CN103808764A - Open food safety inspection system based on mobile phone platform - Google Patents

Abstract

The invention discloses an open-type food safety inspection system based on a mobile phone platform. The system comprises food to be measured 0, a food safety inspection information collection device 1, bluetooth communication 2 (or a USB interface), a mobile phone 3, GPRS communication 4, Internet 5, a food safety inspection database 6 and image cloud computing 7. The one-dimensional information respectively collected by the food safety inspection information collection device is analyzed directly by a piece of fuzzy iterative self-organizing data analysis program installed in the mobile phone to obtain inspection results. The mobile phone operates the host of the food safety inspection center database, and the image information is calculated through the image cloud computing to obtain the inspection result. The system provided by the invention has the advantages of simple and practical structure and convenient operation, and can form a real food safety socialization network.

Description

Open food safety detecting system based on cell phone platform

Technical field

The present invention's design is a large food safety detection system, and it is the large system of an opening, and this common instrument of application mobile phone, as the carrying tool of core, forms the food safety detection of community network.

Background technology

Food security is related to the mankind's healthy even life security, and food-safety problem is subject to the great attention of society and government day by day.But food security relates to complicacy, the global question in multiple fields such as agricultural, animal husbandry and commercial production, and it not only needs effective system and strict management, also needs effective, advanced modern technologies support.

Current disclosed food inspection system is all applying RFID technology, adds that wireless sensor network technology realization detects and monitoring certain the concrete link in industry or industry in food production.

Food inspection Che Shi food superintendent office is special, and the dining room of each office, enterprise, school has also been equipped with corresponding food-safety detecting instrument device, but does not form a food safety detection system and network.Moreover these operations are all to be operated by full-time member, and sensing range is little, cost is high, does not form the food safety detection situation of Involvement of public community, also brings very important problem to food safety detection.

Summary of the invention

The invention discloses the open food safety detecting system based on cell phone platform.Object of the present invention, in view of present situation and the importance thereof of food security situation, overcomes existing food safety detection deficiency.Fully application cell phone platform advantage, by GPRS and Internet, combines, and applies cloud computing mode hypograph searching system, provides technical support for realizing food security socialized supervision.

To achieve these goals, the invention provides the open food safety detecting system based on cell phone platform, system of the present invention forms as accompanying drawing 1, food 0 to be detected, food safety detection information collecting device 1, Bluetooth communication (or USB interface) 2, mobile phone 3, GPRS communication 4 and Internet5,, food safety detection database 6, image cloud computing method 7 etc.

Further improvement of the present invention is:

1. food safety detection information collecting device is as accompanying drawing 2, its composition: combined sensor 8 (containing resistivity sensor, ultrasonic sensor, near infrared light sensor, micro-fluorescent optical sensor), signal conditioning circuit 9, A/D change-over circuit 10, bluetooth communication (or USB interface) 11.

2. at mobile phone 4 mounting portion food safety detection softwares, consider the ability to bear of mobile phone, the software of the present invention's exploitation is only processed the data of ultrasonic sensor and electric resistance sensor collection, adopt the algorithm of fuzzy iteration self-organization data analysis to process, can detect at the scene, mobile phone sends to testing result in food safety detection database automatically.

3. nearly the image of the collection of infrared image sensor and micro-fluoroscopic image sensor is sent to food safety detection database 7 through mobile phone 4, identify calculating by image cloud computing, database automatically records and sends food safety detection information simultaneously, sets up corresponding detection archives.

The open food safety detecting system that the present invention is based on cell phone platform has following advantage:

1. the realization of food security remote detection can, by means of much equipment, have various forms.Why select based on cell phone platform, reason is just that mobile phone now arrives widespread use global, and only needs less cost just can complete communication and data processing task.

Mobile phone convenient, flexibly, be easy to as public use, select that mobile phone is realized high-level efficiency, food safety detection is a good selection cheaply, and be easy to form the food safety supervision network of socialization, the law of food safety is fulfilled conscientiously.

3. the food safety detection function of exploitation mobile phone is extremely important application, and the food safety detection equipment based on mobile phone and service are introduced to the market and be of great immediate significance and application prospect.

4. this system is an open system, along with photoelectromagnetic biosensor technique, the development of micro mechanical system (MEMS) technology, and the technical progress of mobile phone itself, the application of this system will be more extensive, and practicality, reliability in the time of food safety detection all can improve constantly.

