CN104143137A - Storage method for samples in medical refrigerator system - Google Patents

Abstract

The embodiment of the invention provides a storage method for samples in a medical refrigerator system. The problem that the rationalization degree of positions of the samples in the intelligent medical refrigerator system is not sufficient, so that storage efficiency is affected is at least solved. The storage method includes the steps that identifications and attributes of the samples to be stored are obtained; according to the attributes of the samples to be stored, the attribute association degree between the samples to be stored and each sample mi in n samples stored in the medical refrigeration system is determined; according to the identifications of the samples to be stored, the user behavior association degree between the samples to be stored and each sample mi in the stored n samples; according to the attribute association degrees and the user behavior association degrees, in combination with association degree self-adaptation factors, the sample association degree between the samples to be stored and each sample mi in the stored n samples is determined; the sample association degrees are sequenced, and a sample physical position storage table is called to determine the to-be-stored positions of the samples to be stored; the samples to be stored are stored at the to-be-stored positions. The storage method is applied to the field of medical sample data management.

Description

The storage means of sample in medical treatment refrigerator system

Technical field

The present invention relates to medical sample data management domain, relate in particular to the storage means of sample in medical refrigerator system.

Background technology

Development along with medical industry, intelligent medical refrigerator is widely used in numerous industries and the fields such as research institutions, health care, military aviation, bio-pharmaceuticals, pharmacy, pharmaceutical factory, blood station as a kind of vital classification in refrigerator, becomes one of requisite critical medical devices.

The product category of intelligent medical refrigerator is various, as blood refrigerating case, medicine refrigerator, vaccine storage box, household refrigerator-freezer, low temperature storage box, profound hypothermia storage box, Medical heat-preserving box etc.These intelligent medical refrigerators and common medical refrigerator have bigger difference in performance, not only when for sample or medicine storage, need to meet the environmental requirements such as harsh temperature, humidity, when storage or extraction, also to reduce as much as possible artificial or the impact of external environment condition on environment in cabinet, preferably meet the requirement of automated storing or extraction.Yet, in the automated storing process of intelligent medical refrigerator, need to consider a new problem, because sample size that stored in cabinet or to be stored is more, if the rationalization degree of the memory location of sample is not enough, such as when once storing a plurality of sample, between the position that each sample is deposited, there is no rule, not too reasonable, automation equipment just need to be longer running time, the running time that when this extracts after also having caused simultaneously, automation equipment equally need to be longer, thus period of reservation of number is lengthened, the efficiency that has a strong impact on storage or extract.

Therefore, seek that a kind of rationalization degree that solves sample position in intelligent medical refrigerator system is not enough and the problem of impact storage or extraction efficiency is the technical matters of needing at present solution badly.

Summary of the invention

Embodiments of the invention provide the storage means of sample in medical refrigerator system, at least to solve the rationalization degree deficiency of sample position in intelligent medical refrigerator system, impact is stored or the problem of extraction efficiency, has promoted storage or the extraction efficiency of sample in intelligent medical refrigerator system.

For achieving the above object, embodiments of the invention adopt following technical scheme:

First aspect, provides the storage means of sample in a kind of medical refrigerator system, and described method comprises:

Obtain sample sign and the sample attribute of sample to be stored;

According to the sample attribute of described sample to be stored, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree; And, according to the sample sign of described sample to be stored, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe user behavior degree of association, wherein, m _ifor the sample sign of a sample in n the sample of having stored in described medical refrigerator system, 1≤i≤n;

According to each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree and the user behavior degree of association, in conjunction with default degree of association adaptive factor, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe sample degree of association;

By each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _isample degree of association sequence, obtain ranking results;

Call sample physical location storage list, and according to described ranking results and described sample physical location storage list, determine the position to be stored of described sample to be stored;

By described sample storage to be stored to described position to be stored.

The storage means of sample in the medical refrigerator system providing based on the embodiment of the present invention, after obtaining the sample sign and sample attribute of sample to be stored, attribute is determined each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system per sample _iattribute Association degree; obtain treating stored sample and store the Attribute Association degree magnitude relationship of sample; such as conventionally paying the utmost attention to the close position that sample to be stored is placed on to the sample larger with its Attribute Association degree, sign is determined each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system per sample simultaneously _ithe user behavior degree of association, obtain user's extraction affairs behavior or the influence degree data that operating habit is put sample position to be stored, if such as user often extracts the sample A having stored in sample to be stored and described medical refrigerator system jointly, pay the utmost attention to the close position that sample to be stored is placed on to sample A.

