CN107027650A - A kind of boar abnormal state detection method and device based on PSO SVM - Google Patents
A kind of boar abnormal state detection method and device based on PSO SVM Download PDFInfo
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Abstract
The present invention discloses a kind of boar abnormal state detection method and device based on PSO SVM, can effectively detect doubtful sick pig, and accurately and timely recognizes boar estrus behavior, reduces manual observation cost, improves the benefit of raiser.This method includes：S1, collection boar individual information, wherein, the individual information includes 3-axis acceleration, body temperature and feeding information；S2, by the individual information carry out feature extraction, construction feature matrix；S3, based on the boar knowledge base pre-established, using the eigenmatrix, judge whether boar sick using rule-based method；S4, by the way that the eigenmatrix is inputted into the boar estrus behavior identification model based on SVMs that is pre-created classification is identified to boar estrus behavior, obtain whether heat and the output of heat probability two, wherein, SVMs parameter is optimized by particle cluster algorithm when the boar estrus behavior identification model is created.
Description
Technical field
The present invention relates to the processing of livestock and poultry cultivation Internet of Things data and analysis field, and in particular to a kind of based on PSO-SVM's
Boar abnormal state detection method and device.
Background technology
In recent years, as China is widely popularized to boar breeding technology, the large-scale degree for raising boar is constantly being carried
It is high.Boar health status is the guarantee of Swine Production, and the country still monitors livestock by the way of manual observation mostly at present
Behavior, so does and not only takes a substantial amount of time and energy, and the data subjectivity that manual observation is arrived is strong, is unfavorable for essence
Really, stably, continuously record；Timely and accurately identification the normal heat of sow be obtain sow optimum period breeding time and improve its by
The key problem in technology of tire rate, but in actual production, it is most of at present to plant the means identification oestrus of sow only manually observed on pig farm
Behavior, many raisers are due to the careless heat performance for having neglected sow and the in good time science breeding opportunity for being difficult to grasp sow etc.
Factor, causes breeding time not good or conception rate reduction, greatly affected the normal breeding potential of sow, exist workload it is big,
Cost of labor is high and the problem of inefficiency, the economic benefit to raiser produces material impact.Therefore, detection boar is supported in real time
During growing the health status of individual pig and timely and accurately the behavior of identification oestrus of sow grind as livestock and poultry cultivation field is important
Study carefully content.
Some below usually is showed during oestrus of sow：1. body temperature has relatively risen usually；2. activity showed increased；③
Walk about more frequently, mounting behavior；4. appetite slightly subtracts, type of mostly chewing carefully and swallow slowly, and Feeding time has relatively extended usually.Wherein live
Momentum and Temperature changing are the most obvious, also many researchs that oestrus of sow detection is carried out according to the two features in current research.
Performance is usually when boar is sick：1. body temperature is raised；2. lassitude, sleepingly drowsiness；3. anorexia or stopping；
4. suffer from diarrhoea.It is for the physiological performances such as breathing problem, diarrhoea progress disease detection to study at present more.
Breed sick less with heat abnormality progress detection simultaneously research in production currently for boar, and pin
A certain physiological characteristic when the research with oestrous detection sick to boar is sick foundation boar or heat mostly is identified, than
Change such as independent foundation activity or body temperature is identified and ignores other correlation factors, greatly influences abnormality inspection
The accuracy rate of survey.
The content of the invention
In view of this, the problem to be solved in the present invention：A kind of boar abnormal state detection method and dress based on PSO-SVM
Put.
On the one hand, the embodiment of the present invention proposes a kind of boar abnormal state detection method based on PSO-SVM, including：
S1, collection boar individual information, wherein, the individual information includes 3-axis acceleration, body temperature and feeding information；
S2, by the individual information carry out feature extraction, construction feature matrix；
S3, based on the boar knowledge base pre-established, using the eigenmatrix, judged kind using rule-based method
Whether pig is sick；
S4, the identification mould of the boar estrus behavior based on SVMs by the way that eigenmatrix input is pre-created
Classification is identified to boar estrus behavior in type, obtain whether heat and the output of heat probability two, wherein, in the boar
Estrus behavior identification model is optimized when creating by particle cluster algorithm to SVMs parameter.