5. device for detecting safety of foods and mobile phone are realized short-distance wireless communication, have USB interface for subsequent use, make that system availability is good, reliability is high.

6. food safety detection of the invention process is all physical detection methods, without any need for chemical reagent, meets the requirement that reduces discharge, protection of the environment.

Any need to be described especially, the slot of reserved some in food security information harvester, along with the development of biometric image sensor and image processing techniques thereof, the parameter of food safety detection is more and more, accuracy is more and more higher, and this is also the open objective requirement of the present invention.Meanwhile also the sensors such as some light, sound, electromagnetism can be built in mobile phone.Formulate corresponding cell phone platform food safety detection standard, make can recycling rate of waterused the improving of resource, function be easy to expansion, reduce development and maintenance expense.

Accompanying drawing explanation

Fig. 1. the exploitation formula food safety detection system composition diagram based on cell phone platform

Fig. 2. food security information harvester composition diagram

Fig. 3. food safety detection image indexing system structural drawing under cloud computing mode

Fig. 4. high speed signal modulate circuit block diagram

Fig. 5. image-signal processing system block diagram

In figure: food 0 to be detected, food safety monitoring information detector 1, Bluetooth communication 2, mobile phone 3, GPRS4, Internet5, food safety monitoring central database 6, cloud computing method 7, combination sensor (near infrared cmos sensor; Micro-fluorescent optical sensor; Ultrasonic sensor; Electric resistance sensor) 8, signal conditioning circuit 9, A/D circuit 10, bluetooth communication 11, power supply 12, fire wall 13, custom system terminal and application program 14

Embodiment

Below in conjunction with accompanying drawing, the open food safety detecting system that the present invention is based on cell phone platform is elaborated.

1. the sensor of food safety detection information collecting device 2 is selected as follows:

Resistivity sensor, product type: SC21D110, product manufacturer: U.S. AquaSensors.For use continuously the bipolar electrode conductivity sensor designing in the most harsh commercial Application.Titanium electrode (0.1,1.0 and 5.0Cell), provides CPVC (sensor material) for low cost application, measure of system performance (environment: 20 feet of cables, 25 degrees Celsius).Scope: 0.1 Cell:0-18.2M Ω/cm, 1.0 Cell:0-5000 μ S/cm, 5.0 Cell:0-10000 μ S/cm; Angle resolution: 4.5 significant figure; Degree of accuracy: 0.1% (reading); Step response time: 90%30 seconds; Temperature range-5 are to 75 degrees Celsius; 75 degrees Celsius of maximum pressure 150psig@; Peak Flow Rate 10ft/S; Meet the CE standard-required that heavy industry is used.

2) sonac, product type FUS-40E, product brand FUJICERA, construct open, transceiver, rated frequency 40kHz, transmitting-receiving sensitivity-43dB above (30cm), maximum input voltage 100V (Pulse V p.p.), resolution 9mm, 25～70 ℃ of serviceability temperatures.

3) near-infrared optical sensor, the EV76C454 of E2V company, Jade series, cmos imaging sensor.High QE, low light cmos sensor; Resolution is 860*640 pixel, and Pixel Dimensions is 5.8*5.8 micron; Picture size is 1/2.9 inch; Maximum refresh rate is 60@full format, 80@VGA format; Position is dark is 8bit; Dynamic range is 52dB (linearity), 100dB (HDR pattern); Maximum signal to noise ratio is for being greater than 40dB; Supply voltage is 3.3V and 1.8V; When work, job number is 80mW, and when standby, power consumption is 40 μ W; Be encapsulated as μ CLCC48 10 × 10mm.

4) micro-fluorescent optical sensor, brand: essence spectrum come, model: G1UC01C.There is low noise, high dynamic range, high definition, high-quality image.The CCD noise that partly freezes is extremely low, is applicable to the shooting of faint light and fluorescence etc.Be suitable for relatively low to detection speed and sensitivity and signal to noise ratio (S/N ratio) are required to all higher field.Design parameter: Pixel Dimensions 4.6 μ m × 4.6 μ m; Resolution is 1360 × 1024; Scan mode is for lining by line scan; Frame per second is 7.5fps@1360 × 1024; Time shutter 10 μ s-60s; Signal to noise ratio (S/N ratio) 43dB; Shutter is electronic shutter; Spectral response 400nm～1000nm; Data-interface USB2.0,480Mb/s; Power supply DC 5V ± 5%; Electric current ≈ 200mA; White balance is a key white balance; 0 ℃～60 ℃ of working temperatures; Working relative humidity 45～85%.