According to Attribute Association degree and the user behavior degree of association, in conjunction with default degree of association adaptive factor, determine each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe sample degree of association.Wherein, each sample m in n the sample that Attribute Association degrees of data and user behavior degree of association data have been stored in determining sample to be stored and described medical refrigerator system _ithe sample degree of association time produce and adaptive factor is corresponding separately influence degree.Utilize this sample calculation of relationship degree method, when storage sample to be stored, definite while of the position to be stored of sample to be stored is determined by sample to be stored and Attribute Association degree and the user behavior degree of association of having stored between sample, in prior art, put at random or effect per sample solely, the storage condition of sample or user's extraction frequencies etc. are because usually determining the technical scheme of sample storage position, the sample calculation of relationship degree method that example technique scheme of the present invention provides, can objectively reflect sample to be stored comparatively comprehensively and store correlation degree or the correlativity size between sample, thereby for calculative determination position to be stored provides foundation more accurately, make sample memory location determine more reasonable, regular enhancing.

By by each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _isample degree of association sequence, obtain ranking results, and call sample physical location storage list, according to ranking results and described sample physical location storage list, find out and the maximally related close position of the sample position of having stored, and then this close position can be defined as to the position to be stored of described sample to be stored, such as, select the two or more samples larger with the sample degree of association data of sample to be stored, the close position of these two or more samples is defined as to the position of sample to be stored.

Finally by described sample storage to be stored to described position to be stored.

To sum up, pass through technique scheme, on the one hand, utilize the calculating of the sample degree of association to determine memory location, reduced randomness when sample is deposited, strengthened the regularity between the position that sample deposits, the rationalization degree of the memory location of medical treatment refrigerator inner sample improves, can be when a plurality of sample of the disposable storage of user, reduce the storage operation time of automation equipment, save the stand-by period of storing process, this be because, the sample that the degree of association is larger has been dispensed on contiguous position, medical treatment refrigerator memory storage can be disposable by a plurality of sample storage in contiguous position, even if or store one at every turn, its operation route is also relatively simple, thereby the running time reduces, storage efficiency improves, on the other hand, user is when a plurality of sample of disposable extraction, the regularity of the position of depositing due to each sample strengthens, conventionally be placed on contiguous position, reduce too the extraction running time of automation equipment, save the stand-by period of leaching process, no matter efficiency is all improved when making user in storage or extracting, this has also improved the competitive power of the medical Freezer Products of applying this storage means simultaneously.

Accompanying drawing explanation

The schematic flow sheet one of the storage means of sample in a kind of medical refrigerator system that Fig. 1 provides for the embodiment of the present invention;

User behavior degree of association matrix schematic diagram between a kind of sample that Fig. 2 provides for the embodiment of the present invention;

The schematic flow sheet two of the storage means of sample in a kind of medical refrigerator system that Fig. 3 provides for the embodiment of the present invention.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.

For the ease of the clear technical scheme of describing the embodiment of the present invention, in an embodiment of the present invention, adopted the printed words such as " first ", " second " to distinguish the essentially identical identical entry of function and efficacy or similar item, it will be appreciated by those skilled in the art that the printed words such as " first ", " second " right quantity and execution order limit.

Embodiment mono-,

The embodiment of the present invention provides the storage means of sample in a kind of medical refrigerator system, specifically as shown in Figure 1, comprising:

S101, the sample sign of obtaining sample to be stored and sample attribute.

Concrete, sample sign can identify a sample, is generally the title of sample, and sample attribute is for characterizing the information of sample characteristics of for example.

Exemplary, sample attribute can comprise: the temperature of structure type (such as belonging to the information of the chemical constitution aspects such as benzene aroma type), source-information/producer information, biologically active, POP information, storage, humidity environment parameter etc.

Certainly, above-mentioned is only the exemplary kind of enumerating some sample attributes, and sample attribute also may comprise other kind, and the embodiment of the present invention is not done concrete restriction to this.

S102, according to the sample attribute of sample to be stored, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _iattribute Association degree; And, according to the sample sign of sample to be stored, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _ithe user behavior degree of association.

Wherein, m _ifor the sample sign of a sample in n the sample of having stored in described medical refrigerator system, 1≤i≤n.

Concrete, in the medical refrigerator system that the embodiment of the present invention provides, in the storage means of sample, according to the sample attribute of sample to be stored, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _ithe step of Attribute Association degree can be as follows:

Step 1: according to the sample attribute of sample to be stored, set up the attribute feature vector of sample to be stored $f_{\mathrm{mj}}=\{w_{m_{j1}},w_{m_{j2}},......,w_{m_{\mathrm{jk}}},......,w_{m_{\mathrm{js}}}\},$ Wherein, f _mjthe attribute feature vector that represents sample to be stored, represent the weight of k class sample attribute to sample to be stored.

Preferably, in a kind of possible implementation, step 1 specifically can realize in the following manner:

The default sample community set P of traversal, according to the sample attribute of sample to be stored, in conjunction with the first default formula, sets up the attribute feature vector of sample to be stored $f_{\mathrm{mj}}=\{w_{m_{j1}},w_{m_{j2}},......,w_{m_{\mathrm{jk}}},......,w_{m_{\mathrm{js}}}\},$ Wherein, the length of P is s, and the first default formula is as shown in formula (1):

$w_{m_{\mathrm{jk}}}=\left\{\begin{array}{c}1,p_k\∈m_j\\ 0,p_k\∉m_j\end{array}\right.,$ Formula (1)

M _jthe sample sign that represents sample to be stored, p _krepresent k class sample attribute.

