CN109493959A - Hospital's scheduling method and device - Google Patents
Hospital's scheduling method and device Download PDFInfo
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Abstract
The present invention provides a kind of hospital's scheduling method and device, by obtaining patient satisfaction model and medical teacher's total cost model, and the initial population parameter of particle swarm algorithm is obtained according to default constraint condition, then according to the initial population parameter, the satisfaction of the patient satisfaction model output and the totle drilling cost of medical teacher's total cost model output, obtain the globally optimal solution of the particle swarm algorithm, and using the globally optimal solution as result of arranging an order according to class and grade, it realizes and arrangement of arranging an order according to class and grade is carried out according to the total time cost of patient satisfaction and medical teacher, so that finally obtained scheme of arranging an order according to class and grade more is rationalized, realize the double goal for promoting patient satisfaction and reducing service cost.
Description
Technical field
The present invention relates to field of artificial intelligence more particularly to a kind of hospital's scheduling methods and device.
Background technique
With the rise of convalescent home in China, rehabilitation is just towards specialized, integrated trend development.
For medical procedure, rehabilitation process is often veryer long, more than the training of body and limbs, also to there is spirit
With the rehabilitation of psychology, patient can comprehensive rehabilitation, therefore, rehabilitation needs very powerful rehabilitation team to cooperate with to complete.
The type of medical teacher in rehabilitation team is more, comprising: physics medical treatment teacher, operation medical treatment teacher, speech medical treatment teacher, fortune
Dynamic medical treatment teacher, the medical teacher of psychology etc., the medical teacher of each of them type can be divided into different brackets again.Currently, convalescent home
Daily operation arrange mainly to use artificial experience method, i.e., go to arrange the sequence of arranging an order according to class and grade of each Medical Treatment Room using manual dispatching method.
However, above-mentioned artificial scheduling method is relatively fixed, cause patient satisfaction lower or medical teacher's service time
The problem of higher cost.
Summary of the invention
The present invention provides a kind of hospital's scheduling method and device, improves patient satisfaction to realize and reduces medical teacher's service
The double goal of time cost.
In a first aspect, hospital's scheduling method provided by the invention, comprising:
Obtain patient satisfaction model and medical teacher's total cost model；The patient satisfaction model include medical teacher whether
It arranges an order according to class and grade parameter, medical treatment teacher's total cost model includes the parameter whether each medical teacher arranges an order according to class and grade and each medical teacher
It arranges an order according to class and grade duration parameters；
The initial population parameter of particle swarm algorithm is obtained according to default constraint condition, the initial population parameter includes to excellent
Change the position and speed of particle, the position of the particle to be optimized is the duration parameters of arranging an order according to class and grade of each medical teacher, the speed
For duration variable quantity of arranging an order according to class and grade；
The satisfaction exported according to the initial population parameter, the patient satisfaction model and medical teacher's assembly
The totle drilling cost of this model output, obtains the globally optimal solution of the particle swarm algorithm, and using the globally optimal solution as arranging an order according to class and grade
As a result.
Optionally, the default constraint condition includes at least one of following:
Error between the prediction total duration and destination service total duration of the output of patient demand prediction model is less than default miss
Difference, wherein the destination service total duration is the parameter whether arranged an order according to class and grade according to each medical teacher and each medical teacher
What duration parameters of arranging an order according to class and grade determined；
The destination service total duration is greater than or equal to doctor's advice and executes total duration；
The satisfaction of the patient satisfaction model output is greater than default satisfaction.
Optionally, before the basis presets the initial population parameter that constraint condition obtains particle swarm algorithm, the method
Further include:
The working hour of patient populations, doctor's advice quantity and type, the working hour of execution reservation doctor's advice, the existing doctor's advice of execution is input to
Patient demand prediction model；
Obtain the prediction total duration of patient demand prediction model output, wherein the patient demand prediction model is
Neural network model.
Optionally, the satisfaction exported according to the initial population parameter, the patient satisfaction model and institute
The totle drilling cost for stating medical teacher's total cost model output obtains the globally optimal solution of the particle swarm algorithm, and most by the overall situation
Excellent solution is as result of arranging an order according to class and grade, comprising:
According to the satisfaction of patient satisfaction model output, the totle drilling cost of medical teacher's total cost model output and
Penalty factor determines the fitness value of each particle；
Particle optimal location and global optimum position are determined according to the fitness value of each particle；
Judge whether the particle swarm algorithm reaches stopping criterion for iteration；
If so, using the global optimum position as the globally optimal solution；
If it is not, then according to the speed of the particle optimal location of each particle, each particle of global optimum's location updating
The position and；
The step of particle optimal location and global optimum position are determined according to the fitness value of each particle is repeated,
Until the particle swarm algorithm reaches stopping criterion for iteration.
Optionally, each particle of particle optimal location, global optimum's location updating according to each particle
Speed and position, comprising:
Obtain the Species structure factor；
It is adjusted, is obtained according to the Species structure factor pair inertia weight, the first accelerator coefficient, the second accelerator coefficient
Inertia weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted adjusted；
According to the particle optimal location of each particle, global optimum position and the inertia weight adjusted, tune
The first accelerator coefficient and the second accelerator coefficient adjusted after whole update speed and the position of each particle.
Optionally, the acquisition Species structure factor, comprising:
The fitness value of each particle is ranked up, chooses fitness value in the particle in middle position as median
Particle；
According to the fitness value of the fitness value of each particle and the middle position particle, the Species structure factor is obtained.
