CN104143137B - The storage method of sample in medical refrigerator system - Google Patents
The storage method of sample in medical refrigerator system Download PDFInfo
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- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 description 3
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Abstract
The embodiment of the present invention provides the storage method of sample in medical refrigerator system, at least solves the problems, such as the rationalization degree deficiency of sample position in intelligent medical refrigerator system and influences access efficiency.Including：Obtain the sample mark and sample properties of sample to be stored；According to the sample properties of sample to be stored, each sample m in the n sample stored in sample to be stored and medical refrigerator system is determined respectivelyiAttribute Association degree；Sample mark according to sample to be stored, determines sample to be stored with each sample m in the n sample for having stored respectivelyiThe user behavior degree of association；According to Attribute Association degree and the user behavior degree of association, with reference to degree of association adaptive factor, determine sample to be stored with each sample m in the n sample for having stored respectivelyiThe sample degree of association；By sample relational degree taxis, sample physical location storage table is called, determine the position to be stored of sample to be stored；By sample storage to be stored to position to be stored.The present invention is applied to medical sample data management domain.
Description
Technical Field
The invention relates to the field of medical sample data management, in particular to a method for storing samples in a medical refrigerator system.
Background
With the development of the medical industry, the intelligent medical refrigerator as an important classification in refrigerators is widely applied to various industries and fields such as scientific research institutions, medical health, military aviation, biopharmaceuticals, pharmacies, pharmaceutical factories, blood stations and the like, and becomes one of essential important medical equipment.
The intelligent medical refrigerator has various products, such as a blood refrigerator, a medicine refrigerator, a vaccine storage box, a refrigeration and freezing box, a low-temperature storage box, a deep low-temperature storage box, a medical insulation box and the like. The intelligent medical refrigerator has a great difference with the common medical refrigerator in performance, so that the intelligent medical refrigerator not only needs to meet harsh environmental requirements such as temperature, humidity and the like when samples or medicines are stored, but also reduces the influence of human or external environment on the environment in the refrigerator as much as possible when the samples or the medicines are stored or extracted, and best meets the requirement of automatic storage or extraction. However, in the automatic storage process of the intelligent medical refrigerator, a new problem needs to be considered, that is, because the number of samples stored or to be stored in the cabinet is large, if the storage positions of the samples are not reasonable enough, for example, when a plurality of samples are stored at one time, the positions where the samples are stored are not regular, so that the samples are not reasonable, the automatic equipment needs a long operation time, and meanwhile, the automatic equipment also needs a long operation time when the samples are extracted later, so that the waiting time of a user is prolonged, and the storage or extraction efficiency is seriously influenced.
Therefore, it is an urgent technical problem to seek to solve the problem that the rationalization degree of the sample position in the intelligent medical refrigerator system is insufficient to affect the storage or extraction efficiency.
Disclosure of Invention
The embodiment of the invention provides a method for storing samples in a medical refrigerator system, which at least solves the problem that the storage or extraction efficiency is influenced due to insufficient rationalization degree of the positions of the samples in the intelligent medical refrigerator system, and improves the storage or extraction efficiency of the samples in the intelligent medical refrigerator system.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
in a first aspect, a method of storing samples in a medical cooler system is provided, the method comprising:
acquiring a sample identifier and a sample attribute of a sample to be stored;
respectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system according to the sample attribute of the sample to be storediThe attribute association degree of (2); and respectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system according to the sample identification of the sample to be storediIn the user behavior correlation degree of (1), wherein miIdentifying a sample of n samples stored in the medical refrigerator system, wherein i is more than or equal to 1 and less than or equal to n;
according to the samples to be stored and each sample m in n samples stored in the medical refrigerator systemiRespectively determining the sample to be stored and the user behavior association degree by combining with a preset association degree self-adaptive factorEach sample m of n samples stored in the medical refrigerator systemiThe degree of sample correlation of (a);
the sample to be stored and each sample m in n samples stored in the medical refrigerator system are storediThe sample relevance degree is sequenced to obtain a sequencing result;
calling a sample physical position storage table, and determining a to-be-stored position of the to-be-stored sample according to the sequencing result and the sample physical position storage table;
and storing the sample to be stored to the position to be stored.