Those skilled in the art hold intelligible, and the physical meaning that formula (1) characterizes can be understood as: as sample m _jthere is attribute p _ktime, sample m _jattribute feature vector f _mjcorresponding assignment is 1, as sample m _jdo not there is attribute p _ktime, sample m _jattribute feature vector f _mjcorresponding assignment is 0.

Step 2: according to each sample m in n the sample of having stored in the attribute feature vector of sample to be stored, medical refrigerator system _iattribute feature vector and the second default formula, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _iattribute Association degree, the second default formula is as shown in formula (2):

$\mathrm{simcontent}(m_j,m_i)=\frac{\underset{k=1}{\overset{s}{\mathrm{\Σ}}}{w}_{m_{\mathrm{ik}}}\×w_{m_{\mathrm{jk}}}}{\sqrt{\underset{k=1}{\overset{s}{\mathrm{\Σ}}}{\left(w_{m_{\mathrm{ik}}}\right)}^2}\×\sqrt{\underset{k=1}{\overset{s}{\mathrm{\Σ}}}{\left(w_{m_{\mathrm{jk}}}\right)}^2}}$ Formula (2)

Wherein, represent attribute p _kto sample m _jweight, represent attribute p _kto sample m _iweight, simcontent (m _j, m _i) expression sample m _jwith sample m _ibetween Attribute Association degree.

It should be noted that each sample m in n the sample of having stored in medical refrigerator system _iattribute feature vector may be pre-stored, may be also Real-time Obtaining, the embodiment of the present invention is not done concrete restriction to this.

Preferably, consider in default sample community set P and comprise all properties of storing sample sets, so that the attribute feature vector dimension of sample can be very high, may have influence on counting yield, therefore further, after setting up the attribute feature vector of sample to be stored, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _iattribute Association degree before, can also comprise:

By f _mjdimensionality reduction is tieed up to l, the attribute feature vector after acquisition dimensionality reduction $f_{\mathrm{mj}}^{,}=\{w_{m_{j1}},w_{m_{j2}},......,w_{m_{\mathrm{jk}}},......,w_{m_{\mathrm{jl}}}\},l<s.$

Accordingly, in above-mentioned steps two according to each sample m in n the sample of having stored in the attribute feature vector of sample to be stored, medical refrigerator system _iattribute feature vector and the second default formula, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _iattribute Association degree, specifically can comprise:

According to each sample m in n the sample of having stored in the attribute feature vector after the dimensionality reduction of sample to be stored, medical refrigerator system _idimensionality reduction after attribute feature vector and the revised second default formula, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _iattribute Association degree, the revised second default formula is as shown in formula (3):

$\mathrm{simcontent}(m_j,m_i)=\frac{\underset{k=1}{\overset{l}{\mathrm{\&Sigma;}}}{w}_{m_{\mathrm{ik}}}\&times;w_{m_{\mathrm{jk}}}}{\sqrt{\underset{k=1}{\overset{l}{\mathrm{\&Sigma;}}}{\left(w_{m_{\mathrm{ik}}}\right)}^2}\&times;\sqrt{\underset{k=1}{\overset{l}{\mathrm{\&Sigma;}}}{\left(w_{m_{\mathrm{jk}}}\right)}^2}}$ Formula (3)

According to above-mentioned preferred scheme, can reduce the too high impact on algorithm counting yield of dimension, improve the counting yield of Attribute Association degree.For example, the set of supposing n sample having stored in medical refrigerator system is M={m ₁, m ₂..., m _i..., m _n, according to the sample-attribute matrix of n the sample of having stored in the known medical refrigerator system of formula (1), be M _n*s, the matrix that the capable s of n is listed as, after dimensionality reduction, the sample-attribute matrix of n the sample of having stored in medical refrigerator system is M _n*l, i.e. the matrix of the capable l of n row, and then can reduce the complexity of computing.

It should be noted that, the quantity of sample to be stored may not be one, the embodiment of the present invention is not done concrete restriction to this, when more than one of the quantity of sample to be stored, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _iattribute Association degree specifically refer to, for each sample to be stored in a plurality of samples to be stored, all to determine respectively each sample m in n the sample of having stored in itself and medical refrigerator system _iattribute Association degree.

It should be noted that, by f _mjdimensionality reduction may be incessantly a kind of to the method for l dimension, and the embodiment of the present invention is not done concrete restriction to this.Exemplary, can be according to svd (Singular Value Decomposition, SDV) algorithm by f _mjdimensionality reduction is tieed up to l, and wherein, SDV algorithm is a kind of decomposition method that can be applicable to Arbitrary Matrix, belongs to a part for prior art, specifically can be with reference to the implementation method of prior art, and the embodiment of the present invention is not specifically addressed it.