Optionally, described according to the particle optimal location of each particle, global optimum position and described adjusted
Inertia weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted, update each particle speed and
Before position, the method also includes:
When the Species structure factor is greater than setting factor beforehand, Gaussian mutation processing is carried out to the position of each particle,
Obtain Gaussian mutation treated particle optimal location and global optimum position；
It is described to be weighed according to the particle optimal location of each particle, global optimum position and the inertia adjusted
Weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted update speed and the position of each particle, packet
It includes:
According to Gaussian mutation treated particle optimal location, global optimum position and the inertia power adjusted
Weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted update speed and the position of each particle.
Second aspect, hospital provided by the invention arrange an order according to class and grade device, comprising:
Module is obtained, for obtaining patient satisfaction model and medical teacher's total cost model；The patient satisfaction model
Whether arrange an order according to class and grade parameter including medical teacher, medical treatment teacher's total cost model include the parameter whether each medical teacher arranges an order according to class and grade and
The duration parameters of arranging an order according to class and grade of each medical teacher；
Determining module, for according to preset constraint condition obtain particle swarm algorithm initial population parameter, described initial kind
Swarm parameter includes the position and speed of particle to be optimized, and the position of the particle to be optimized is the duration of arranging an order according to class and grade of each medical teacher
Parameter, the speed are duration variable quantity of arranging an order according to class and grade；
Processing module, for according to the initial population parameter, the patient satisfaction model output satisfaction and
The totle drilling cost of medical treatment teacher's total cost model output, obtains the globally optimal solution of the particle swarm algorithm, and by the overall situation
Optimal solution is as result of arranging an order according to class and grade.
Optionally, the default constraint condition includes at least one of following:
Error between the prediction total duration and destination service total duration of the output of patient demand prediction model is less than default miss
Difference, wherein the destination service total duration is the parameter whether arranged an order according to class and grade according to each medical teacher and each medical teacher
What duration parameters of arranging an order according to class and grade determined；
The destination service total duration is greater than or equal to doctor's advice and executes total duration；
The satisfaction of the patient satisfaction model output is greater than default satisfaction.
Optionally, described device further include: prediction module；
The prediction module, for patient populations, doctor's advice quantity and type, the working hour of execution reservation doctor's advice, execution is existing
There is the working hour of doctor's advice to be input to patient demand prediction model；The prediction total duration of the patient demand prediction model output is obtained,
Wherein, the patient demand prediction model is neural network model.
Optionally, the processing module, specifically for exported according to the patient satisfaction model satisfaction, the doctor
The totle drilling cost and penalty factor for the treatment of the output of teacher's total cost model determine the fitness value of each particle；
Particle optimal location and global optimum position are determined according to the fitness value of each particle；
Judge whether the particle swarm algorithm reaches stopping criterion for iteration；
If so, using the global optimum position as the globally optimal solution；
If it is not, then according to the speed of the particle optimal location of each particle, each particle of global optimum's location updating
The position and；
The step of particle optimal location and global optimum position are determined according to the fitness value of each particle is repeated,
Until the particle swarm algorithm reaches stopping criterion for iteration.
Optionally, the processing module is specifically used for obtaining the Species structure factor；
It is adjusted, is obtained according to the Species structure factor pair inertia weight, the first accelerator coefficient, the second accelerator coefficient
Inertia weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted adjusted；
According to the particle optimal location of each particle, global optimum position and the inertia weight adjusted, tune
The first accelerator coefficient and the second accelerator coefficient adjusted after whole update speed and the position of each particle.
Optionally, the processing module is ranked up specifically for the fitness value to each particle, chooses fitness
It is worth the particle in middle position as median particle；
According to the fitness value of the fitness value of each particle and the middle position particle, the Species structure factor is obtained.
Optionally, the processing module is specifically used for when the Species structure factor is greater than setting factor beforehand, to each described
The particle optimal location of particle, global optimum position carry out Gaussian mutation processing, obtain Gaussian mutation treated that particle is optimal
Position and global optimum position；
According to Gaussian mutation treated particle optimal location, global optimum position and the inertia power adjusted
Weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted update speed and the position of each particle.
The third aspect, electronic equipment provided by the invention, comprising:
Memory；
Processor；And
Computer program；
Wherein, the computer program stores in the memory, and is configured as being executed by the processor with reality
Now such as the described in any item methods of first aspect.
Fourth aspect, computer readable storage medium provided by the invention are stored thereon with computer program, the calculating
Machine program is executed by processor to realize such as the described in any item methods of first aspect.
Hospital's scheduling method and device provided by the invention, by obtaining patient satisfaction model and medical teacher's totle drilling cost mould
Type, and according to the initial population parameter of default constraint condition acquisition particle swarm algorithm, then according to the initial population parameter, institute
The satisfaction of patient satisfaction model output and the totle drilling cost of medical teacher's total cost model output are stated, the particle is obtained
The globally optimal solution of group's algorithm, and using the globally optimal solution as arranging an order according to class and grade as a result, realizing according to patient satisfaction and medical treatment
The total time cost of teacher carries out arrangement of arranging an order according to class and grade, so that finally obtained scheme of arranging an order according to class and grade more is rationalized, it is full to realize promotion patient
Meaning degree and the double goal reduced service cost.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without any creative labor, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the flow chart of hospital's scheduling method embodiment one provided by the invention；
Fig. 2 is the schematic diagram of Demand Forecast Model in the embodiment of the present invention one；
Fig. 3 A is the flow chart one of hospital's scheduling method embodiment two provided by the invention；
Fig. 3 B is the flowchart 2 of hospital's scheduling method embodiment two provided by the invention；
Fig. 3 C is the flow chart being updated in the embodiment of the present invention two to the speed of particle and position；
Fig. 4 is that hospital provided by the invention arranges an order according to class and grade the structural schematic diagram of Installation practice；
Fig. 5 is the structural schematic diagram of electronic equipment embodiment provided by the invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
The cooperation of multidisciplinary, polygonal color is needed during rehabilitation.Rehabilitation doctor is seen and treated patients patient and after assigning doctor's advice, is suffered from
Person's doctor's advice information there may be different types of rehabilitation project, such as rehabilitation project type include: physiotherapy,
Psychotherapy, speech therapy etc..The embodiment of the present invention does not do the type of rehabilitation item types and specific division mode
It limits.In turn, for different patients, required medical Shi Keneng is identical, it is also possible to different.Such as: some patients need
Speech medical treatment teacher and psychological medical treatment Shi Jinhang rehabilitation, some patients need physics medical treatment teacher and massage Shi Jinhang rehabilitation
Treatment, also some patients need physics medical treatment teacher and psychological medical treatment Shi Jinhang rehabilitation etc..