On the other hand, the embodiment of the present invention proposes a kind of boar abnormal state detecting apparatus based on PSO-SVM, including：
Collecting unit, for gathering boar individual information, wherein, the individual information include 3-axis acceleration, body temperature and
Feeding information；
Construction unit, for by carrying out feature extraction, construction feature matrix to the individual information；
First recognition unit, based on the boar knowledge base pre-established, using the eigenmatrix, using rule-based
Method judges whether boar is sick；
Second recognition unit, for by the way that the eigenmatrix is inputted into the boar based on SVMs being pre-created
Classification is identified to boar estrus behavior in estrus behavior identification model, obtain whether heat and the output of heat probability two,
Wherein, SVMs parameter is optimized by particle cluster algorithm when the boar estrus behavior identification model is created.
Boar abnormal state detection method and device provided in an embodiment of the present invention based on PSO-SVM, considers kind
The individual correlation factor such as activity, body temperature, changes in diet of pig, builds boar knowledge base, is judged by rule-based method
It is whether sick；Method based on SVMs carries out heat identification, and by particle cluster algorithm to support vector cassification
Device parameter is optimized, and the accuracy rate of boar estrus behavior identification is improved, so as to improve the conception rate of boar.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of boar abnormal state detection method one embodiment of the invention based on PSO-SVM；
Fig. 2 is the structural representation of boar abnormal state detecting apparatus one embodiment of the invention based on PSO-SVM.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is explicitly described, it is clear that described embodiment be the present invention
A part of embodiment, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art are not having
The every other embodiment obtained under the premise of creative work is made, the scope of protection of the invention is belonged to.
Referring to Fig. 1, the present embodiment discloses a kind of boar abnormal state detection method based on PSO-SVM, including：
Individual information when S1, collection boar are movable, wherein, the individual information includes 3-axis acceleration, body temperature and adopted
Eat information；
S2, by the individual information carry out feature extraction, construction feature matrix；
S3, based on the boar knowledge base pre-established, using the eigenmatrix, judged kind using rule-based method
Whether pig is sick；
S4, the identification mould of the boar estrus behavior based on SVMs by the way that eigenmatrix input is pre-created
Classification is identified to boar estrus behavior in type, obtain whether heat and the output of heat probability two, wherein, in the boar
Estrus behavior identification model is optimized when creating by particle cluster algorithm to SVMs parameter.
Boar abnormal state detection method provided in an embodiment of the present invention based on PSO-SVM, considers the work of boar
The individual correlation factor such as momentum, body temperature, changes in diet, builds boar knowledge base, life is judged whether by rule-based method
Disease；Method based on SVMs carries out heat identification, and by particle cluster algorithm to support vector machine classifier parameter
Optimize, the accuracy rate of boar estrus behavior identification is improved, so as to improve the conception rate of boar.
In order to achieve the above object, technical scheme proposes a kind of boar abnormality inspection based on PSO-SVM
Survey method, this method gathers the individual informations such as 3-axis acceleration, body temperature, the dietary amount of boar by awareness apparatus first, and
Carry out the operation such as data consistency detection；Secondly the pretreatment operations such as feature extraction, normalization are carried out, 6 dimensional feature matrixes are built；
Then boar knowledge base is set up, judges whether boar is sick using rule-based method；Finally create and be based on SVMs
Boar estrus behavior identification model, wherein being optimized by particle cluster algorithm to SVMs parameter；To boar heat
Classification is identified in behavior, includes whether two outputs of heat and heat probability.
This method includes four steps：
Individual information is gathered.Gathered and planted by ADXL345 3-axis acceleration sensors, infrared radiation thermometer and self-feeder
The individual informations such as 3-axis acceleration, body temperature, the feeding of pig.Information gathering process is related to acquisition node, routing node, centromere
Point, RS485 and RS232 switching nodes and PC terminals (information centre).Specifically gatherer process is：
1) ADXL345 3-axis acceleration sensor acquisition nodes are worn on boar neck, gather certain moment boar X, Y, Z
3-axis acceleration data；
2) infrared radiation thermometer is combined into acquisition volume with self-feeder gently to search for food information, when boar searches for food automatically into
In feeding station, door of standing is closed, it is ensured that an only boar is searched for food every time, and ear tag card reader passes through electron ear tage identification record
The time in station is entered and left to boar, blanking device is able to record that the feed intake of the boar, and infrared radiation thermometer collects kind
Body temperature when pig searches for food.Wherein the acquisition node of 3-axis acceleration sensor and routing node and Centroid formation ZigBee without
Gauze network, Centroid is transferred data to through routing node, and infrared radiation thermometer, ear tag card reader and self-feeder are adopted
Collection node transfers data to Centroid by netting twine through routing node；
3) Centroid is protected by data such as acceleration, body temperature, feedings by RS485 and RS232 wire transmission to PC terminals
Deposit to server.