Based on the sample storage method in the medical refrigerator system provided by the embodiment of the invention, after the sample identification and the sample attribute of the sample to be stored are obtained, the sample to be stored and each m sample in n samples stored in the medical refrigerator system can be determined according to the sample attributeiThe correlation degree of the attributes of the sample to be stored and the stored sample is obtained, for example, the sample to be stored is preferably placed at a position adjacent to the sample with the larger correlation degree of the attributes, and the sample to be stored and each sample m of the n samples stored in the medical freezer system are determined according to the sample identificationiThe user behavior correlation degree of the medical freezer system is obtained, and data of influence degree of the user on the placement of the sample to be stored, such as if the user frequently extracts the sample to be stored and the sample a stored in the medical freezer system together, the sample to be stored is preferentially placed at a position adjacent to the sample a.
Determining m samples of the samples to be stored and each sample m of n samples stored in the medical refrigerator system according to the attribute relevance and the user behavior relevance by combining a preset relevance self-adaptive factoriThe degree of sample correlation of (a). Wherein the attribute correlation degree data and the user behavior correlation degree data determine each sample m in the samples to be stored and the n samples stored in the medical refrigerator systemiDegree of sample correlation ofThe degrees of influence corresponding to the respective adaptation factors are generated. By using the sample relevance calculating method, when the sample to be stored is stored, the position to be stored of the sample to be stored is determined by the attribute relevance between the sample to be stored and the stored sample and the user behavior relevance, and compared with the technical scheme that the sample storage position is determined randomly or singly according to the sample efficacy, the storage condition of the sample or the extraction frequency of a user and other factors in the prior art, the sample relevance calculating method provided by the exemplary technical scheme of the invention can comprehensively and objectively reflect the relevance degree or the relevance size between the sample to be stored and the stored sample, thereby providing more accurate basis for calculating and determining the position to be stored, ensuring more reasonable determination of the storage position of the sample and enhancing the regularity.
By mixing the sample to be stored with each sample m of n samples already stored in the medical freezer systemiThe sample association degree sorting is performed to obtain a sorting result, a sample physical position storage table is called, according to the sorting result and the sample physical position storage table, a neighboring position most relevant to the stored sample position is found out, and then the neighboring position can be determined as the to-be-stored position of the to-be-stored sample, for example, two or more samples with larger sample association degree data with the to-be-stored sample are selected, and the neighboring positions of the two or more samples are determined as the positions of the to-be-stored sample.
And finally, storing the sample to be stored to the position to be stored.
In summary, according to the above technical solutions, on one hand, the storage location is determined by using the calculation of the sample association degree, so that the randomness of the samples during storage is reduced, the regularity of the locations where the samples are stored is enhanced, the rationalization degree of the storage location of the samples inside the medical refrigerator is improved, and when a user stores a plurality of samples at one time, the storage operation time of the automation equipment is reduced, and the waiting time of the storage process is saved, because the samples with the large association degree are already distributed at the adjacent locations, the medical refrigerator storage device can store a plurality of samples at the adjacent locations at one time, or even if one sample is stored at each time, the operation route is relatively simple, so that the operation time is reduced, and the storage efficiency is improved; on the other hand, when a user extracts a plurality of samples at one time, the regular storage positions of the samples are enhanced and are usually placed at adjacent positions, so that the extraction operation time of the automatic equipment is also reduced, the waiting time of the extraction process is saved, the efficiency of the user is improved when the user stores or extracts the samples, and the competitiveness of the medical refrigerator product applying the storage method is improved.
Drawings
Fig. 1 is a first schematic flow chart illustrating a method for storing a sample in a medical freezer system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a user behavior correlation matrix between samples according to an embodiment of the present invention;
fig. 3 is a second schematic flowchart of a sample storage method in a medical freezer system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For the convenience of clearly describing the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the terms "first" and "second" are used to distinguish the same items or similar items with basically the same functions and actions, and those skilled in the art can understand the terms "first" and "second" and do not limit the quantity and execution order.
The first embodiment,
The embodiment of the invention provides a method for storing samples in a medical refrigerator system, which is specifically shown in figure 1 and comprises the following steps:
s101, obtaining a sample identification and a sample attribute of a sample to be stored.
In particular, the sample identifier may identify a sample, typically the name of the sample, and the sample attribute is information characterizing the sample.