Therefore, the information of arranging an order according to class and grade of each medical teacher is complicated and changeable, and manually right using manual dispatching method at present
The time of each medical treatment teacher carries out processing of arranging an order according to class and grade, it is easy to it causes to waste medical resource due to not arranging an order according to class and grade to some medical treatment Shi Jinhang,
Cause patient that cannot reasonably arrange to needing to take into account multiple patients but also some medical teachers are busy, to be unfavorable for suffering from
The rehabilitation process of person, and process of manually arranging an order according to class and grade is complicated, cumbersome, expends more man power and materials.
Therefore, the present invention provides a kind of hospital's scheduling method and device, according to the quantity of patient, doctor's advice quantity and doctor's advice class
Type targetedly arranges an order according to class and grade to the demand for services amount of prediction using intelligent optimization algorithm to predict demand for services amount, so that
There are enough medical teachers to provide service when demand for services amount is big, improve patient satisfaction, in volume of services hour, reduces medical treatment
Teacher's arranges an order according to class and grade, and reduces hospital services cost.
Fig. 1 is the flow chart of hospital's scheduling method embodiment one provided by the invention, as shown in Figure 1, the side of the present embodiment
Method may include:
S11, patient satisfaction model and medical teacher's total cost model are obtained；The patient satisfaction model includes medical teacher
Whether arrange an order according to class and grade parameter, medical treatment teacher's total cost model includes the parameter and each medical treatment whether each medical teacher arranges an order according to class and grade
The duration parameters of arranging an order according to class and grade of teacher.
Wherein, according to the difference of medical mode, medical teacher is usually divided into multiple types, including but not limited to physics medical treatment
Teacher, speech medical treatment teacher, moves medical teacher, the medical teacher of psychology etc. at operation medical treatment teacher, and the medical teacher of each of them type again can be with
It is divided into different brackets, such as: primary, intermediate, advanced, expert's grade etc..
Specifically, patient satisfaction model is used to indicate patient for the satisfaction of all medical teachers.For each type
The medical teacher of type can obtain patient for the satisfaction of the different grades of medical teacher of the type according to historical data.It is a kind of
In optional embodiment, patient satisfaction model can be obtained according to formula (1).
Wherein,It is patient for the satisfaction of the medical teacher of the i-th type, εinIt is patient for i-th the n-th grade of type
Medical teacher satisfaction, ainWhether the medical teacher for i-th the n-th grade of type arranges an order according to class and grade, QinFor the medical treatment of i-th the n-th grade of type
The satisfaction evaluation constant of teacher, m are medical teacher's type sum.
Medical teacher's total cost model is used to indicate the total time cost of all types patient demand medical treatment teacher, it is assumed that shared l
The patient of a type, then can according to the 1st, 2 ..., the sum of the time cost of patient demand medical treatment teacher of l type, such as formula (2)
(3) shown in:
J2(t)=J21(t)+...+J2j(t)+...+J2l(t) (2)
Wherein, J2jFor the time cost of patient demand medical treatment teacher of jth type, ηjiFor j-th type patient whether demand
I-th type rehabilitation medical teacher can indicate using the switch function as described in formula (4),For n in the i-th typeiGrade health
The time cost of multiple medical treatment teacher,For n in the i-th typeiWhether the medical teacher of grade arranges an order according to class and grade；For n in the i-th typeiGrade
The duration of arranging an order according to class and grade of medical teacher.
S12, the initial population parameter that particle swarm algorithm is obtained according to default constraint condition, the initial population parameter include
The position and speed of particle to be optimized, the position of the particle to be optimized is the duration parameters of arranging an order according to class and grade of each medical teacher, described
Speed is duration variable quantity of arranging an order according to class and grade.
In this step, on the basis of above-mentioned patient satisfaction model and medical teacher's total cost model, calculated using population
Method arranges an order according to class and grade parameter (i.e. in above-mentioned modelWith) optimize.Specifically, obtaining the initial of particle swarm algorithm first
Parameter and population, it is assumed that number of particles N, the position X of the particle to be optimized in initial population parameteri=(xi1,xi2,…,xin)
It indicates, the speed V of particle to be optimizedi=(vi1,vi2,…,vin) indicate.Wherein, the position of particle to be optimized corresponds to
The duration parameters of arranging an order according to class and grade of each medical treatment teacher, i.e.,The speed of particle to be optimized corresponds to the variable quantity for duration of arranging an order according to class and grade.