2. signal conditioning circuit 9 and A/D convertor circuit 10, in order to reduce food safety detection information collecting device, improve this reliability, and the present invention has following scheme:

1) aspect resistivity sensor and sonac, a kind of design based on FPGA data acquisition system (DAS) high-precision signal modulate circuit is proposed, the core of signal conditioning circuit is decay and amplifying circuit, needs to determine decay and the multiple amplifying.The ADC selected range of data acquisition system (DAS) acquisition system is 12 modulus conversion chip AD9238 of-1～+ 1V.The technical indicator of circuit: amplifying circuit frequency is 0～100kHz; Range is ± 1mV～± 20V; Output signal range is ± 1V; Input resistance is 1M Ω, and output resistance is 50 Ω; Zero-error≤10mV; Gain error < 0.1%.Signal condition amplifies and the 4 kinds of multiples of each selection of decaying, and has 14 kinds of program-controlled multiples after combination, range can be divided into 14 grades, and accompanying drawing 4 is circuit structure diagram.

2) aspect near-infrared optical sensor and micro-fluorescent optical sensor, propose a kind of method for designing based on FPGA, by the design of hardware circuit software implementation, on a slice FPGA, complete the functions such as collection, processing and the demonstration of image.Compare with traditional method for designing, not only have that the construction cycle is short, design efficiency is high, the Development characteristics of the FPGA device such as extendability and upgradability is good, flexible design, and owing to adopting hardware circuit to realize, therefore in the speed of view data processing, there is clear superiority.The design proposal native system of system is a Digital Image Processing display circuit based on FPGA, can realize the functions such as the compression of grey scale transformation, rim detection and the image of image.System chart as shown in Figure 5, has comprised the development and Design of following 5 function module circuits in FPGA.

3. the description of mobile phone and cloud computing effect

Mobile phone does not detect and just collects data from sensor simply, but will carry out data processing, packs fuzzy iteration self-organization DAP in mobile phone into, can analyze and provide comparatively perfect testing result at user side, for example, detects.

But the information gathering for imageing sensor, mobile phone is to complete this complex analyses, on the one hand not have computer function powerful for mobile phone, and the sensor that mobile phone connects possibly cannot possess enough precision, and they have been simplified in the time forming coupling system as far as possible after all.Therefore the cloud computing instrument that, application is being risen carries out on-line analysis to the image gathering.

Cloud computing is the better fusion of computer and network, mobile phone is connected to Internet5 by GPRS4, from being remotely logged into the desktop of food safety detection data base computer, in browser, input is by IP address and the port of main control system, and at the username and password of logining porch input computing machine, use and also can register and by mobile phone, this computing machine be operated for the first time, just as before placing oneself in the midst of this computing machine.Because in meat (fish) based food safety detection, the use of image, make people can obtain intuitively the various information in meat (fish) based food, thereby can detect exactly, plane picture, not only directly perceived not, and the observation of different people easily draws different conclusions, 3-D view can provide concrete detection information to people, access by mobile phone to meat (fish) based food safety image resource, show, access, editor and even complex process, and browse two dimensional image by mobile phone, clearly show 3-D view, this class problem realizes by the method for cloud computing.

5. the description of system work process

The remaining agricultural chemicals, the heavy metal that detect vegetables (fruit) surface start the ultrasonic sensor in food safety detection information collecting device; The freshness that detects the meat products such as poultry pig, cattle and sheep fish starts the resistivity sensor in food safety detection information collecting device.By the one-dimension information gathering through signal conditioning circuit 9 with A/D convertor circuit 10 by bluetooth communication (USB interface), be sent in mobile phone, by fuzzyly processing in real time by fuzzy iteration self-organization DAP of installing in mobile phone, can show testing result then and there.

While detecting the food such as bean product, dried food and nuts and grain, start the near-infrared optical sensor in food safety detection information collecting device; While detecting the interior tissue situation of the meat products such as poultry pig, cattle and sheep fish, start the micro-fluorescent optical sensor in food safety detection information collecting device.By the image information gathering through signal conditioning circuit 9 with A/D convertor circuit 10 by bluetooth communication (USB interface), be sent in mobile phone, mobile phone is connected to Internet5 by GPRS4, from being remotely logged into the desktop of food safety detection data base computer, in browser, input is by IP address and the port of main control system, and at the username and password of logining porch input computing machine, use for the first time industry can register and by mobile phone, this computing machine be operated, just as before placing oneself in the midst of this computing machine.Can draw very soon testing result.Database automatically records and sends food safety detection information simultaneously, sets up corresponding detection archives.