Exemplary, sample attributes may include: structure type (e.g., information pertaining to chemical structure such as benzene aroma type), source/manufacturer information, biological activity, pop information, stored temperature, humidity environmental parameters, etc.
Of course, the above is merely an exemplary list of some types of sample attributes, and the sample attributes may also include other types, which is not specifically limited in the embodiment of the present invention.
S102, respectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system according to the sample attribute of the sample to be storediThe attribute association degree of (2); and respectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system according to the sample identification of the sample to be storediThe user behavior association degree.
Wherein m isiAnd i is more than or equal to 1 and less than or equal to n for the sample identification of one sample in n samples stored in the medical refrigerator system.
Specifically, in the method for storing samples in the medical freezer system provided by the embodiment of the present invention, the samples to be stored and each of n samples stored in the medical freezer system are respectively determined according to the sample attributes of the samples to be storedA sample miThe step of associating the attributes of (1) may be as follows:
the method comprises the following steps: establishing an attribute feature vector of the sample to be stored according to the sample attribute of the sample to be storedWherein f ismjA feature vector representing the attributes of the sample to be stored,representing the weight of the kth class sample attribute to the sample to be stored.
Preferably, in a possible implementation manner, the step one may be specifically implemented by:
traversing a preset sample attribute set P, and establishing an attribute feature vector of the sample to be stored according to the sample attribute of the sample to be stored and by combining a first preset formulaWherein, the length of P is s, and the first preset formula is shown as formula (1):
formula (1)
mjSample identity, p, representing the sample to be storedkRepresenting the kth class of sample attributes.
It is readily understood by those skilled in the art that the actual meaning of the characterization of equation (1) can be understood as: when the sample mjHaving an attribute pkWhile, sample mjAttribute feature vector f ofmjCorresponding toAssigned a value of 1, when sample mjDoes not have an attribute pkWhile, sample mjAttribute feature vector f ofmjCorresponding toThe value is assigned to 0.
Step two: according to the attribute feature vector of the sample to be stored, each sample m in n samples stored in the medical refrigerator systemiRespectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator systemiThe second preset formula is shown as formula (2):
formula (2)
Wherein,representing an attribute pkFor sample mjThe weight of (a) is determined,representing an attribute pkFor sample miWeight of (c), simcontent (m)j，mi) Represents a sample mjWith sample miThe degree of attribute association between.
It should be noted that each of the n samples m stored in the medical freezer systemiThe attribute feature vector of (1) may be pre-stored or may be obtained in real time, which is not specifically limited in the embodiment of the present invention.
Preferably, considering that the preset sample attribute set P includes all attributes of the stored sample set, so that the attribute feature vector dimension of the sample is high, which may affect the calculation efficiency, further, after the attribute feature vector of the sample to be stored is established, the sample to be stored and each sample m of the n samples stored in the medical freezer system are determined separatelyiBefore the attribute association degree, the method may further include:
will f ismjReducing the dimension to l dimension to obtain the attribute feature vector after dimension reduction
Correspondingly, in the second step, each sample m in the n samples stored in the medical refrigerator system is selected according to the attribute feature vector of the sample to be storediRespectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator systemiThe attribute association degree of (2) may specifically include:
according to the attribute feature vector of the sample to be stored after dimensionality reduction and each sample m in n samples stored in the medical refrigerator systemiThe attribute feature vector after dimension reduction and the corrected second preset formula respectively determine the sample to be stored and each sample m in n samples stored in the medical refrigerator systemiThe modified second preset formula is shown as formula (3):
formula (3)
According to the preferable scheme, the influence of overhigh dimensionality on the algorithm calculation efficiency can be reduced, and the calculation efficiency of the attribute association degree is improved. For example, assume that the set of n samples stored in the medical freezer system is M ═ M1，m2，...，mi，...，mnAnd (3) according to the formula (1), the sample-attribute matrix of n samples stored in the medical refrigerator system is Mn*sThat is, the matrix of n rows and s columns, and the sample-attribute matrix of n samples stored in the medical freezer system after dimension reduction is Mn*lI.e. a matrix of n rows and l columns, and thus the complexity of the operation can be reduced.