It should be understood that initial population parameter can be randomly selected in ordinary particle group's algorithm.In the present embodiment, according to
Default constraint condition obtains initial population parameter, wherein default constraint condition can be select according to the actual situation it is any about
Beam condition.Assuming that constraint condition is that patient satisfaction is greater than preset value, then optimized according to the initial population parameter iteration
Globally optimal solution also can satisfy the default constraint condition.Therefore, by reasonably selecting default constraint condition, it is ensured that this
The reasonability and optimality for scheme that embodiment obtained arrange an order according to class and grade.
Optionally, the default constraint condition includes at least one of following:
(A) error between the prediction total duration and destination service total duration of the output of patient demand prediction model is less than default
Error, wherein the destination service total duration is the parameter whether arranged an order according to class and grade according to each medical teacher and each medical teacher
Arrange an order according to class and grade duration parameters determine.
Specifically, using prediction total duration and the error between destination service total time as constraint condition, it is a kind of optional
In embodiment, constraint condition A can be described using formula (5).
Wherein,To predict total duration, y (t) is destination service total duration, and e is error constant 0.01.
It should be understood that destination service total duration can be according to the parameter and each medical teacher whether each medical teacher arranges an order according to class and grade
Duration parameters of arranging an order according to class and grade determine, as shown in formula (6), whereinFor n in the i-th typeiThe duration of arranging an order according to class and grade of the medical teacher of grade, if
Medical treatment teacher does not arrange an order according to class and grade, then
In addition, prediction total duration can establish patient demand prediction model according to artificial intelligence approach and be predicted.Fig. 2 is
The schematic diagram of Demand Forecast Model in the embodiment of the present invention one, as shown in Fig. 2, by person's quantity, doctor's advice quantity and type, executing in advance
If the working hour of doctor's advice, the working hour for executing existing doctor's advice are input to patient demand prediction model；Obtain the patient demand prediction mould
The prediction total duration of type output.
It should be noted that the patient demand prediction model can be established using any artificial intelligence approach.It is optional
, in the present embodiment with radial basis function (Radial Basis Function, RBF) neural network algorithm for, establish demand
The total duration of prediction model prediction patient demand medical treatment teacher, that is to say, that the patient demand prediction model is neural network mould
Type.Specific prediction process is as follows: historical data is inputted as unit of consecutive days, specifically include patient populations, doctor's advice quantity and
It type, the working hour for executing reservation doctor's advice, the working hour for executing existing doctor's advice, exports to predict total duration, threshold is arranged to prediction error
Value determines network weight when meeting and predicting error condition, completes the optimization to RBF neural, at the same export prediction it is total when
It is long.
(B) the destination service total duration is greater than or equal to doctor's advice execution total duration.
(C) satisfaction of the patient satisfaction model output is greater than default satisfaction.
Wherein, default satisfaction can be configured according to actual needs, and optionally, presetting satisfaction is 90%.
In the present embodiment, above three constraint condition is determined, using patient satisfaction and the time cost of medical teacher as two
A optimization aim, can eliminate existing promotion satisfaction will lead to the mistaken ideas of cost of serving raising, so that finally obtained
Scheme of arranging an order according to class and grade more is rationalized, and realizes the double goal for promoting patient satisfaction and reducing service cost.
S13: the satisfaction exported according to the initial population parameter, the patient satisfaction model and the medical teacher
The totle drilling cost of total cost model output, obtains the globally optimal solution of the particle swarm algorithm, and using the globally optimal solution as
It arranges an order according to class and grade result.
It after determining initial population parameter, can be iterated using particle swarm algorithm, obtain globally optimal solution, and will be global
Optimal solution is as result of arranging an order according to class and grade.Existing particle swarm algorithm can be used using the process that example swarm optimization finds globally optimal solution
It realizes, this embodiment is not repeated.
It is exemplified below, it is assumed that the medical teacher of certain convalescent home is divided into 5 seed types, and the medical teacher of each type is divided into 4
A grade shares 20 medical teachers if a grade is calculated by 1 medical teacher.Assuming that it is each medical treatment teacher's operating time with
Hour it is unit, shares 1-8 hours, i.e. 8 kinds of situations, then one share 8 when being arranged an order according to class and grade20Plant scheme of arranging an order according to class and grade.Pass through this
The method of embodiment can choose out can meet patient satisfaction height two mesh low with the time cost of medical teacher's service simultaneously
Target is arranged an order according to class and grade scheme.
Specifically, initialization scheme can be arranged as follows: assuming that the work hour learnt after prediction certain day is 100
Hour, then 20 medical teachers are averagely given, the service duration of each medical treatment teacher is exactly 5 hours.Then, judge the initially side of arranging an order according to class and grade
Whether case meets 3 above-mentioned constraint conditions, if satisfied, then by the duration point of arranging an order according to class and grade of the corresponding 20 medical teachers of the initial scheme
Position not as 20 particles to be optimized, to obtain the X in initial population parameteri=(xi1,xi2,…,xin)；It is again each
An iteration step length, that is, duration of arranging an order according to class and grade variable quantity, as the speed of each particle to be optimized, to obtain initial is respectively set in particle
V in parameter and populationi=(vi1,vi2,…,vin).After determining initial population parameter, can using existing particle swarm algorithm into
Row iteration obtains globally optimal solution, and using globally optimal solution as result of arranging an order according to class and grade.