Eigenmatrix is built.Activity, body temperature, changes in diet are more apparent during boar heat, are built according to this three parameters
Boar estrus behavior eigenmatrix.According to static, mounting, walk each accounting example, unit interval mean body temperature, list in training sample
Position time intake time, 6 input vectors of unit feed intake levy the eigenmatrix for building M*6 dimensions, and M gathers for boar individual information
Number of days.Specially：1) can be static, mounting, three classes of walking by the crawler behavior rough segmentation of boar according to different standard and purpose.
ADXL345 3-axis acceleration sensors gather X, Y, Z 3-axis acceleration data, set training set number of samples as N, correspondence number of days
For M, the 3-axis acceleration value of t collection is respectivelyTag along sort isThen one can be constituted by 3 axle accelerations
Acceleration signature vector (input vector) X of individual N*3 dimensionsv, tag along sort is (static：- 1, mounting：0, walking：1) mark is tieed up for N*1
Sign vector (output vector) Yv。
Characteristic vector to training set carries out data normalization processing, after being optimized by PSO-SVM Algorithm for Training
Boar crawler behavior disaggregated model, utilizes 3-axis acceleration characteristic vector (normalizing of the boar crawler behavior disaggregated model to boar
After change) handled, so as to obtain boar crawler behavior classification results, i.e., in the unit interval, boar is static, mounting, time of walking
Number.
Wherein SVMs (Support Vector Machine, SVM) is that one kind realizes that structural risk minimization is thought
The method thought, SVM structures exactly like three layer perceptron.Wherein input layer is, in order to store input data, not make any processing fortune
Calculate；Intermediate layer is by the study to sample set, selection kernel function K (x, xi), i=1,2,3 ..., l；Last layer is exactly structure
Make classification function.Whole process be equivalent in feature space construct an optimal hyperlane so that between two class samples away from
From maximization.
Given training sample set
T={ (x1,y1),(x2,y2),...(xl,yl)} (1)
In formula, xi--- n-dimensional vector, xi∈Rn, yi--- corresponding output variable, yi∈ R,
The largest interval model classified using approximate non-linear, is introduced kernel function and the data of luv space is mapped to higher-dimension
In space, its optimization problem is expressed as：
C in formula --- penalty factor, αi--- Lagrange multiplier of introducing, l --- sample set number, K (xi,
xj) --- kernel function, SVM kernel function is used as using Radial basis kernel function herein.
Particle cluster algorithm (Particle Swarm Optimization, PSO) is by the cooperation between individual in population
A kind of Swarm Intelligent Algorithm of optimal solution is found with information sharing.
The point that the potential solution of 1 each optimization problem can be imagined as on D dimensions search space is defined, we term it
" particle " (Particle), all particles have an adaptive value determined by object function (Fitness Value), each
Particle also has a speed to determine the direction and distance that they circle in the air, position, speed and the index expression of fitness value three grain
Subcharacter；
Define 2 individuals and undergo and obtained fitness value optimal location is calculated in position be referred to as individual extreme value；Refer to population
In all particle search to fitness optimal location be referred to as colony's extreme value.
PSO algorithms initialize a group particle first in solution space, and each particle represents extremal optimization problem
One potential optimal solution；Then particle is moved in solution space, by tracking individual extreme value and colony's extreme value more new individual position,
Particle often updates a position, just calculates a fitness value, and by compare new particle fitness value and individual extreme value,
The fitness value of colony's extreme value more new individual extreme value and colony's extreme value place；It is empty in solution that particles just follow current optimal particle
Between in search for, by successive ignition, final evolve obtains optimum individual extreme value and colony's extreme value place and fitness value.