Concrete, in the medical refrigerator system that the embodiment of the present invention provides, in the storage means of sample, according to the sample sign of sample to be stored, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _ithe user behavior degree of association, specifically can comprise:

According to the sample sign of sample to be stored, in conjunction with user behavior degree of association matrix, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _ithe user behavior degree of association, wherein, user behavior degree of association matrix is to extract affairs set T according to user, in conjunction with the 3rd default formula, determines, the 3rd default formula is as shown in formula (4):

$\mathrm{simevent}(m_j,m_i)=\frac{{\mathrm{\&Sigma;}}_{t_k\&Element;T}a(t_k,m_i)\&times;a(t_k,m_j)}{{\mathrm{\&Sigma;}}_{t_k\&Element;T}a(t_k,m_j)}$ Formula (4)

Wherein, simevent (m _j, m _i) expression sample m _jwith sample m _ibetween the user behavior degree of association, a (t _k, m _v) expression sample m _vweight in the k time extraction affairs, $a(t_k,m_v)=\left\{\begin{array}{c}1,m_v\&Element;t_k\\ 0,m_v\&NotElement;t_k\end{array}\right.,$ V=i, j, t _krepresent to extract affairs, t the k time in set T _k={ m ₁, m ₂..., m _q, m _qrepresent to extract for the k time the sample sign of extracting sample in affairs.

One of ordinary skill in the art will readily recognize that a (t _k, m _v) physical meaning that characterizes can be understood as: as affairs t _kin comprise sample m _vtime, sample m _vweight assignment in the k time extraction affairs is 1, as affairs t _kin do not comprise sample m _vtime, sample m _vweight assignment in the k time extraction affairs is 0.

What one of ordinary skill in the art will readily recognize that the actual reflection of formula (4) is to extract sample m _jtime sample m _ithe probability simultaneously being extracted, probability is higher, characterizes sample m _jwith sample m _ibetween the user behavior degree of association larger.

It should be noted that, from formula (4), the user behavior degree of association between the sample that the embodiment of the present invention provides is directive, i.e. sample m _jwith sample m _ibetween user behavior degree of association simevent (m _j, m _i) and sample m _iwith sample m _jbetween user behavior degree of association simevent (m _i, m _j) be different, it is asymmetric can be understood as user behavior degree of association matrix.

Exemplary, such as sample to be stored is m ₂, in the sample of having stored in medical refrigerator system, comprise m ₁, according to formula (4), sample m to be stored ₂with the sample m having stored ₁the user behavior degree of association be $\mathrm{simevent}(m_2,m_1)=\frac{{\mathrm{\&Sigma;}}_{t_k\&Element;T}a(t_k,m_1)\&times;a(t_k,m_2)}{{\mathrm{\&Sigma;}}_{t_k\&Element;T}a(t_k,m_2)};$

And if sample to be stored is m ₁, in the sample of having stored in medical refrigerator system, comprise m ₂time, according to formula (4), sample m to be stored ₁with the sample m having stored ₂the user behavior degree of association be $\mathrm{simevent}(m_1,m_2)=\frac{{\mathrm{\&Sigma;}}_{t_k\&Element;T}a(t_k,m_1)\&times;a(t_k,m_2)}{{\mathrm{\&Sigma;}}_{t_k\&Element;T}a(t_k,m_1)}.$

Obviously, simevent (m ₂, m ₁) and simevent (m ₁, m ₂) be numerically different, and then user behavior degree of association matrix is asymmetric.

It should be noted that, the user behavior degree of association matrix in the embodiment of the present invention may be pre-stored, may be also that real-time update is obtained, and the embodiment of the present invention is not done concrete restriction to this.

Exemplary, for n the sample of having stored in medical refrigerator system, can provide user behavior degree of association matrix between a kind of sample as shown in Figure 2 here, certainly, user behavior degree of association matrix norm type may be also other form, and the embodiment of the present invention is not done concrete restriction to this.

It should be noted that, in the embodiment of the present invention, determine that Attribute Association degree and definite user behavior degree of association do not have strict priority execution sequence, can first determine Attribute Association degree, then determine the user behavior degree of association; Also can first determine the user behavior degree of association, then determine Attribute Association degree; Can also carry out, the embodiment of the present invention is not done concrete restriction to this simultaneously.

S103, according to each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _iattribute Association degree and the user behavior degree of association, in conjunction with default degree of association adaptive factor, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _ithe sample degree of association.

Concrete, in the embodiment of the present invention, the sample degree of association is based on Attribute Association degree and the user behavior degree of association, according to degree of association adaptive factor, finally determines.That is, Attribute Association degree and user behavior degree of association role size when the sample degree of association is determined can change by adjusting degree of association adaptive factor.