In the present embodiment, by obtaining patient satisfaction model and medical teacher's total cost model, and according to default constraint item
Part obtains the initial population parameter of particle swarm algorithm, then defeated according to the initial population parameter, the patient satisfaction model
The totle drilling cost of satisfaction and medical teacher's total cost model output out, obtains the global optimum of the particle swarm algorithm
Solution, and using the globally optimal solution as arrange an order according to class and grade as a result, realize according to patient satisfaction and medical treatment teacher total time cost into
Row is arranged an order according to class and grade arrangements so that finally obtained scheme of arranging an order according to class and grade more is rationalized, realize promoted patient satisfaction and reducing service at
This double goal.
Fig. 3 A is the flow chart one of hospital's scheduling method embodiment two provided by the invention, and Fig. 3 B is doctor provided by the invention
The flowchart 2 of institute's scheduling method embodiment two, on the basis of the above embodiments, the present embodiment use a kind of improved particle
Group's algorithm, is iterated optimization to the position and speed of each particle.
As shown in figs.3 a and 3b, the method for the present embodiment may include:
S30: according to default constraint condition, the initial position and speed of each particle are determined.
S31: according to the satisfaction of patient satisfaction model output, the assembly of medical teacher's total cost model output
This determines the fitness value of each particle with penalty factor.
S32: each particle optimal location and global optimum position are determined according to the fitness value of each particle.Wherein, grain
Sub- optimal location refers to the personal best particle of each particle, and global optimum position refers to the global optimum position of all particles.
In a kind of optional embodiment, the fitness value of each particle is determined according to formula (7).
Wherein,It is patient in the t times iteration for the satisfaction of medical teacher,For patient couple in the t-1 times iteration
In the satisfaction of medical teacher, can be obtained according to formula (1)；Z is penalty factor, can carry out flexible setting according to the actual situation, main
It is used to compare the superiority and inferiority of current iteration and last iteration；For the total time cost of all types of patient demand medical treatment teacher,
It can be obtained according to formula (2).
In specific implementation, satisfaction and total time cost can be normalized first, then further according to formula
(7) fitness value of each particle is calculated.
In the present embodiment, using punishment thought, using patient satisfaction and medical teacher total time cost as evaluation adaptation
The standard of angle value punished if this fitness value is inferior to last time, so that it is determined that the optimal location of each particle and complete
Office's optimal location.
S33: judging whether the particle swarm algorithm reaches stopping criterion for iteration, if so, S34 is executed, if it is not, then executing
S35。
Optionally, stopping criterion for iteration can be any of following:
(A) the number of iterations is greater than maximum number of iterations, i.e. t > Tmax, wherein TmaxFor preset maximum number of iterations.
(B) accuracy value of fitness value is less than preset precision threshold, as shown in formula (8), wherein JtFor the t times iteration
Fitness value, Jt-1For the fitness value of the t-1 times iteration, λpsoFor preset precision threshold, can carry out according to the actual situation
Setting.
S34: using the global optimum position as the globally optimal solution.
If any of above-mentioned two stopping criterion for iteration meets, stop iteration, by the global optimum of current iteration
It is arranged an order according to class and grade as globally optimal solution as a result, so that it is determined that going out the following one day or one week medical teacher to arrange an order according to class and grade and every position
The duration of arranging an order according to class and grade of a medical treatment teacher.
S35: according to the particle optimal location of each particle, the speed of each particle of global optimum's location updating and position
It sets, returns and execute S31 and S32, until particle swarm algorithm reaches stopping criterion for iteration.
It should be understood that when being unsatisfactory for any of the above-described a stopping criterion for iteration, according to the particle optimal location of each particle
With global optimum position, the speed and position of each particle are updated, then returns and executes S31 and S32, until population is calculated
Method reaches stopping criterion for iteration.Wherein, the present embodiment is easily trapped into local optimum to solve particle swarm algorithm in the prior art
The not high problem with search precision, the method that the speed and position for providing a kind of pair of particle are updated.Fig. 3 C is the present invention
The flow chart being updated in embodiment two to the speed of particle and position specifically comprises the following steps: as shown in Figure 3 C
S351: being ranked up the fitness value of each particle, chooses fitness value in the particle conduct in middle position
Median particle.
S352: according to the fitness value of the fitness value of each particle and the middle position particle, obtain Species structure because
Son.
Specifically, the Species structure factor can be obtained according to formula (9):
Wherein, utFor the corresponding Species structure factor of the t times iteration, k is Dynamic gene, and N is the number of particle, fi tIt is group
In body i-th of particle the t times iteration fitness value,It is fitness value of the median particle in the t times iteration, fmaxFor
Maximum value in the fitness value of all particles, fminFor the minimum value in the fitness value of all particles.
S353: it is adjusted according to the Species structure factor pair inertia weight, the first accelerator coefficient, the second accelerator coefficient
It is whole, inertia weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted after being adjusted.
Specifically, can be adjusted according to formula (10) to inertia weight, the first accelerator coefficient, the second accelerator coefficient.
ω=ωmax-ut×(ωmax-ωmin)
c1=c1max-ut×(c1max-c1min)
c2=c2min+ut×(c2max-c2min) (10)
Wherein, ωmaxAnd ωminRespectively indicate the maximum value and minimum value of inertia weight ω, c1max、c1minRespectively indicate
One accelerator coefficient c1Maximum and minimum value, c2max、c2minRespectively indicate the second accelerator coefficient c2Maximum and minimum value, ω, c1、
c2Inertia weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted respectively adjusted, wherein the
One accelerator coefficient is individual accelerator coefficient, and the second accelerator coefficient is social accelerator coefficient.
S354: it is weighed according to the particle optimal location of each particle, global optimum position and the inertia adjusted
Weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted update speed and the position of each particle.
