the vending record module 1022 is configured to perform periodic vending record on the unmanned food supermarket according to the sample detection period, so as to obtain vending record data.
And the selling classification processing module 1023 is used for classifying the selling record data to obtain standard food selling data and nonstandard food selling data.
Further, the food sample detection data processing system further comprises:
and the food sampling planning unit 103 is configured to perform sampling planning according to the standard food vending data and the nonstandard food vending data, and generate standard sampling planning data and nonstandard sampling planning data.
In the embodiment of the invention, the food sampling planning unit 103 analyzes the standard food vending data to determine the vending number corresponding to a plurality of standard foods in an unmanned food supermarket and a sample detection period, vends and arranges the plurality of standard foods according to the plurality of standard foods and the vending number corresponding to the standard foods to generate standard food arrangement data, screens out five final standard foods from the plurality of standard foods according to the standard food arrangement data, marks the five final standard foods as standard sampling commodities, and performs sampling planning according to the plurality of standard sampling commodities to generate standard sampling planning data; and the nonstandard food vending data are analyzed to determine the vending quantity corresponding to a plurality of nonstandard foods in an unmanned food supermarket, the nonstandard foods are vended and arranged according to the nonstandard foods and the corresponding vending quantity thereof, nonstandard food arrangement data are generated, the final ten nonstandard foods are screened out from the nonstandard foods according to the nonstandard food arrangement data, the final nonstandard foods are marked as nonstandard sampling commodities, and then sampling planning is carried out according to the nonstandard sampling commodities to generate nonstandard sampling planning data.
Specifically, fig. 10 shows a block diagram of a food sampling planning unit 103 in the system according to an embodiment of the present invention.
In a preferred embodiment of the present invention, the food sampling plan unit 103 specifically includes:
the standard food vending arrangement module 1031 is configured to vend and arrange a plurality of standard foods according to the standard food vending data, and generate standard food arrangement data.
And the standard sampling planning module 1032 is used for carrying out sampling planning according to the standard food arrangement data to generate standard sampling planning data.
And the non-standard selling arrangement module 1033 is configured to sell and arrange a plurality of non-standard foods according to the non-standard food selling data, and generate non-standard food arrangement data.
And a non-standard sampling planning module 1034, configured to perform sampling planning according to the non-standard food arrangement data, and generate non-standard sampling planning data.
Further, the food sample detection data processing system further comprises:
the sampling detection processing unit 104 is configured to sample and detect the standard food area and the nonstandard food area according to the standard sampling planning data and the nonstandard sampling planning data, and generate standard food detection data and nonstandard food detection data.
In the embodiment of the invention, the sampling detection processing unit 104 determines sampling positions of five standard foods in a standard food area according to standard sampling planning data, generates a standard sampling planning instruction, and then sends the standard sampling planning instruction to the automatic racking mechanical arm or the automatic racking robot, so that the automatic racking mechanical arm or the automatic racking robot sequentially samples at the sampling positions of the five standard foods according to the standard sampling planning instruction, sequentially identifies production dates, appearance forms and the like of the five standard foods, detects and judges whether the problems of food expiration, breakage, air leakage and the like exist, records the problems, and generates standard food detection data; according to the nonstandard sampling planning data, ten sampling positions of nonstandard foods are determined in a nonstandard food area, nonstandard sampling planning instructions are generated, and then the nonstandard sampling planning instructions are sent to an automatic racking mechanical arm or an automatic racking robot, so that the automatic racking mechanical arm or the automatic racking robot sequentially samples at the ten sampling positions of the nonstandard foods according to the nonstandard sampling planning instructions, corresponding nonstandard food samples are obtained, and then sent to sample detection positions, sample detection is carried out through sample detection equipment, and further nonstandard food detection data obtained through sample detection are obtained.
And the detection information display unit 105 is used for integrating the standard food detection data and the nonstandard food detection data to generate and display food detection information of the unmanned food supermarket.
In the embodiment of the invention, the detection information display unit 105 extracts corresponding detection conclusions in the standard food detection data and the nonstandard food detection data, performs summary recording to obtain a food detection result, performs information representation on the food detection result according to a preset detection information frame, generates food detection information, and further sends the generated food detection information to a food supervision display screen of the unmanned food supermarket, and displays the food detection information in the food supervision display screen.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A method for processing food sample detection data, which is characterized by comprising the following steps:
acquiring upper frame record data of an unmanned food supermarket, identifying a food region, and determining a standard food region and a nonstandard food region;
carrying out periodic selling record and classification on the unmanned food supermarket to generate standard food selling data and nonstandard food selling data;
sampling planning is carried out according to the standard food selling data and the nonstandard food selling data, and standard sampling planning data and nonstandard sampling planning data are generated;
according to the standard sampling planning data and the nonstandard sampling planning data, sampling and detecting the standard food area and the nonstandard food area respectively to generate standard food detection data and nonstandard food detection data;
and integrating the standard food detection data and the nonstandard food detection data to generate and display food detection information of the unmanned food supermarket.