It should be noted that the number of samples to be stored may not be one, and that the present invention is practiced in that caseThe embodiment is not particularly limited, and when the number of the samples to be stored is more than one, the samples to be stored and each sample m of the n samples stored in the medical freezer system are respectively determinediThe attribute association degree of (1) specifically means that for each sample to be stored in a plurality of samples to be stored, m of the sample to be stored and each sample m of n samples stored in the medical freezer system are respectively determinediThe attribute association degree of (2).
In addition, f ismjThere may be more than one method for reducing the dimension to l, which is not limited in this embodiment of the present invention. Illustratively, f may be decomposed according to a Singular Value Decomposition (SDV) algorithmmjDimension reduction to l dimension, wherein the SDV algorithm is a decomposition method applicable to any matrix, and belongs to a part of the prior art, and reference may be made to the implementation method of the prior art, which is not specifically described in the embodiments of the present invention.
Specifically, in the method for storing samples in the medical freezer system provided by the embodiment of the present invention, the samples to be stored and each sample m of n samples stored in the medical freezer system are respectively determined according to the sample identifiers of the samples to be storediThe user behavior association degree may specifically include:
respectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system according to the sample identification of the sample to be stored and by combining the user behavior correlation matrixiThe user behavior association degree matrix is determined by combining a third preset formula according to the user extraction transaction set T, wherein the third preset formula is shown as a formula (4):
formula (4)
Wherein, simevent (m)j，mi) Represents a sample mjWith sample miDegree of association between user behaviors, a (t)k，mv) Watch (A)Sample mvThe weight in the transaction is fetched the k-th time,v＝i，j，tkrepresents the kth fetch transaction in the set T, Tk＝{m1，m2，...，mq}，mqA sample identification representing the sample taken in the kth extraction transaction.
As will be readily understood by those skilled in the art, a (t)k，mv) The actual meaning of the characterization can be understood as: when transaction tkIn which sample m is containedvWhile, sample mvThe weight in the kth fetch transaction is assigned a value of 1, when the transaction tkIn which sample m is not includedvWhile, sample mvThe weight in the kth fetch transaction is assigned a value of 0.
As will be readily understood by those skilled in the art, equation (4) actually reflects the extraction of sample mjHour sample miProbability of being extracted simultaneously, the higher the probability, the characterization sample mjWith sample miThe greater the degree of correlation of user behavior therebetween.
It should be noted that, as can be seen from the formula (4), the user behavior correlation degree between the samples provided by the embodiment of the present invention is directional, that is, the sample mjWith sample miDegree of association between user behaviors simevent (m)j，mi) With sample miWith sample mjDegree of association between user behaviors simevent (m)i，mj) Differently, it can be understood that the user behavior association matrix is asymmetric.
Illustratively, the sample to be stored is, for example, m2M is contained in the stored sample in the medical refrigerator system1According to equation (4), sample m is to be stored2With stored sample m1The degree of association of the user behavior is
And m if the sample to be stored is m1M is contained in the stored sample in the medical refrigerator system2According to equation (4), the sample m to be stored1With stored sample m2The degree of association of the user behavior is
Obviously, simevent (m)2，m1) And simevent (m)1，m2) The values are different, and the user behavior association matrix is asymmetric.
It should be noted that, in the embodiment of the present invention, the user behavior association matrix may be pre-stored, or may be obtained by updating in real time, which is not specifically limited in the embodiment of the present invention.
For example, for n samples stored in the medical refrigerator system, a user behavior correlation matrix between the samples may be provided as shown in fig. 2, and of course, the model of the user behavior correlation matrix may be in other forms, which is not particularly limited in the embodiment of the present invention.
It should be noted that, in the embodiment of the present invention, determining the attribute association degree and determining the user behavior association degree do not have a strict sequential execution order, and the attribute association degree may be determined first, and then the user behavior association degree may be determined; or determining the user behavior association degree and then determining the attribute association degree; and may also be performed simultaneously, which is not specifically limited by the embodiments of the present invention.
S103, according to the sample to be stored and each sample m in n samples stored in the medical refrigerator systemiRespectively determining m samples of the samples to be stored and n samples stored in the medical refrigerator system by combining preset relevance adaptive factorsiThe degree of sample correlation of (a).