In iterative process each time, particle updates speed and the position of itself by individual extreme value and colony's extreme value, more
New formula is as follows：
In formula,--- speed of i-th of particle in kth time iteration；
--- position of i-th of particle in kth time iteration；
c1, c2--- coefficient correlation variable, value is respectively 1.5,1.7；
r1, r2--- random number；
W --- the inertia weight factor, represents the ability of search speed before particle is inherited；
--- position when fitness is optimal in all paths of i-th of particle, i.e., individual extreme value；
--- position when fitness is optimal in the population in kth time iteration, i.e. colony's extreme value.
When creating support vector cassification model, the selection for having two parameters plays vital work to classifying quality
With：One is penalty factor, i.e. the tolerance to error, and C is higher, illustrates more to can't stand and error occurs, and easy over-fitting, C is got over
Small, easy poor fitting, C is excessive or too small, and generalization ability is deteriorated；Two be the parameter γ in kernel function, impliedly determines data
The distribution after new feature space is mapped to, γ is bigger, and supporting vector is fewer, γ values are smaller, and supporting vector is more, population is calculated
Method is updated according to current group optimal solution search all the time by creating random population, and continuous iteration, until meeting condition, is found
Optimal parameter C and γ.
According to above-mentioned boar crawler behavior classification results, in units of day, according to boar within one day is static, mounting,
The number of times of walking, calculates static, mounting, each accounting of walking example respectively, set that certain day static, mounting, walking proportion difference
ForThe body temperature detected according to infrared radiation thermometer, which can be calculated, obtains the daily mean body temperature of boarAccording to certainly
The boar feeding information of dynamic feeder record can obtain intake timeFeed intakeComprehensive boar crawler behavior, body temperature,
Parameter of searching for food builds the eigenmatrix of M*6 dimensional features.
According to each day is static, mounting, walk each accounting example, unit interval mean body temperature, unit interval intake time, unit
6 input vectors of feed intake levy the eigenmatrix X for building M*6 dimensionso, tag along sort (whether heat, be：0, it is no：1) tieed up for M*1
Label vector Yo。
Disease detection.Judge whether boar is sick using rule-based method, boar knowledge base is created first；Then pin
The boar individual information gathered in real time is compared with rule base, judged whether sick.Specially：
1) normal condition in boar knowledge base is obtained by the means such as books and electronic material, survey, expert interviewing
Under individual physiological characteristic information, including the information such as body temperature, activity, diet under different cultivars under boar normal condition.
2) initial data got chosen, the processing such as classification associated；Boar knowledge data base is designed, each product are arranged
The information of the boar planted in normal state, including body temperature, body weight, intake time, feed intake, activity etc..
3) a disease detection rule base is generated, disease detection of the invention focuses on " differentiation of disease ", that is, judges certain
Whether boar is sick, therefore main based on threshold decision in rule base；If boar under normal circumstances (in health status and
Estrus), NpsRepresent daily quiescent time accounting, NThRepresent shell temperature peak, NTlShell temperature minimum is represented,
NfnRepresent average daily feed intake；There is three below condition：
If meeting any of the above condition, judge the boar for doubtful sick pig；Otherwise, it is determined that being healthy boar.
Heat is recognized.Create and training boar heat identification model, heat identification is carried out to boar.Specially：1) to M*6
The eigenmatrix of dimension is normalized, by particle cluster algorithm optimum choice SVMs parameter, creates and is based on PSO-
SVM boar heat identification model, wherein PSO-SVM algorithms have a detailed description in eigenmatrix structure.
2) estrus behavior classification is carried out to test set according to estrus behavior identification model, setting output model is bivector
Y{Yifo, Yopro, wherein YifoIndicating whether heat (is：0, it is no：1),Represent heat probability.
Referring to Fig. 2, the present embodiment discloses a kind of boar abnormal state detecting apparatus based on PSO-SVM, including：
Collecting unit 1, the individual information for gathering boar, wherein, the individual information includes 3-axis acceleration, body temperature
With feeding information；
The collecting unit, specifically for passing through ADXL345 3-axis acceleration sensors, infrared radiation thermometer and automatic feeding
Device gathers corresponding boar individual information.