In a kind of possible implementation, step S103 specifically can comprise:

According to each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _iattribute Association degree and the user behavior degree of association, in conjunction with the 4th default formula, determine respectively each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _ithe sample degree of association, the 4th default formula is as shown in formula (5):

sim(m _j，m _i)＝β×simcontent(m _j，m _i)+(1-β)×simevent(m _j，m _i)

Formula (5)

Wherein, sim (m _j, m _i) expression sample m _jwith sample m _ibetween the sample degree of association, β represents degree of association adaptive factor,

It should be noted that, by formula (5), can be found out, degree of association adaptive factor β can extraction frequency per sample regulate automatically, and when sample to be stored is fresh sample, β is 1, and the sample degree of association is determined by the Attribute Association degree of sample completely; Along with sample to be stored is extracted the increase of frequency, the value of β trends towards 0 gradually, thereby makes the user behavior degree of association determine largely the sample degree of association, can solve well the cold start-up problem in medical refrigerator system so on the one hand; On the other hand, because β can extraction frequency per sample regulate automatically, when determining the sample degree of association, considered along with sample to be stored, to be extracted under real scene the increase of frequency, the factor that the impact of the user behavior degree of association in the sample degree of association is increasing, therefore definite sample degree of association is more accurate, and then the position to be stored of the sample to be stored that the degree of association is determined is per sample more reasonable, make two or more samples that the sample degree of association is larger can be stored in neighbour's position.

Cold start-up can be regarded as when depositing fresh sample in, also without any user's access behavioural information, cannot obtain the degree of association between sample according to user's behavioural information.In order to address this problem, added the attribute information of sample to assist the calculating of the sample degree of association of fresh sample.

S104, by each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _isample degree of association sequence, obtain ranking results.

In a kind of possible implementation, step S104 specifically can comprise:

According to each sample m in n the sample of having stored in sample to be stored and medical refrigerator system _ithe sample degree of association, determine r sample the highest with the sample degree of association to be stored in n sample, form neighbours' sample set R of sample to be stored.

That is, the sortord providing by the embodiment of the present invention, can, by determining with the highest r the sample of the sample degree of association to be stored, form neighbours' sample set R of sample to be stored

S105, call sample physical location storage list, and according to ranking results and sample physical location storage list, determine the position to be stored of sample to be stored.

In a kind of possible implementation, specifically can comprise a)-c of step S105):

A) call sample physical location storage list, and determine respectively the memory disc numbering of r sample in medical refrigerator system according to ranking results and sample physical location storage list, form the memory disc numbering set W of neighbours' sample of sample to be stored.

B) according to the 5th default formula, calculate respectively the matching degree of each the memory disc numbering in sample to be stored and memory disc numbering set W, the 5th default formula is as shown in formula (6):

$S{\mathrm{core}}_{Z\&Element;W}={\mathrm{\&Sigma;}}_{m_i\&Element;R}a(m_i,Z)*\mathrm{sim}(m_j,m_i)$ Formula (6)

Wherein, Z represents a memory disc numbering in memory disc numbering set W, $a(m_i,Z)=\left\{\begin{array}{c}1,m_i\&Element;Z\\ 0,m_i\&NotElement;Z\end{array}\right.,$ A(m _i, Z) represent sample m _iweight on memory disc numbering Z.

C) the highest memory disc numbering of matching degree is defined as to the memory disc numbering corresponding to position to be stored of sample to be stored.

It should be noted that, above-mentioned is only the exemplary step S105 preferred implementation that provides, and certainly, step S105 also may exist other possible implementation, and the embodiment of the present invention is not done concrete restriction to this.

S106, by sample storage to be stored to position to be stored.

Concrete, after the location positioning to be stored of sample to be stored, by the pallet window ejecting on medical refrigerator system cabinet door, sample to be stored can be input to cabinet, and then the automatic storage device by medical refrigerator system by sample storage to be stored to assigned address, such as can be by mechanical arm by sample storage to be stored to assigned address.

Further, for storing sample, in conjunction with sample degree of association matrix, can regularly carry out cluster analysis, by interior location, adjust, further optimize memory location.That is, as shown in Figure 3, in the medical refrigerator system that the embodiment of the present invention provides, in the storage means of sample, can also comprise:

S107, in Preset Time, to n the sample of having stored in medical refrigerator system per sample the degree of association carry out cluster analysis, obtain cluster result.

Concrete, the Preset Time in the embodiment of the present invention can be specifically non-user service time, and such as a certain set time section in morning every day, the embodiment of the present invention is not done concrete restriction to this.

Concrete, when the degree of association is carried out cluster analysis per sample to n the sample of having stored in medical refrigerator system, can adopt existing Canopy clustering algorithm, can be also other, the embodiment of the present invention is not done concrete restriction to it.