Wherein, the image retrieval under fuzzy iteration self-organization data analysis algorithm and cloud computing mode is described below:

1 fuzzy iteration self-organization data analysis algorithm

1.1 fuzzy classification

J.C.Bezdek utilizes the concept of fuzzy set to propose fuzzy classification (also crying soft division) problem, thinks and is classified the sample X in object set X _i, be under the jurisdiction of a certain class with certain degree, that is to say, all samples are all respectively from the different a certain classes that is under the jurisdiction of.Therefore, each class is just thought a fuzzy subset of sample set X, so the corresponding classification matrix of each this class classification results is exactly a fuzzy matrix

R＝(Y _ij) _C×n

In formula: Y _ij∈ [0,1]; ${\mathrm{\&Sigma;}}_{i=1}^c\mathrm{Yij}=1;$ ${\mathrm{\&Sigma;}}_{i=1}^c\mathrm{Yij}>0.$

If M _fefor the set of all fuzzy matrix R that meet above condition,

$M_{\mathrm{fe}}=\{R\&Element;V_{C\&times;n}|{\mathrm{\&gamma;}}_{\mathrm{ij}}\&Element;\left[\mathrm{0,1}\right],\&ForAll;i,j;\underset{i=1}{\overset{c}{\mathrm{\&Sigma;}}}{\mathrm{\&gamma;}}_{\mathrm{ij}}=1,\&ForAll;j;\underset{i=1}{\overset{c}{\mathrm{\&Sigma;}}}{\mathrm{\&gamma;}}_{\mathrm{ij}}>0,\&ForAll;i\}$

Claim M _febecome sample set X to be divided into the fuzzy classification space of C class.

In cluster analysis, if can find out best fuzzy classification matrix R under certain condition according to the characteristic index matrix of n sample, the fuzzy mankind corresponding with R, are exactly the fuzzy classification of sample set X the best under this condition.

1.2 Fuzzy ISODATA Classified Analysing Method

If be classified object set be

X＝{x ₁，x ₂，…，x _n}

Wherein each sample x _iall there are m characteristic index, i.e. x _i=(x _i1, x _i2..., x _im).Characteristic index matrix is

(X _ij) _n×m

(2≤C≤n) establishes C cluster centre vector and is sample set X will to be divided into C class

V＝(v _ij) _C×n

In order to obtain a best fuzzy classification, can be according to following clustering criteria, from fuzzy classification space M _fein a preferred best fuzzy classification.

The clustering criteria of 1.3 fuzzy classifications and clustering criterion

The in the situation that of fuzzy classification, in order to obtain optimum classification, clustering criterion is done to following popularization.Even objective function

Reach minimal value.Wherein V _i, || X _k-V _i|| meaning identical with formula (7), q > 0.In order to change neatly relative subjection degree, the desirable certain value of q (generally getting q=2), value is crossed conference and is caused information distortion.

Clustering criteria is: take out suitable fuzzy classification matrix R and cluster centre vector V, make the represented objective function of formula (8) reach minimal value.Generally speaking, the extreme value of above-mentioned objective function solves quite difficulty, but Bezdek is verified: when q>=1, and X _k≠ V _itime, can carry out interative computation by mode below, and calculating process restrains, Here it is fuzzy ISODATA method.The steps include:

(1) selected C, 2≤C≤n, gets initial fuzzy classification matrix R (0) ∈ M _fe, progressively iteration, I=0,1,2 ...

(2), for R (1), calculate cluster centre vector

$V^{\left(i\right)}={(V_1^{\left(l\right)},V_2^{\left(l\right)},\&CenterDot;\&CenterDot;\&CenterDot;,V_c^{\left(l\right)})}^T$

In formula, $V_i^{\left(l\right)}={\mathrm{\&Sigma;}}_{k=1}^n{\left(r_{\mathrm{ik}}^{\left(l\right)}\right)}^q{X}_k/{\mathrm{\&Sigma;}}_{k=1}^n{\left(r_{\mathrm{ix}}^{\left(l\right)}\right)}^q.$

(3) revise fuzzy classification matrix R ^(l)

Relatively R ⁽¹⁾with R ^(I+1)if, to getting fixed ∈ > 0,

max{[Y _ik(I+1)-Y _ik(I)]}≤∈

R ^(I+1)with V (1) is required, stop iteration, otherwise l=I+1, gets back to step (2) and repeats.