Specifically, in the embodiment of the present invention, the sample association degree is finally determined according to an association degree adaptive factor based on the attribute association degree and the user behavior association degree. That is, the role played by the attribute relevance and the user behavior relevance in the sample relevance determination can be changed by adjusting the relevance adaptive factor.
In a possible implementation manner, step S103 may specifically include:
according to the samples to be stored and each sample m in n samples stored in the medical refrigerator systemiRespectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system by combining a fourth preset formulaiThe fourth predetermined formula is shown in formula (5):
sim(mj，mi)＝β×simcontent(mj，mi)+(1-β)×simevent(mj，mi)
formula (5)
Wherein, sim (m)j，mi) Represents a sample mjWith sample miThe degree of sample correlation therebetween, β denotes a correlation adaptive factor,
it should be noted that, as can be seen from the formula (5), the association degree adaptive factor β can be automatically adjusted according to the extraction frequency of the sample, when the sample to be stored is a new sample, β is 1, and the sample association degree is completely determined by the attribute association degree of the sample; along with the increase of the extraction frequency of the samples to be stored, the value of beta gradually tends to 0, so that the user behavior correlation degree greatly determines the sample correlation degree, and the cold start problem in the medical freezer system can be well solved; on the other hand, the beta can be automatically adjusted according to the extraction frequency of the sample, namely, the factor that the influence of the user behavior association degree in the sample association degree is larger and larger along with the increase of the extraction frequency of the sample to be stored in a real scene is considered when the sample association degree is determined, so that the determined sample association degree is more accurate, the position to be stored of the sample to be stored, which is determined according to the sample association degree, is more reasonable, and two or more samples with larger sample association degrees can be stored in the adjacent positions.
The cold start can be understood as that when a new sample is stored, no user accesses the behavior information, and the correlation degree between the samples cannot be obtained according to the behavior information of the user. In order to solve the problem, the attribute information of the sample is added to assist the calculation of the sample relevance of the new sample.
S104, mixing the samples to be stored with each sample m in n samples stored in the medical refrigerator systemiAnd (4) sorting the sample relevance to obtain a sorting result.
In a possible implementation manner, step S104 may specifically include:
according to the samples to be stored and each sample m in n samples stored in the medical refrigerator systemiDetermining R samples with the highest correlation degree with the samples to be stored in the n samples to form a neighbor sample set R of the samples to be stored.
That is, through the sorting method provided by the embodiment of the present invention, R samples with the highest correlation with the samples to be stored can be determined to form the neighbor sample set R of the samples to be stored
And S105, calling a sample physical position storage table, and determining the position to be stored of the sample to be stored according to the sequencing result and the sample physical position storage table.
In one possible implementation, step S105 may specifically include a) to c):
a) and calling a sample physical position storage table, and respectively determining the storage disc numbers of the r samples in the medical refrigerator system according to the sequencing result and the sample physical position storage table to form a storage disc number set W of the neighbor samples of the samples to be stored.
b) Respectively calculating the matching degree of the sample to be stored and each storage disk number in the storage disk number set W according to a fifth preset formula, wherein the fifth preset formula is shown as a formula (6):
formula (6)
Wherein Z represents one disk number in the set W of disk numbers,a(miz) represents sample miThe weight on the storage disc number Z.
c) And determining the storage disc number with the highest matching degree as the storage disc number corresponding to the position to be stored of the sample to be stored.
It should be noted that, the foregoing is only an exemplary embodiment of one preferred implementation manner of step S105, and of course, there may be other possible implementation manners of step S105, and the embodiment of the present invention is not limited to this.
And S106, storing the sample to be stored to a position to be stored.
Specifically, after the position of waiting to save the sample is confirmed, can be with waiting to save the sample input cabinet body through the tray window that pops out on the medical freezer system cabinet door, and then will wait to save the sample and save to the assigned position through the automatic storage device of medical freezer system, for example can be through the arm with waiting to save the sample and save to the assigned position.
Furthermore, for the stored samples, the cluster analysis can be performed regularly by combining the sample association degree matrix, and the storage position is further optimized by adjusting the internal position. That is, as shown in fig. 3, the method for storing a sample in a medical refrigerator system according to an embodiment of the present invention may further include:
and S107, in a preset time, carrying out clustering analysis on n samples stored in the medical refrigerator system according to the sample association degree to obtain a clustering result.