Specifically, can be updated according to formula (11) and (12) to the speed of each particle and position.
V′i=ω Vi+c1rand(Yi-Xi)+c2rand(Yg-Xi) (11)
X′i=Xi+ηV′i (12)
Wherein, Xi、ViFor the position and speed of the particle i before adjustment, X 'i、V′iPosition and speed for particle i adjusted
Degree, rand are the random number in [0,1], YiFor individual extreme value, YgFor global extremum, η is the decimal between 0 to 1.
Optionally, as shown in Figure 3B, before the speed and position for updating each particle, can also include:
S355: judge whether the Species structure factor is greater than setting factor beforehand；
S356: when the Species structure factor is greater than setting factor beforehand, Gaussian mutation is carried out to the position of each particle
Processing obtains Gaussian mutation treated particle optimal location and global optimum position.
Specifically, setting factor beforehand can be selected according to the actual situation, in the present embodiment, setting factor beforehand be can be set to
0.75.Gaussian mutation treatment process can be carried out according to formula (13).
Wherein,For the position of each particle after variation, δ is the variance of the position of each particle, and x is the position of particle,
μ is the mean value of the position of each particle.
Correspondingly, according to Gaussian mutation treated particle optimal location, global optimum position and described adjusted
Inertia weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted, update each particle speed and
Position.
It in the present embodiment, is easily trapped into that office is excellent and the not high problem of search precision for particle swarm algorithm, provides one kind
Particle swarm algorithm is improved, is ranked up to obtain intermediate particle fitness value by the fitness value to each particle, using population
Distribution factor indicates the current distribution situation of population, and by the Species structure factor come dynamic, be adaptively adjusted inertia power
Weight, individual accelerator coefficient and social accelerator coefficient.At iteration initial stage, since population relatively disperses, distribution factor levels off to 0, at this time
According to inertia weight, the adjustment formula inertia weight of individual accelerator coefficient and social accelerator coefficient, individual accelerator coefficient compared with
Greatly, social accelerator coefficient is smaller, enhances algorithm ability of searching optimum；In the iteration later period, population is relatively concentrated, and distribution factor becomes larger,
Inertia weight and individual accelerator coefficient become smaller at this time, and social accelerator coefficient becomes larger, and enhances algorithm part exploring ability, improve algorithm
Search precision.Meanwhile to the Species structure factor be arranged a threshold value, when the Species structure factor be greater than the threshold value when, to population into
Row Gaussian mutation increases population diversity and algorithm is avoided to fall into local optimum.
Fig. 4 is that hospital provided by the invention arranges an order according to class and grade the structural schematic diagram of Installation practice, as shown in figure 4, the present embodiment
Hospital arranges an order according to class and grade device 400, may include: to obtain module 401, determining module 402 and processing module 403.
Wherein, module 401 is obtained, for obtaining patient satisfaction model and medical teacher's total cost model；The patient is full
Meaning degree model includes whether medical teacher arranges an order according to class and grade parameter, and medical treatment teacher's total cost model includes whether each medical teacher arranges an order according to class and grade
Parameter and the duration parameters of arranging an order according to class and grade of each medical teacher.
Determining module 402, it is described initial for obtaining the initial population parameter of particle swarm algorithm according to default constraint condition
Parameter and population includes the position and speed of particle to be optimized, and the position of the particle to be optimized is each medical when arranging an order according to class and grade of teacher
Long parameter, the speed are duration variable quantity of arranging an order according to class and grade.
Processing module 403, for according to the initial population parameter, the patient satisfaction model output satisfaction with
And the totle drilling cost of medical teacher's total cost model output, the globally optimal solution of the particle swarm algorithm is obtained, and will be described complete
Office's optimal solution is as result of arranging an order according to class and grade.
Optionally, the default constraint condition includes at least one of following:
Error between the prediction total duration and destination service total duration of the output of patient demand prediction model is less than default miss
Difference, wherein the destination service total duration is the parameter whether arranged an order according to class and grade according to each medical teacher and each medical teacher
What duration parameters of arranging an order according to class and grade determined；
The destination service total duration is greater than or equal to doctor's advice and executes total duration；
The satisfaction of the patient satisfaction model output is greater than default satisfaction.
Optionally, as shown in figure 4, the device of the present embodiment can also include: prediction module 404.
Prediction module 404, for patient populations, doctor's advice quantity and type, the working hour of execution reservation doctor's advice, execution is existing
The working hour of doctor's advice is input to patient demand prediction model；The prediction total duration of the patient demand prediction model output is obtained,
In, the patient demand prediction model is neural network model.
Optionally, processing module 403, specifically for exported according to the patient satisfaction model satisfaction, the doctor
The totle drilling cost and penalty factor for the treatment of the output of teacher's total cost model determine the fitness value of each particle；According to the adaptation of each particle
Angle value determines particle optimal location and global optimum position；Judge whether the particle swarm algorithm reaches stopping criterion for iteration；If
It is, then using the global optimum position as the globally optimal solution；If it is not, then according to the optimal position of particle of each particle
It sets, the speed of each particle of global optimum's location updating and position；It repeats true according to the fitness value of each particle
The step of determining particle optimal location and global optimum position, until the particle swarm algorithm reaches stopping criterion for iteration.
Optionally, processing module 403 are specifically used for obtaining the Species structure factor；It is used according to the Species structure factor pair
Property weight, the first accelerator coefficient, the second accelerator coefficient be adjusted, inertia weight after being adjusted, adjusted first plus
Fast coefficient and the second accelerator coefficient adjusted；According to the particle optimal location of each particle, global optimum position and
The inertia weight adjusted, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted update each described
The speed of particle and position.