Exemplary, provide here a kind of to n the sample of having stored in medical refrigerator system per sample the degree of association to carry out the example of Canopy cluster analysis as follows:

Such as first, extract per sample the frequency and determine that sample A is the first initial center point, and set two sample degree of association threshold value t1, t2, wherein t1 > t2.

Secondly, travel through each storage sample, if the sample degree of association of sample B and sample A is greater than t1, sample B and sample A are considered as similar, delete the record of sample B simultaneously; If the sample degree of association of sample B and sample A between t1 and t2, the record of temporary transient keeping sample B; If the sample degree of association of sample B and sample A is less than t2, sample B and sample A are considered as non-similar, sample B is considered as to the sample to be selected of next central point simultaneously, repeat above-mentioned computation process, finally can obtain N central point, the corresponding sample of each central point, also has a plurality of samples simultaneously in sample degree of association threshold range, and a plurality of samples in this sample degree of association threshold range can be considered with the sample of this central point and are classified as a class.

S108, according to cluster result, the memory location of n the sample of having stored in medical refrigerator system is optimized to adjustment, and according to optimizing and revising more fresh sample physical location storage list of result.

Exemplary, according to cluster result, the method that the memory location of n the sample of having stored in medical refrigerator system is optimized to adjustment can be as follows:

According to cluster result, shine upon corresponding locus distribution/layout, thereby obtain the memory location relation after optimizing, and then by the automatic storage device of medical refrigerator system, sample position is readjusted, such as can sample position being readjusted by mechanical arm.

Wherein, due to cluster result be n sample to having stored in medical refrigerator system per sample the degree of association carry out obtaining after cluster analysis, and optimize and revise result, be to obtain after the memory location of n the sample of having stored in medical refrigerator system being optimized to adjustment according to cluster result, therefore according to optimizing and revising more fresh sample physical location storage list of result, can make the memory location of the sample after the corresponding optimization of this physical location storage list, and then according to the sample degree of association of each sample in n the sample of having stored in sample to be stored and medical refrigerator system, call sample physical location storage list, while determining the position to be stored of sample to be stored, can obtain more rational memory location result.

The storage means of sample in the medical refrigerator system providing based on the embodiment of the present invention, after obtaining the sample sign and sample attribute of sample to be stored, attribute is determined each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system per sample _iattribute Association degree; obtain treating stored sample and store the Attribute Association degree magnitude relationship of sample; such as conventionally paying the utmost attention to the close position that sample to be stored is placed on to the sample larger with its Attribute Association degree, sign is determined each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system per sample simultaneously _ithe user behavior degree of association, obtain user's extraction affairs behavior or the influence degree data that operating habit is put sample position to be stored, if such as user often extracts the sample A having stored in sample to be stored and described medical refrigerator system jointly, pay the utmost attention to the close position that sample to be stored is placed on to sample A.

According to Attribute Association degree and the user behavior degree of association, in conjunction with default degree of association adaptive factor, determine each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe sample degree of association.Wherein, each sample m in n the sample that Attribute Association degrees of data and user behavior degree of association data have been stored in determining sample to be stored and described medical refrigerator system _ithe sample degree of association time produce and adaptive factor is corresponding separately influence degree.Utilize this sample calculation of relationship degree method, when storage sample to be stored, definite while of the position to be stored of sample to be stored is determined by sample to be stored and Attribute Association degree and the user behavior degree of association of having stored between sample, in prior art, put at random or effect per sample solely, the storage condition of sample or user's extraction frequencies etc. are because usually determining the technical scheme of sample storage position, the sample calculation of relationship degree method that example technique scheme of the present invention provides, can objectively reflect sample to be stored comparatively comprehensively and store correlation degree or the correlativity size between sample, thereby for calculative determination position to be stored provides foundation more accurately, make sample memory location determine more reasonable, regular enhancing.

By by each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _isample degree of association sequence, obtain ranking results, and call sample physical location storage list, according to ranking results and described sample physical location storage list, find out and the maximally related close position of the sample position of having stored, and then this close position can be defined as to the position to be stored of described sample to be stored, such as, select the two or more samples larger with the sample degree of association data of sample to be stored, the close position of these two or more samples is defined as to the position of sample to be stored.

Finally by described sample storage to be stored to described position to be stored.

To sum up, pass through technique scheme, on the one hand, utilize the calculating of the sample degree of association to determine memory location, reduced randomness when sample is deposited, strengthened the regularity between the position that sample deposits, the rationalization degree of the memory location of medical treatment refrigerator inner sample improves, can be when a plurality of sample of the disposable storage of user, reduce the storage operation time of automation equipment, save the stand-by period of storing process, this be because, the sample that the degree of association is larger has been dispensed on contiguous position, medical treatment refrigerator memory storage can be disposable by a plurality of sample storage in contiguous position, even if or store one at every turn, its operation route is also relatively simple, thereby the running time reduces, storage efficiency improves, on the other hand, user is when a plurality of sample of disposable extraction, the regularity of the position of depositing due to each sample strengthens, conventionally be placed on contiguous position, reduce too the extraction running time of automation equipment, save the stand-by period of leaching process, no matter efficiency is all improved when making user in storage or extracting, this has also improved the competitive power of the medical Freezer Products of applying this storage means simultaneously.