Construction unit 2, for by carrying out feature extraction, construction feature matrix to the individual information；
The construction unit, specifically for：
The 3-axis acceleration is divided into by three classes of static, mounting and walking by particle cluster algorithm and SVMs, with
It is unit, according to boar within one day is static, mounting, the number of times of walking, static, mounting is calculated respectively, each accounting example of walking,
And calculate the daily mean body temperature of boar, average Feeding time and average feed intake；
Integrate static boar, mounting, each accounting example of walking, daily mean body temperature, average Feeding time and feed intake
Construction feature matrix.
First recognition unit 3, for based on the boar knowledge base pre-established, using the eigenmatrix, using based on
The method of rule judges whether boar is sick；
First recognition unit, specifically for：
Judge whether the individual information of boar meets one in first condition, second condition, third condition and fourth condition
Individual condition, if the individual information of boar meets a bar in the first condition, second condition, third condition and fourth condition
Part, it is determined that boar is sick, otherwise, it is determined that boar is not sick, wherein, the first condition is that the static proportion of boar is big
In boar daily quiescent time accounting under normal circumstances, the second condition is less than boar normal condition for the mean body temperature of boar
Lower shell temperature minimum, the third condition is more than boar shell temperature highest under normal circumstances for the mean body temperature of boar
Value, the fourth condition is that average feed intake is less than boar average daily feed intake under normal circumstances.
Second recognition unit 4, for by the way that the eigenmatrix is inputted into the kind based on SVMs being pre-created
Classification is identified to boar estrus behavior in pig estrus behavior identification model, obtain whether heat and heat probability two it is defeated
Go out, wherein, it is excellent to the progress of SVMs parameter by particle cluster algorithm when the boar estrus behavior identification model is created
Change.
Boar abnormal state detecting apparatus provided in an embodiment of the present invention based on PSO-SVM, considers the work of boar
The individual correlation factor such as momentum, body temperature, changes in diet, builds boar knowledge base, life is judged whether by rule-based method
Disease；Method based on SVMs carries out heat identification, and by particle cluster algorithm to support vector machine classifier parameter
Optimize, the accuracy rate of boar estrus behavior identification is improved, so as to improve the conception rate of boar.
The invention has the advantages that：
1st, while two kinds of abnormalities of sick and heat to boar are detected, activity, the body of boar are considered
Temperature, the change of three aspects of diet are turned to feature extraction index, improve abnormal state detection accuracy rate；The data of every group of measurement are same
Shi Jinhang disease detections and heat identification so that data improve raiser's productivity effect using maximizing in boar breeding process.
2nd, according to consultant expert, check that the means such as books electronic bits of data obtain the information such as boar growth standard, and create
Boar knowledge base, when carrying out disease detection, is carried out according to differentiation of disease rule in boar knowledge base and the boar individual data items of measurement
Compare, so as to judge whether sick, algorithm comparison is easily realized, and preferable for detecting the whether sick effect of boar.
3rd, boar estrus behavior identification is carried out by double classification algorithm.In characteristic extraction procedure, using supporting vector
Machine grader is classified (including static, mounting, three classes of walking) to the 3-axis acceleration data of boar；Creating heat identification
During model, in units of day, daily static, mounting, walking proportion, mean body temperature, intake time, feed intake are regard as 6
Dimensional feature matrix, reuses support vector machine classifier and boar heat state is classified, wherein passing through particle cluster algorithm
Support Vector Machines Optimized, determines SVMs parameter.By the sorting algorithm based on population and SVMs twice, carry
High boar abnormal state detection accuracy.
It should be understood by those skilled in the art that, embodiments herein can be provided as method, system or computer program
Product.Therefore, the application can be using the reality in terms of complete hardware embodiment, complete software embodiment or combination software and hardware
Apply the form of example.Moreover, the application can be used in one or more computers for wherein including computer usable program code
The computer program production that usable storage medium is implemented on (including but is not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The application is the flow with reference to method, equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram are described.It should be understood that can be by every first-class in computer program instructions implementation process figure and/or block diagram
Journey and/or the flow in square frame and flow chart and/or block diagram and/or the combination of square frame.These computer programs can be provided
The processor of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce
A raw machine so that produced by the instruction of computer or the computing device of other programmable data processing devices for real
The device for the function of being specified in present one flow of flow chart or one square frame of multiple flows and/or block diagram or multiple square frames.