2. The method for processing food sample detection data according to claim 1, wherein the step of acquiring the on-shelf record data of the unmanned food supermarket, performing food region identification, and determining the standard food region and the nonstandard food region specifically comprises the following steps:
acquiring the on-shelf record data of an unmanned food supermarket;
classifying the racking record data to obtain standard food racking data and nonstandard food racking data;
identifying and determining a standard food area according to the standard food shelf data;
and identifying and determining a nonstandard food area according to the nonstandard food shelf data.
3. The method for processing food sample detection data according to claim 1, wherein the steps of performing periodic vending record and classification on the unmanned food supermarket to generate standard food vending data and nonstandard food vending data specifically comprise the following steps:
receiving a set sample detection period;
according to the sample detection period, carrying out periodic selling record on the unmanned food supermarket to obtain selling record data;
and classifying the vending record data to obtain standard food vending data and nonstandard food vending data.
4. The method for processing food sample detection data according to claim 1, wherein the step of performing sampling planning according to the standard food vending data and the nonstandard food vending data, and generating standard sampling planning data and nonstandard sampling planning data specifically comprises the steps of:
according to the standard food selling data, selling and arranging a plurality of standard foods to generate standard food arrangement data;
sampling planning is carried out according to the standard food arrangement data, and standard sampling planning data are generated;
according to the nonstandard food vending data, vending and arranging a plurality of nonstandard foods to generate nonstandard food arrangement data;
and carrying out sampling planning according to the nonstandard food arrangement data to generate nonstandard sampling planning data.
5. The method for processing food sample detection data according to claim 1, wherein the sampling detection is performed on the standard food region and the nonstandard food region according to the standard sampling planning data and the nonstandard sampling planning data, respectively, and the step of generating standard food detection data and nonstandard food detection data specifically comprises the following steps:
generating a standard sampling planning instruction according to the standard sampling planning data;
sampling and detecting the standard food area according to the standard sampling planning instruction to generate standard food detection data;
generating a non-standard sampling planning instruction according to the non-standard sampling planning data;
according to the nonstandard sampling planning instruction, carrying out food sampling on the nonstandard food region to obtain a plurality of nonstandard food samples;
and acquiring nonstandard food detection data obtained by detecting a plurality of nonstandard food samples.
6. The food sample detection data processing method according to claim 1, wherein the step of integrating the standard food detection data and the nonstandard food detection data to generate and display food detection information of an unmanned food supermarket specifically comprises the steps of:
acquiring a food detection result according to the standard food detection data and the nonstandard food detection data;
generating food detection information according to the food detection result;
and displaying the food detection information in an unmanned food supermarket.
7. Food sample detects data processing system, its characterized in that, the system includes food region identification unit, sells record classification unit, food sampling planning unit, sampling and detects processing unit and detection information show unit, wherein:
the food area identification unit is used for acquiring the on-shelf record data of the unmanned food supermarket, carrying out food area identification and determining a standard food area and a nonstandard food area;
the vending record classification unit is used for carrying out periodic vending record and classification on the unmanned food supermarket and generating standard food vending data and nonstandard food vending data;
the food sampling planning unit is used for carrying out sampling planning according to the standard food selling data and the nonstandard food selling data to generate standard sampling planning data and nonstandard sampling planning data;
the sampling detection processing unit is used for respectively carrying out sampling detection on the standard food area and the nonstandard food area according to the standard sampling planning data and the nonstandard sampling planning data to generate standard food detection data and nonstandard food detection data;
the detection information display unit is used for synthesizing the standard food detection data and the nonstandard food detection data to generate and display food detection information of the unmanned food supermarket.
8. The food sample detection data processing system of claim 7, wherein the food region identification unit specifically comprises:
the data acquisition module is used for acquiring the on-shelf record data of the unmanned food supermarket;
the record classification processing module is used for classifying the on-shelf record data to obtain standard food on-shelf data and nonstandard food on-shelf data;
the standard area identification module is used for identifying and determining a standard food area according to the standard food shelf data;
and the nonstandard area identification module is used for identifying and determining nonstandard food areas according to the nonstandard food shelf data.
9. The food sample detection data processing system of claim 7, wherein the sales record classification unit specifically comprises:
the device comprises a setting receiving module, a sampling detection module and a sampling detection module, wherein the setting receiving module is used for receiving a set sample detection period;
the vending record module is used for carrying out periodic vending record on the unmanned food supermarket according to the sample detection period to obtain vending record data;
and the selling classification processing module is used for classifying the selling record data to obtain standard food selling data and nonstandard food selling data.
10. The food sample detection data processing system of claim 7, wherein the food sample planning unit specifically comprises:
the standard food vending arrangement module is used for vending and arranging a plurality of standard foods according to the standard food vending data to generate standard food arrangement data;
the standard sampling planning module is used for carrying out sampling planning according to the standard food arrangement data to generate standard sampling planning data;
the nonstandard food selling arrangement module is used for selling and arranging a plurality of nonstandard foods according to the nonstandard food selling data to generate nonstandard food arrangement data;
and the nonstandard sampling planning module is used for carrying out sampling planning according to the nonstandard food arrangement data to generate nonstandard sampling planning data.
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