The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited to this, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; can expect easily changing or replacing, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion by the described protection domain with claim.

Claims (9)

1. a storage means for sample in medical refrigerator system, is characterized in that, described method comprises:

Obtain sample sign and the sample attribute of sample to be stored;

According to the sample attribute of described sample to be stored, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree; And, according to the sample sign of described sample to be stored, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe user behavior degree of association, wherein, m _ifor the sample sign of a sample in n the sample of having stored in described medical refrigerator system, 1≤i≤n;

According to each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree and the user behavior degree of association, in conjunction with default degree of association adaptive factor, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe sample degree of association;

By each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _isample degree of association sequence, obtain ranking results;

Call sample physical location storage list, and according to described ranking results and described sample physical location storage list, determine the position to be stored of described sample to be stored;

By described sample storage to be stored to described position to be stored.

2. method according to claim 1, is characterized in that, described according to each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree and the user behavior degree of association, in conjunction with default degree of association adaptive factor, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe sample degree of association, specifically comprise:

According to each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree and the user behavior degree of association, in conjunction with the 4th default formula, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe sample degree of association, the described the 4th default formula comprises:

sim(m _j，m _i)＝β×simcontent(m _j，m _i)+(1-β)×simevent(m _j，m _i)，

Wherein, m _jthe sample sign that represents sample to be stored, sim (m _j, m _i) expression sample m _jwith sample m _ibetween the sample degree of association, simcontent (m _j, m _i) expression sample m _jwith sample m _ibetween Attribute Association degree, simevent (m _j, m _i) expression sample m _jwith sample m _ibetween the user behavior degree of association, β represents degree of association adaptive factor, t represents that user extracts affairs set, t _krepresent to extract affairs, t the k time in set T _k={ m ₁, m ₂..., m _q, m _qrepresent to extract for the k time the sample sign of extracting sample in affairs, a (t _k, m _j) expression sample m _jweight in the k time extraction affairs, $a(t_k,m_j)=\left\{\begin{array}{c}1,m_j\&Element;t_k\\ 0,m_j\&NotElement;t_k\end{array}\right..$

3. method according to claim 1 and 2, is characterized in that, described according to the sample attribute of described sample to be stored, determines respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree, comprising:

According to the sample attribute of described sample to be stored, the attribute feature vector of setting up described sample to be stored is $f_{\mathrm{mj}}=\{w_{m_{j1}},w_{m_{j2}},......,w_{m_{\mathrm{jk}}},......,w_{m_{\mathrm{js}}}\},$ Wherein, f _mjthe attribute feature vector that represents sample to be stored, represent the weight of k class sample attribute to described sample to be stored;

According to each sample m in n the sample of having stored in the attribute feature vector of described sample to be stored, described medical refrigerator system _iattribute feature vector and the second default formula, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree, the described second default formula comprises:

$\mathrm{simcontent}(m_j,m_i)=\frac{\underset{k=1}{\overset{s}{\mathrm{\&Sigma;}}}{w}_{m_{\mathrm{ik}}}\&times;w_{m_{\mathrm{jk}}}}{\sqrt{\underset{k=1}{\overset{s}{\mathrm{\&Sigma;}}}{\left(w_{m_{\mathrm{ik}}}\right)}^2}\&times;\sqrt{\underset{k=1}{\overset{s}{\mathrm{\&Sigma;}}}{\left(w_{m_{\mathrm{jk}}}\right)}^2}},$

Wherein, m _jthe sample sign that represents sample to be stored, represent that k class sample attribute is to sample m _jweight, represent that k class sample attribute is to sample m _iweight, simcontent (m _j, m _i) expression sample m _jwith sample m _ibetween Attribute Association degree.

4. method according to claim 3, is characterized in that, described according to the sample attribute of described sample to be stored, the attribute feature vector of setting up described sample to be stored is $f_{\mathrm{mj}}=\{w_{m_{j1}},w_{m_{j2}},......,w_{m_{\mathrm{jk}}},......,w_{m_{\mathrm{js}}}\},$ Specifically comprise:

The default sample community set P of traversal, according to the sample attribute of described sample to be stored, in conjunction with the first default formula, the attribute feature vector of setting up described sample to be stored is $f_{\mathrm{mj}}=\{w_{m_{j1}},w_{m_{j2}},......,w_{m_{\mathrm{jk}}},......,w_{m_{\mathrm{js}}}\},$ Wherein, the length of P is s, and the described first default formula is: $w_{m_{\mathrm{jk}}}=\left\{\begin{array}{c}1,p_k\&Element;m_j\\ 0,p_k\&NotElement;m_j\end{array}\right.,$ P _krepresent k class sample attribute.