Optionally, processing module 403 are ranked up specifically for the fitness value to each particle, choose fitness
It is worth the particle in middle position as median particle；According to the adaptation of the fitness value of each particle and the middle position particle
Angle value obtains the Species structure factor.
Optionally, processing module 403 are specifically used for when the Species structure factor is greater than setting factor beforehand, to each described
The particle optimal location of particle, global optimum position carry out Gaussian mutation processing, obtain Gaussian mutation treated that particle is optimal
Position and global optimum position；According to Gaussian mutation treated particle optimal location, global optimum position and the tune
Inertia weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted after whole update each particle
Speed and position.
The hospital of the present embodiment arranges an order according to class and grade device, can be used for executing the technical solution of any of the above-described embodiment of the method, realizes
Principle is similar with technical effect, and details are not described herein again.
Fig. 5 is the structural schematic diagram of electronic equipment embodiment provided by the invention, as shown in figure 5, the electronics of the present embodiment
Equipment 500 may include: memory 501, processor 502 and computer program, wherein the computer program is stored in
In memory 501, and it is configured as being executed by processor 502 to realize the technical solution such as above-mentioned either method embodiment,
The realization principle and technical effect are similar, and details are not described herein again.
The present invention also provides a kind of computer readable storage mediums, are stored thereon with computer program, the computer journey
Sequence is executed by processor the technical solution to realize any of the above-described embodiment of the method, and it is similar that the realization principle and technical effect are similar, this
Place repeats no more.
In several embodiments provided by the present invention, it should be understood that disclosed device and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of unit, only
A kind of logical function partition, there may be another division manner in actual implementation, for example, multiple units or components can combine or
Person is desirably integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Between coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or communication link of device or unit
It connects, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
The above-mentioned integrated unit being realized in the form of SFU software functional unit can store and computer-readable deposit at one
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) or processor (English: processor) execute this hair
The part steps of bright each embodiment the method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory
(English: Read-Only Memory, abbreviation: ROM), random access memory (English: Random Access Memory, letter
Claim: RAM), the various media that can store program code such as magnetic or disk.
In the embodiment of the above-mentioned network equipment or terminal device, it should be appreciated that processor can be central processing unit
(English: Central Processing Unit, referred to as: CPU), it can also be other general processors, digital signal processor
(English: Digital Signal Processor, abbreviation: DSP), specific integrated circuit (English: Application
Specific Integrated Circuit, referred to as: ASIC) etc..General processor can be microprocessor or the processor
It is also possible to any conventional processor etc..Hardware handles can be embodied directly in conjunction with the step of method disclosed in the present application
Device executes completion, or in processor hardware and software module combination execute completion.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above-mentioned each method embodiment can lead to
The relevant hardware of program instruction is crossed to complete.Program above-mentioned can be stored in a computer readable storage medium.The journey
When being executed, execution includes the steps that above-mentioned each method embodiment to sequence；And storage medium above-mentioned include: ROM, RAM, magnetic disk or
The various media that can store program code such as person's CD.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (10)
1. a kind of hospital's scheduling method characterized by comprising
Obtain patient satisfaction model and medical teacher's total cost model；The patient satisfaction model includes whether medical teacher arranges an order according to class and grade
Parameter, medical treatment teacher's total cost model include arranging an order according to class and grade for the parameter whether each medical teacher arranges an order according to class and grade and each medical teacher
Duration parameters；
The initial population parameter of particle swarm algorithm is obtained according to default constraint condition, the initial population parameter includes grain to be optimized
The position and speed of son, the position of the particle to be optimized are the duration parameters of arranging an order according to class and grade of each medical teacher, and the speed is row
Class's duration variable quantity；
The satisfaction exported according to the initial population parameter, the patient satisfaction model and medical teacher's totle drilling cost mould
The totle drilling cost of type output, obtains the globally optimal solution of the particle swarm algorithm, and using the globally optimal solution as result of arranging an order according to class and grade.
2. the method according to claim 1, wherein the default constraint condition includes at least one in following
It is a:
Error between the prediction total duration and destination service total duration of the output of patient demand prediction model is less than default error,
In, the destination service total duration is the parameter whether arranged an order according to class and grade according to each medical teacher and each medical when arranging an order according to class and grade of teacher
What long parameter determined；
The destination service total duration is greater than or equal to doctor's advice and executes total duration；
The satisfaction of the patient satisfaction model output is greater than default satisfaction.
3. according to the method described in claim 2, it is characterized in that, the basis, which presets constraint condition, obtains particle swarm algorithm
Before initial population parameter, the method also includes:
The working hour of patient populations, doctor's advice quantity and type, the working hour of execution reservation doctor's advice, the existing doctor's advice of execution is input to patient
Demand Forecast Model；
Obtain the prediction total duration of the patient demand prediction model output, wherein the patient demand prediction model is nerve
Network model.
4. method according to any one of claims 1 to 3, which is characterized in that described according to the initial population parameter, institute
The satisfaction of patient satisfaction model output and the totle drilling cost of medical teacher's total cost model output are stated, the particle is obtained
The globally optimal solution of group's algorithm, and using the globally optimal solution as result of arranging an order according to class and grade, comprising:
According to the satisfaction of patient satisfaction model output, the totle drilling cost and punishment of medical teacher's total cost model output
The factor determines the fitness value of each particle；
Particle optimal location and global optimum position are determined according to the fitness value of each particle；
Judge whether the particle swarm algorithm reaches stopping criterion for iteration；
If so, using the global optimum position as the globally optimal solution；
If it is not, then according to the particle optimal location of each particle, the speed of each particle of global optimum's location updating and position
It sets；
The step of particle optimal location and global optimum position are determined according to the fitness value of each particle is repeated, until
The particle swarm algorithm reaches stopping criterion for iteration.