Apply above algorithm and obtain fuzzy classification matrix R ^(l+1)with cluster centre V (l) be with respect to number of categories C, initial fuzzy classification matrix R ⁽⁰⁾, ∈ and parameter q optimum solution.

Because this algorithm requires X _k≠ V _t, and the reason of formula (9) and formula (10) itself, initial fuzzy classification matrix R ⁽⁰⁾choose 2 in 3 conditions except meeting fuzzy classification matrix.

(1)γ _ik∈[0，1]， k；

(2)

outside, also must be to initial fuzzy matrix R ⁽⁰⁾choose, and in addition restriction as follows;

(3) initial matrix R ⁽⁰⁾it can not be a constant matrices that each element all equates;

(4) initial matrix R ⁽⁰⁾it can not be the matrix of an a certain row element equivalence;

(5) initial matrix R ⁽⁰⁾in to only having the class of a sample, before cluster, to remove, put into again after to be clustered.

Meet above 5 initial fuzzy classification matrix R that condition is selected simultaneously ⁽⁰⁾, just not can fuzzy ISODATA cause distortion phenomenon in calculating iterative process, otherwise will make cluster analysis failure.This point, in the time choosing initial fuzzy classification matrix, must cause enough attention.

What class new samples is attributed to is identified by following principle:

Decision principle 1 is established the cluster centre vector of finally trying to achieve

if

$\left|\right|X_k-V_i^{*}\left|\right|=\begin{array}{c}\min \\ i\&le;j\&le;c\end{array}\left(\right||X_k-V_j^{*}|\left|\right)$

By sample X _kbe attributed to i class.

Decision principle 2 is established the fuzzy classification matrix of finally trying to achieve

R ^*＝(Yij) _C×n

at R ^*k row in, if

$r_{\mathrm{ik}}^{*}=\begin{array}{c}\max \\ i<j<c\end{array}\left(r_{\mathrm{jk}}^{*}\right)$

By sample X _kbe attributed to i class.

The check of 1.4 Clustering Effects

Point out above, the fuzzy clustering that application fuzzy ISODATA method obtains is with respect to number of categories C, initial fuzzy classification matrix R ⁽⁰⁾, error ∈ and parameter q optimum solution.If change C, R ⁽⁰⁾, ∈ and q, can obtain many locally optimal solutions.If select the best from these optimum solutions, needing has the index of differentiating fuzzy ISODATA Clustering Effect.Discriminating Clustering Effect can be used following index:

Classification factor

$F_C\left(R\right)=\frac{1}{n}{\mathrm{\&Sigma;}}_{k=1}^n{\mathrm{\&Sigma;}}_{i=1}^c{\mathrm{\&gamma;}}_{\mathrm{<figure-callout\; id="2"\; label="ik"\; filenames="HSA00000801330200011.png"\; state="\{\{state\}\}">ik</figure-callout>}}^2$

As R ∈ M _ctime, F _c(R)=1. therefore, F _c(R) more approach 1; The ambiguity of final classification is less, and Clustering Effect better; Average blur entropy

$H_C\left(R\right)=-\frac{1}{n}{\mathrm{\&Sigma;}}_{k=1}^n{\mathrm{\&Sigma;}}_{i=1}^c{\mathrm{\&gamma;}}_{\mathrm{ik}}\ln {\mathrm{\&gamma;}}_{\mathrm{ik}}$

When average blur entropy more better close to zero.

2. the image retrieval under cloud computing mode

The concept of 2.1 cloud computings

The ununified definition of cloud computing system at present, cloud computing supplier releases relevant cloud computing strategy according to own business event.Hewitt thinks that cloud computing computing system is mainly that information is for good and all stored on the server in cloud, in the time of the information of use, just carries out buffer memory in client.Client can be desktop level, notebook, handheld device etc.The functional perspective that the people such as WangLi-zhe should have from cloud computing system has provided the definition of science cloud computing system, point out to calculate cloud system and not only can provide hardware service Hass as user, software service Sass, data resource service Dass, but also can provide the platform service Pass that can configure to user.Therefore user's hardware configuration, software installation, data access demand of phase computing platform submission oneself as required.In general, cloud computing is exactly on the resource population by calculation task being distributed in to many computing machine compositions, makes various application systems can obtain as required computing power, storage space and various software service.