Specifically, the preset time in the embodiment of the present invention may be a non-user use time, for example, a certain fixed time period every morning, which is not specifically limited in the embodiment of the present invention.
Specifically, when n samples stored in the medical freezer system are subjected to cluster analysis according to the sample correlation degree, the existing Canopy clustering algorithm may be adopted, or other algorithms may be adopted, and the embodiment of the present invention does not specifically limit the clustering algorithm.
Illustratively, an example of Canopy cluster analysis of n samples stored in a medical cooler system based on sample relevancy is provided as follows:
for example, first, the sample a is determined as the first initial center point according to the sample extraction frequency, and two sample correlation threshold values t1, t2 are set, where t1 > t 2.
Secondly, traversing each storage sample, if the sample correlation degree of the sample B and the sample A is greater than t1, regarding the sample B and the sample A as the same kind, and deleting the record of the sample B; temporarily retaining the record of sample B if the sample association of sample B with sample a is between t1 and t 2; if the sample correlation degree of the sample B and the sample A is smaller than t2, regarding the sample B and the sample A as non-homogeneous, and regarding the sample B as a sample to be selected at the next central point, repeating the above calculation process to finally obtain N central points, wherein each central point corresponds to one sample, and a plurality of samples are still in the sample correlation degree threshold range, and the plurality of samples in the sample correlation degree threshold range can be considered as being classified into one class with the samples at the central point.
And S108, optimizing and adjusting the storage positions of the n samples stored in the medical refrigerator system according to the clustering result, and updating the sample physical position storage table according to the optimization and adjustment result.
For example, according to the clustering result, the method for optimally adjusting the storage positions of n samples stored in the medical freezer system may be as follows:
and mapping the corresponding spatial position distribution/layout according to the clustering result to obtain an optimized storage position relationship, and further readjusting the position of the sample through an automatic storage device of the medical freezer system, for example, readjusting the position of the sample through a mechanical arm.
The clustering result is obtained after n samples stored in the medical refrigerator system are clustered and analyzed according to the sample association degree, and the optimization adjustment result is obtained after the storage positions of the n samples stored in the medical refrigerator system are optimized and adjusted according to the clustering result, so that the sample physical position storage table is updated according to the optimization adjustment result, the physical position storage table can correspond to the storage positions of the optimized samples, and further, when the sample physical position storage table is called according to the sample association degree of the samples to be stored and each sample in the n samples stored in the medical refrigerator system, and the positions of the samples to be stored are determined, a more reasonable storage position result can be obtained.
Based on the sample storage method in the medical refrigerator system provided by the embodiment of the invention, after the sample identification and the sample attribute of the sample to be stored are obtained, the sample to be stored and each m sample in n samples stored in the medical refrigerator system can be determined according to the sample attributeiThe correlation degree of the attributes of the sample to be stored and the stored sample is obtained, for example, the sample to be stored is preferably placed at a position adjacent to the sample with the larger correlation degree of the attributes, and the sample to be stored and each sample m of the n samples stored in the medical freezer system are determined according to the sample identificationiThe degree of correlation of the user behavior is obtained, and the data of the influence degree of the user on the placement of the sample to be stored, such as the sample to be stored and the medical refrigerator system are frequently placed by the userThe stored samples A are extracted together, and the samples to be stored are preferably placed at the adjacent positions of the samples A.
Determining m samples of the samples to be stored and each sample m of n samples stored in the medical refrigerator system according to the attribute relevance and the user behavior relevance by combining a preset relevance self-adaptive factoriThe degree of sample correlation of (a). Wherein the attribute correlation degree data and the user behavior correlation degree data determine each sample m in the samples to be stored and the n samples stored in the medical refrigerator systemiThe degree of influence corresponding to each adaptive factor is generated in the sample correlation degree (2). By using the sample relevance calculating method, when the sample to be stored is stored, the position to be stored of the sample to be stored is determined by the attribute relevance between the sample to be stored and the stored sample and the user behavior relevance, and compared with the technical scheme that the sample storage position is determined randomly or singly according to the sample efficacy, the storage condition of the sample or the extraction frequency of a user and other factors in the prior art, the sample relevance calculating method provided by the exemplary technical scheme of the invention can comprehensively and objectively reflect the relevance degree or the relevance size between the sample to be stored and the stored sample, thereby providing more accurate basis for calculating and determining the position to be stored, ensuring more reasonable determination of the storage position of the sample and enhancing the regularity.