These computer program instructions, which may be alternatively stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that the instruction being stored in the computer-readable memory, which is produced, to be included referring to
Make the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one square frame of block diagram or
The function of being specified in multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that in meter
Series of operation steps is performed on calculation machine or other programmable devices to produce computer implemented processing, thus in computer or
The instruction performed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in individual square frame or multiple square frames.
It should be noted that herein, such as first and second or the like relational terms are used merely to a reality
Body or operation make a distinction with another entity or operation, and not necessarily require or imply these entities or deposited between operating
In any this actual relation or order.Moreover, term " comprising ", "comprising" or its any other variant are intended to
Nonexcludability is included, so that process, method, article or equipment including a series of key elements not only will including those
Element, but also other key elements including being not expressly set out, or also include being this process, method, article or equipment
Intrinsic key element.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that
Also there is other identical element in process, method, article or equipment including the key element.Term " on ", " under " etc. refers to
The orientation or position relationship shown is, based on orientation shown in the drawings or position relationship, to be for only for ease of the description present invention and simplify
Description, rather than indicate or imply that the device or element of meaning must have specific orientation, with specific azimuth configuration and behaviour
Make, therefore be not considered as limiting the invention.Unless otherwise clearly defined and limited, term " installation ", " connected ",
" connection " should be interpreted broadly, for example, it may be being fixedly connected or being detachably connected, or be integrally connected；Can be
Mechanically connect or electrically connect；Can be joined directly together, can also be indirectly connected to by intermediary, can be two
The connection of element internal.For the ordinary skill in the art, above-mentioned term can be understood at this as the case may be
Concrete meaning in invention.
In the specification of the present invention, numerous specific details are set forth.Although it is understood that, embodiments of the invention can
To be put into practice in the case of these no details.In some instances, known method, structure and skill is not been shown in detail
Art, so as not to obscure the understanding of this description.Similarly, it will be appreciated that disclose in order to simplify the present invention and helps to understand respectively
One or more of individual inventive aspect, above in the description of the exemplary embodiment of the present invention, each of the invention is special
Levy and be grouped together into sometimes in single embodiment, figure or descriptions thereof.However, should not be by the method solution of the disclosure
Release and be intended in reflection is following：I.e. the present invention for required protection requirement is than the feature that is expressly recited in each claim more
Many features.More precisely, as the following claims reflect, inventive aspect is to be less than single reality disclosed above
Apply all features of example.Therefore, it then follows thus claims of embodiment are expressly incorporated in the embodiment,
Wherein each claim is in itself as the separate embodiments of the present invention.It should be noted that in the case where not conflicting, this
The feature in embodiment and embodiment in application can be mutually combined.The invention is not limited in any single aspect,
Any single embodiment is not limited to, any combination and/or the displacement of these aspects and/or embodiment is also not limited to.And
And, can be used alone the present invention each aspect and/or embodiment or with other one or more aspects and/or its implementation
Example is used in combination.
Finally it should be noted that：Various embodiments above is merely illustrative of the technical solution of the present invention, rather than its limitations；To the greatest extent
The present invention is described in detail with reference to foregoing embodiments for pipe, it will be understood by those within the art that：Its according to
The technical scheme described in foregoing embodiments can so be modified, or which part or all technical characteristic are entered
Row equivalent substitution；And these modifications or replacement, the essence of appropriate technical solution is departed from various embodiments of the present invention technology
The scope of scheme, it all should cover among the claim of the present invention and the scope of specification.
Claims (8)
1. a kind of boar abnormal state detection method based on PSO-SVM, it is characterised in that including：
S1, collection boar individual information, wherein, the individual information includes 3-axis acceleration, body temperature and feeding information；
S2, by the individual information carry out feature extraction, construction feature matrix；
S3, based on the boar knowledge base pre-established, using the eigenmatrix, judge that boar is using rule-based method
It is no sick；
S4, the boar estrus behavior identification model pair based on SVMs by the way that eigenmatrix input is pre-created
Classification is identified in boar estrus behavior, obtain whether heat and the output of heat probability two, wherein, in the boar heat
SVMs parameter is optimized by particle cluster algorithm during Activity recognition model creation.