5. according to the method described in claim 3 or 4, it is characterized in that, after the described attribute feature vector of setting up described sample to be stored, describedly determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree before, also comprise:

By described f _mjdimensionality reduction is tieed up to l, the attribute feature vector after acquisition dimensionality reduction $f_{\mathrm{mj}}^{,}=\{w_{m_{j1}},w_{m_{j2}},......,w_{m_{\mathrm{jk}}},......,w_{m_{\mathrm{jl}}}\},l<s;$

Described according to each sample m in n the sample of having stored in the attribute feature vector of described sample to be stored, described medical refrigerator system _iattribute feature vector and the second default formula, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree, specifically comprise:

According to each sample m in n the sample of having stored in the attribute feature vector after the dimensionality reduction of described sample to be stored, described medical refrigerator system _idimensionality reduction after attribute feature vector and the revised second default formula, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _iattribute Association degree, the described revised second default formula comprises:

$\mathrm{simcontent}(m_j,m_i)=\frac{\underset{k=1}{\overset{l}{\mathrm{\&Sigma;}}}{w}_{m_{\mathrm{ik}}}\&times;w_{m_{\mathrm{jk}}}}{\sqrt{\underset{k=1}{\overset{l}{\mathrm{\&Sigma;}}}{\left(w_{m_{\mathrm{ik}}}\right)}^2}\&times;\sqrt{\underset{k=1}{\overset{l}{\mathrm{\&Sigma;}}}{\left(w_{m_{\mathrm{jk}}}\right)}^2}}.$

6. according to the method described in claim 1-5 any one, it is characterized in that, the described sign of the sample according to described sample to be stored, determines respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe user behavior degree of association, comprising:

According to the sample sign of described sample to be stored, in conjunction with user behavior degree of association matrix, determine respectively each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe user behavior degree of association, wherein, described user behavior degree of association matrix is to extract affairs set T according to user, in conjunction with the 3rd default formula, determines, the described the 3rd default formula comprises:

$\mathrm{simevent}(m_j,m_i)=\frac{{\mathrm{\&Sigma;}}_{t_k\&Element;T}a(t_k,m_i)\&times;a(t_k,m_j)}{{\mathrm{\&Sigma;}}_{t_k\&Element;T}a(t_k,m_j)},$ Wherein, m _jthe sample sign that represents sample to be stored, simevent (m _j, m _i) expression sample m _jwith sample m _ibetween the user behavior degree of association, a (t _k, m _v) expression sample m _vweight in the k time extraction affairs, $a(t_k,m_v)=\left\{\begin{array}{c}1,m_v\&Element;t_k\\ 0,m_v\&NotElement;t_k\end{array}\right.,$ V=i, j, t _krepresent to extract affairs, t the k time in set T _k={ m ₁, m ₂..., m _q, m _qrepresent to extract for the k time the sample sign of extracting sample in affairs.

7. according to the method described in claim 1-6 any one, it is characterized in that, described by each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _isample degree of association sequence, obtain ranking results, specifically comprise:

According to each sample m in n the sample of having stored in described sample to be stored and described medical refrigerator system _ithe sample degree of association, determine r sample the highest with the described sample degree of association to be stored in a described n sample, form neighbours' sample set R of described sample to be stored.

8. method according to claim 7, is characterized in that, described in call sample physical location storage list, and according to described ranking results and described sample physical location storage list, determine the position to be stored of described sample to be stored, specifically comprise:

Call described sample physical location storage list, and determine respectively the memory disc numbering of a described r sample in described medical refrigerator system according to described ranking results and described sample physical location storage list, form the memory disc numbering set W of neighbours' sample of described sample to be stored;

According to the 5th default formula, calculate respectively the matching degree of each the memory disc numbering in described sample to be stored and described memory disc numbering set W, the described the 5th default formula comprises:

$S{\mathrm{core}}_{Z\&Element;W}={\mathrm{\&Sigma;}}_{m_i\&Element;R}a(m_i,Z)\&times;\mathrm{sim}(m_j,m_i),$ Wherein, Z represents a memory disc numbering in memory disc numbering set W, $a(m_i,Z)=\left\{\begin{array}{c}1,m_i\&Element;Z\\ 0,m_i\&NotElement;Z\end{array}\right.,$ A(m _i, Z) represent sample m _iweight on memory disc numbering Z;

The highest memory disc numbering of described matching degree is defined as to the memory disc numbering corresponding to position to be stored of described sample to be stored.

9. according to the method described in claim 1-8 any one, it is characterized in that, described method also comprises:

In Preset Time, to n the sample of having stored in described medical refrigerator system per sample the degree of association carry out cluster analysis, obtain cluster result;

According to described cluster result, the memory location of n the sample of having stored in described medical refrigerator system is optimized to adjustment, and upgrades described sample physical location storage list according to the described result of optimizing and revising.