By mixing the sample to be stored with each sample m of n samples already stored in the medical freezer systemiThe sample association degree sorting is performed to obtain a sorting result, a sample physical position storage table is called, according to the sorting result and the sample physical position storage table, a neighboring position most relevant to the stored sample position is found out, and then the neighboring position can be determined as the to-be-stored position of the to-be-stored sample, for example, two or more samples with larger sample association degree data with the to-be-stored sample are selected, and the neighboring positions of the two or more samples are determined as the positions of the to-be-stored sample.
And finally, storing the sample to be stored to the position to be stored.
In summary, according to the above technical solutions, on one hand, the storage location is determined by using the calculation of the sample association degree, so that the randomness of the samples during storage is reduced, the regularity of the locations where the samples are stored is enhanced, the rationalization degree of the storage location of the samples inside the medical refrigerator is improved, and when a user stores a plurality of samples at one time, the storage operation time of the automation equipment is reduced, and the waiting time of the storage process is saved, because the samples with the large association degree are already distributed at the adjacent locations, the medical refrigerator storage device can store a plurality of samples at the adjacent locations at one time, or even if one sample is stored at each time, the operation route is relatively simple, so that the operation time is reduced, and the storage efficiency is improved; on the other hand, when a user extracts a plurality of samples at one time, the regular storage positions of the samples are enhanced and are usually placed at adjacent positions, so that the extraction operation time of the automatic equipment is also reduced, the waiting time of the extraction process is saved, the efficiency of the user is improved when the user stores or extracts the samples, and the competitiveness of the medical refrigerator product applying the storage method is improved.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A method of storing samples in a medical cooler system, the method comprising:
acquiring a sample identifier and a sample attribute of a sample to be stored;
respectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system according to the sample attribute of the sample to be storediThe attribute association degree of (2); and respectively determining the sample to be stored and each sample in the n samples stored in the medical refrigerator system according to the sample identification of the sample to be storedArticle miIn the user behavior correlation degree of (1), wherein miIdentifying a sample of n samples stored in the medical refrigerator system, wherein i is more than or equal to 1 and less than or equal to n;
according to the samples to be stored and each sample m in n samples stored in the medical refrigerator systemiRespectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system by combining a preset relevance adaptive factoriThe degree of sample correlation of (a);
the sample to be stored and each sample m in n samples stored in the medical refrigerator system are storediThe sample relevance degree is sequenced to obtain a sequencing result; according to the samples to be stored and each sample m in n samples stored in the medical refrigerator systemiDetermining R samples with the highest correlation degree with the sample to be stored in the n samples to form a neighbor sample set R of the sample to be stored;
calling a sample physical position storage table, and determining a to-be-stored position of the to-be-stored sample according to the sequencing result and the sample physical position storage table;
calling the sample physical position storage table, and respectively determining the storage tray numbers of the r samples in the medical refrigerator system according to the sequencing result and the sample physical position storage table to form a storage tray number set W of neighbor samples of the samples to be stored;
according to a fifth preset formula, respectively calculating the matching degree between the sample to be stored and each storage disk number in the storage disk number set W, wherein the fifth preset formula comprises:
wherein Z represents a disk number in the set W of disk numbers, ScoreZ∈WIndicating the matching degree of the sample to be stored and the storage disk with the storage disk number Z in the storage disk number set W,a(miz) represents sample miWeight on storage disk number Z, sim (m)j,mi) Representing the sample m to be storedjWith sample miA degree of sample correlation therebetween;
determining the storage disc number with the highest matching degree as the storage disc number corresponding to the position to be stored of the sample to be stored;
and storing the sample to be stored to the position to be stored.