2. according to the method described in claim 1, it is characterised in that the individual information collecting work in the S1 is by information gathering
System complete, described information acquisition system by acquisition node, routing node, Centroid, RS485 and RS232 switching nodes and
PC terminals are constituted, and corresponding boar is gathered by ADXL345 3-axis acceleration sensors, infrared radiation thermometer and self-feeder
Body information, acquisition node and routing node and Centroid formation ZigBee wireless networks, through routing node by individual information number
According to Centroid is sent to, Centroid is whole to PC by the wire transmission of RS485 and RS232 switching nodes by individual information data
End, is preserved to server.
3. method according to claim 2, it is characterised in that the S2, including：
The 3-axis acceleration is divided into by three classes of static, mounting and walking by particle cluster algorithm and SVMs, using day as
Unit, according to boar within one day is static, mounting, the number of times of walking, static, mounting is calculated respectively, each accounting example of walking, and count
Calculate the daily mean body temperature of boar, average Feeding time and average feed intake；
Static boar, mounting, each accounting example of walking are integrated, daily mean body temperature, average Feeding time and feed intake is built
Eigenmatrix.
4. method according to claim 3, it is characterised in that the S3, including：
Judge whether the individual information of boar meets a bar in first condition, second condition, third condition and fourth condition
Part, if the individual information of boar meets a condition in the first condition, second condition, third condition and fourth condition,
Then determine that boar is sick, otherwise, it is determined that boar is not sick, wherein, the first condition is that the static proportion of boar is more than
Boar daily quiescent time accounting under normal circumstances, the second condition is less than boar under normal circumstances for the mean body temperature of boar
Shell temperature minimum, the third condition is more than boar shell temperature peak under normal circumstances for the mean body temperature of boar,
The fourth condition is that average feed intake is less than boar average daily feed intake under normal circumstances.
5. a kind of boar abnormal state detecting apparatus based on PSO-SVM, it is characterised in that including：
Collecting unit, for gathering boar individual information, wherein, the individual information includes 3-axis acceleration, body temperature and feeding
Information；
Construction unit, for by carrying out feature extraction, construction feature matrix to the individual information；
First recognition unit, for based on the boar knowledge base pre-established, using the eigenmatrix, using rule-based
Method judges whether boar is sick；
Second recognition unit, for by the way that the eigenmatrix is inputted into the boar heat based on SVMs being pre-created
Classification is identified to boar estrus behavior in Activity recognition model, obtain whether heat and the output of heat probability two, wherein,
SVMs parameter is optimized by particle cluster algorithm when the boar estrus behavior identification model is created.
6. device according to claim 5, it is characterised in that the collecting unit, specifically for passing through the axles of ADXL345 tri-
Acceleration transducer, infrared radiation thermometer and self-feeder gather corresponding boar individual information.
7. device according to claim 6, it is characterised in that the construction unit, specifically for：
The 3-axis acceleration is divided into by three classes of static, mounting and walking by particle cluster algorithm and SVMs, using day as
Unit, according to boar within one day is static, mounting, the number of times of walking, static, mounting is calculated respectively, each accounting example of walking, and count
Calculate the daily mean body temperature of boar, average Feeding time and average feed intake；
Static boar, mounting, each accounting example of walking are integrated, daily mean body temperature, average Feeding time and feed intake is built
Eigenmatrix.
8. device according to claim 7, it is characterised in that first recognition unit, specifically for：
Judge whether the individual information of boar meets a bar in first condition, second condition, third condition and fourth condition
Part, if the individual information of boar meets a condition in the first condition, second condition, third condition and fourth condition,
Then determine that boar is sick, otherwise, it is determined that boar is not sick, wherein, the first condition is that the static proportion of boar is more than
Boar daily quiescent time accounting under normal circumstances, the second condition is less than boar under normal circumstances for the mean body temperature of boar
Shell temperature minimum, the third condition is more than boar shell temperature peak under normal circumstances for the mean body temperature of boar,
The fourth condition is that average feed intake is less than boar average daily feed intake under normal circumstances.
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