5. according to the method described in claim 4, it is characterized in that, the particle optimal location according to each particle, complete
Office's optimal location updates speed and the position of each particle, comprising:
Obtain the Species structure factor；
It is adjusted, is adjusted according to the Species structure factor pair inertia weight, the first accelerator coefficient, the second accelerator coefficient
Inertia weight, the first accelerator coefficient adjusted and the second accelerator coefficient adjusted afterwards；
After the particle optimal location of each particle, global optimum position and the inertia weight adjusted, adjustment
The first accelerator coefficient and the second accelerator coefficient adjusted, update speed and the position of each particle.
6. according to the method described in claim 5, it is characterized in that, the acquisition Species structure factor, comprising:
The fitness value of each particle is ranked up, chooses fitness value in the particle in middle position as median grain
Son；
According to the fitness value of the fitness value of each particle and the middle position particle, the Species structure factor is obtained.
7. according to the method described in claim 5, it is characterized in that, the particle optimal location according to each particle, complete
Office's optimal location and the inertia weight adjusted, the first accelerator coefficient adjusted and adjusted second accelerate system
It counts, before the speed and the position that update each particle, the method also includes:
When the Species structure factor is greater than setting factor beforehand, Gaussian mutation processing is carried out to the position of each particle, is obtained
Gaussian mutation treated particle optimal location and global optimum position；
It is described according to the particle optimal location of each particle, global optimum position and the inertia weight adjusted, tune
The first accelerator coefficient and the second accelerator coefficient adjusted after whole update speed and the position of each particle, comprising:
According to Gaussian mutation treated particle optimal location, global optimum position and the inertia weight adjusted, tune
The first accelerator coefficient and the second accelerator coefficient adjusted after whole update speed and the position of each particle.
The device 8. a kind of hospital arranges an order according to class and grade characterized by comprising
Module is obtained, for obtaining patient satisfaction model and medical teacher's total cost model；The patient satisfaction model includes
Whether medical teacher arranges an order according to class and grade parameter, and medical treatment teacher's total cost model includes the parameter and each institute whether each medical teacher arranges an order according to class and grade
State the duration parameters of arranging an order according to class and grade of medical teacher；
Determining module, for according to the initial population parameter for presetting constraint condition acquisition particle swarm algorithm, the initial population ginseng
Number includes the position and speed of particle to be optimized, and the position of the particle to be optimized is the duration ginseng of arranging an order according to class and grade of each medical teacher
Number, the speed are duration variable quantity of arranging an order according to class and grade；
Processing module, for the satisfaction and described according to the initial population parameter, patient satisfaction model output
The totle drilling cost of medical teacher's total cost model output, obtains the globally optimal solution of the particle swarm algorithm, and by the global optimum
Solution is as result of arranging an order according to class and grade.
9. a kind of electronic equipment characterized by comprising
Memory；
Processor；And
Computer program；
Wherein, the computer program stores in the memory, and is configured as being executed by the processor to realize such as
The described in any item methods of claim 1-7.
10. a kind of computer readable storage medium, which is characterized in that be stored thereon with computer program, the computer program
It is executed by processor to realize the method according to claim 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695819A (en) * | 2020-06-16 | 2020-09-22 | 中国联合网络通信集团有限公司 | Method and device for scheduling seat personnel |
| CN111883241A (en) * | 2020-08-05 | 2020-11-03 | 四川大学华西医院 | Automatic scheduling method and system for PCI (peripheral component interconnect) operation of cardiac catheterization room nurses |
| CN113555096A (en) * | 2021-06-10 | 2021-10-26 | 合肥工业大学 | Operating room scheduling method and system considering doctor scheduling condition |
| CN116612870A (en) * | 2023-07-17 | 2023-08-18 | 山东圣剑医学研究有限公司 | General surgery patient data management method |
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2018
- 2018-11-08 CN CN201811326022.1A patent/CN109493959A/en active Pending
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695819A (en) * | 2020-06-16 | 2020-09-22 | 中国联合网络通信集团有限公司 | Method and device for scheduling seat personnel |
| CN111695819B (en) * | 2020-06-16 | 2023-06-02 | 中国联合网络通信集团有限公司 | Seat personnel scheduling method and device |
| CN111883241A (en) * | 2020-08-05 | 2020-11-03 | 四川大学华西医院 | Automatic scheduling method and system for PCI (peripheral component interconnect) operation of cardiac catheterization room nurses |
| CN111883241B (en) * | 2020-08-05 | 2023-07-21 | 四川大学华西医院 | Automatic scheduling method and scheduling system for PCI operation of nurses in cardiac catheter room |
| CN113555096A (en) * | 2021-06-10 | 2021-10-26 | 合肥工业大学 | Operating room scheduling method and system considering doctor scheduling condition |
| CN113555096B (en) * | 2021-06-10 | 2023-06-30 | 合肥工业大学 | Operating room scheduling method and system considering doctor scheduling condition |
| CN116612870A (en) * | 2023-07-17 | 2023-08-18 | 山东圣剑医学研究有限公司 | General surgery patient data management method |
| CN116612870B (en) * | 2023-07-17 | 2023-10-10 | 山东圣剑医学研究有限公司 | General surgery patient data management method |
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Application publication date: 20190319 |