2. The method of claim 1, wherein the sample to be stored is m samples from each of the n samples stored in the medical cooler systemiRespectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator system by combining a preset relevance adaptive factoriThe sample correlation degree specifically includes:
according to the samples to be stored and each sample m in n samples stored in the medical refrigerator systemiRespectively determining the sample to be stored and each sample m of n samples stored in the medical refrigerator system by combining a fourth preset formulaiThe fourth preset formula comprises:
sim(mj,mi)＝β×simcontent(mj,mi)+(1-β)×simevent(mj,mi)，
wherein m isjSample identification, sim (m), representing the sample to be storedj,mi) Represents a sample mjWith sample miDegree of sample correlation between, simcontent (m)j,mi) Represents a sample mjWith sample miDegree of attribute association between, simevent (m)j,mi) Represents a sample mjWith sample miThe degree of correlation of the user behavior between them, β denotes the degree of correlation adaptive factor,t denotes a user fetch transaction set, TkRepresents the kth fetch transaction in the set T, Tk＝{m1,m2,...,mq}，mqSample identification, a (t), representing the sample taken in the kth extraction transactionk,mj) Represents a sample mjThe weight in the transaction is fetched the k-th time,
3. the method of claim 1 or 2, wherein the m samples of the to-be-stored sample and the n samples stored in the medical cooler system are determined according to sample attributes of the to-be-stored sample respectivelyiThe attribute association degree of (2) includes:
according to the sample attribute of the sample to be stored, establishing an attribute feature vector of the sample to be stored asWherein f ismjA feature vector representing the attributes of the sample to be stored,representing the weight of the kth sample attribute to the sample to be stored;
according to the attribute feature vector of the sample to be stored, each sample m in n samples stored in the medical refrigerator systemiRespectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator systemiThe second preset formula includes:
wherein m isjA sample identification representing the sample to be stored,represents the k-th type sample attribute to the sample mjThe weight of (a) is determined,represents the k-th type sample attribute to the sample miWeight of (c), simcontent (m)j,mi) Represents a sample mjWith sample miThe degree of attribute association between.
4. The method according to claim 3, wherein the establishing of the attribute feature vector of the sample to be stored according to the sample attribute of the sample to be stored isThe method specifically comprises the following steps:
traversing a preset sample attribute set P, and establishing an attribute feature vector of the sample to be stored asWherein, the length of P is s, and the first preset formula is:pkrepresenting the kth class of sample attributes.
5. The method of claim 3, wherein the separately determining the sample to be stored and each of the n samples stored in the medical cooler system is performed after the establishing of the attribute feature vector of the sample to be storediBefore the attribute association degree, the method further comprises:
will f ismjReducing the dimension to l dimension to obtain the attribute feature vector after dimension reductionl＜s；
According to the attribute feature vector of the sample to be stored, each sample m in n samples stored in the medical refrigerator systemiRespectively determining the sample to be stored and each sample m in n samples stored in the medical refrigerator systemiThe attribute association degree specifically includes:
according to the attribute feature vector of the sample to be stored after dimensionality reduction, each sample m in n samples stored in the medical refrigerator systemiThe attribute feature vector after dimension reduction and the second preset formula after correction respectively determine the sample to be stored and each sample m in n samples stored in the medical refrigerator systemiThe modified second preset formula comprises:
6. the method of any of claims 1-2, wherein m is determined for each of the sample to be stored and n samples stored in the medical cooler system based on the sample identifier of the sample to be storediThe user behavior association degree of (2) comprises:
respectively determining the samples to be stored and each sample m in n samples stored in the medical refrigerator system according to the sample identifications of the samples to be stored and by combining a user behavior correlation matrixiThe user behavior association degree matrix is determined by combining a third preset formula according to the user extraction transaction set T, and the third preset formula includes:
wherein m isjSample identification, simevent (m), representing the sample to be storedj,mi) Represents a sample mjWith sample miDegree of association between user behaviors, a (t)k,mv) Represents a sample mvThe weight in the transaction is fetched the k-th time,v＝i,j，tkrepresents the kth fetch transaction in the set T, Tk＝{m1,m2,...,mq}，mqA sample identification representing the sample taken in the kth extraction transaction.
7. The method according to any one of claims 1-2, further comprising:
in a preset time, carrying out clustering analysis on n samples stored in the medical refrigerator system according to the sample association degree to obtain a clustering result;
and optimizing and adjusting the storage positions of n samples stored in the medical refrigerator system according to the clustering result, and updating the sample physical position storage table according to the optimization and adjustment result.
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