Image retrieval under 2.2 cloud computing modes

Under cloud computing mode, image resource databases numerous in internet is combined into cloud resource population, forms an information retrieval service system that utilization factor is high, computing velocity is fast.The image retrieval of realizing under dizzy computation schema is mainly set about from three aspects:

(1) for system end, need to set up the mass data storage model based on cloud computing, to a large amount of store tasks and calculation task are distributed under the server or client under system for cloud computing, reach the target that expands resource sharing scope and improve arithmetic speed.

(2) for user, searching system need to provide the client retrieves application program of standard, and user utilizes this program can from cloud resources bank, retrieve quickly and easily required image.If simultaneously user is ready, application program also needs to provide the function that allows user self image resource be uploaded to cloud resources bank, so that other user search use.

(3) under cloud computing mode, need to set up unified search criteria and resource management mechanism, if lack this standard and mechanism, cloud resources bank is difficult to respond the retrieval request of different clients, and the resource of different server or database is because data storage and delivery protocol difference also cannot share.

3. cloud computing mode hypograph searching system structure

By to how realizing image retrieval analysis under cloud computing mode, cloud computing mode hypograph searching system structure can be divided into three levels.

(1) cloud resource layer: this one deck is mainly made up of large scale computer, server and each client machine of being distributed under cloud network, all storage of image information resource, calculating and transmit all and carry out on this layer.

(2) fire wall layer: this layer is safety for guaranteeing image indexing system data message and the confidentiality of user profile, prevents hacker attacks.

(3) client layer: client layer comprises system equipment termination box application program.Terminal device comprises PC, notebook computer, PDA, Digital Television, mobile phone, panel computer etc., and application program is the standard retrieval program that system provides, and is used for submitting to user's Search Requirement.

Claims (9)

1. the open food safety detecting system based on cell phone platform, it is characterized in that: the composition of system comprises food 0 to be detected food safety detection information collecting device 1, Bluetooth communication (or USB interface) 2, mobile phone 3, GPRS communication 4, Internet5, food safety detection database 6 and image cloud computing method 7 etc.

2. according to claim 1, it is characterized in that: described system is open, progressively powerful along with the continuous progress of biometric image sensor technology and cloud computing performance, the parameter of food safety detection is more and more, accuracy is more and more higher, formulate respective standard of the present invention simultaneously, make can recycling rate of waterused the improving of its resource, function be easy to expansion.

3. according to claim 1, it is characterized in that: described food safety detection information collecting device 1 composition comprises: combined sensor 8 (containing resistivity sensor, ultrasonic sensor, near infrared light sensor, micro-fluorescent optical sensor), signal conditioning circuit 9, A/D change-over circuit 10, bluetooth communication (or USB interface) 11 etc.

4. according to claim 1, it is characterized in that: described device for detecting safety of foods completes one-dimension information that food information testing fixture gathers: the information that ultrasonic sensor and conductivity sensor gather, by fuzzyly processing in real time by fuzzy iteration self-organization DAP of installing in mobile phone.

5. according to claim 1, it is characterized in that: the image information that described food security information harvester gathers, be sent to food safety detection central database website by mobile phone, and by mobile phone long-distance operating inspection center data base computer, carry out Analysis of test results.

6. according to claim 3, it is characterized in that: in device for detecting safety of foods, signal conditioning circuit 9 is all application FPGA with A/D change-over circuit 10, realize with Verilog Programming with Pascal Language, and two kinds of signals of a peacekeeping image separately process, but can in a FPGA, complete.

7. according to claim 3, it is characterized in that: in device for detecting safety of foods, settle bluetooth communication (USB interface) and mobile phone to realize short-range radio communication, USB interface is as for subsequent use.

8. according to claim 4, it is characterized in that: apply fuzzyly by the algorithm of fuzzy iteration self-organization data analysis, process the One-dimensional Testing Signal that mobile phone receives, and the quality of food security is passed judgment in real time.

9. according to claim 5, it is characterized in that: the image retrieval by cloud computing for food safety detection, by mobile phone, this computing machine is operated, just as before placing oneself in the midst of this computing machine, and department is to draw very soon testing result.Realize storage, the management of food safety detection magnanimity information simultaneously and calculate.

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