CN113823394A - Intelligent diagnosis guiding method, system, equipment and medium based on ant colony algorithm - Google Patents

Intelligent diagnosis guiding method, system, equipment and medium based on ant colony algorithm Download PDF

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CN113823394A
CN113823394A CN202111383003.4A CN202111383003A CN113823394A CN 113823394 A CN113823394 A CN 113823394A CN 202111383003 A CN202111383003 A CN 202111383003A CN 113823394 A CN113823394 A CN 113823394A
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林山驰
沈凯彬
丁双安
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Areson Technology Corp
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Abstract

The invention discloses an intelligent diagnosis guiding method, system, equipment and medium based on an ant colony algorithm, wherein the method comprises the following steps: acquiring department information; initializing pheromone intensity and ant colony; determining an optimal path by adopting an ant colony algorithm, and taking the determined optimal path as the department sequence with the least total time consumption of the medical staff to realize diagnosis guide: determining the expected degree of transfer among ant departments; determining the transfer probability among ant departments; determining paths of ants to all departments by adopting roulette; determining the total time consumption of each ant reaching all departments; determining an optimal path by taking the minimum total time consumption as an optimization target; updating pheromone intensity; judging whether iteration ending conditions are met, if so, using the determined optimal path as the department sequence with the least total time consumption of the staff for seeing a doctor to realize diagnosis guide; if not, the ant colony algorithm after the intensity of the pheromone is updated is continuously adopted to determine the optimal path. The invention is based on the ant colony algorithm, aims at the minimum time consumption, reasonably leads the doctor and improves the diagnosis efficiency.

Description

Intelligent diagnosis guiding method, system, equipment and medium based on ant colony algorithm
Technical Field
The present invention relates to the field of intelligent medical treatment, and more particularly, to an ant colony algorithm-based intelligent diagnosis guiding method, system, device, and medium.
Background
The prior informatization management system of the medical service organization focuses on the informatization management of the organization, ignores the subjective diagnosis experience of the diagnosis personnel, moreover, the system is disjointed to a certain extent from the latest artificial intelligence technology, the intelligence level of information management is low, for example, for the doctor notification of the waiting staff, the existing medical service institution generally displays the name of the waiting staff through manual number calling or an LED display screen, and the doctor often does not know the position distance of each department in the hospital, or the reason that the number of the patients waiting for a doctor in real time in each department is unclear, the doctor journey cannot be reasonably planned, the patients are often discharged out of the department with a large number of the patients waiting for a doctor for a long time, and under the condition that no one exists outside other departments, the queuing efficiency is low, a large amount of time is wasted, the treatment experience of the treatment personnel is influenced, and the waste of medical manpower or material resources is caused at the same time.
Disclosure of Invention
The invention aims to overcome at least one defect (deficiency) of the prior art, and provides an ant colony algorithm-based intelligent diagnosis guiding method, system, equipment and medium, which are used for solving the problems that the diagnosis staff cannot reasonably plan the diagnosis journey and the diagnosis experience is poor.
The invention adopts the technical scheme that an intelligent diagnosis guiding method based on an ant colony algorithm comprises the following steps:
acquiring department information which comprises the average single-time completion visit time of each department
Figure 775733DEST_PATH_IMAGE001
Department room transfer matrix
Figure 109763DEST_PATH_IMAGE002
Distance matrix between departments
Figure 858407DEST_PATH_IMAGE003
And the number of people waiting for a doctor in each department
Figure 176256DEST_PATH_IMAGE004
Wherein i and j represent department numbers, i =1,2, …, N, j =1,2, …, N represents the number of departments, t represents time t,
Figure 160392DEST_PATH_IMAGE005
indicating the possibility of transfer to department j after the visit to department i is completed,
Figure 739141DEST_PATH_IMAGE006
represents the distance between department i and department j;
initializing pheromone intensity and ant colony in the ant colony algorithm;
determining an optimal path using the ant colony algorithm
Figure 783321DEST_PATH_IMAGE007
Determining the optimal path
Figure 588466DEST_PATH_IMAGE008
As the sequencing of the departments with the least total time consumption of the staff for seeing a doctor, the method realizes the diagnosis guide:
according to the average time of single visit of each department
Figure 986080DEST_PATH_IMAGE009
Distance matrix between departments
Figure 560281DEST_PATH_IMAGE010
And the number of people waiting for a doctor in each department
Figure 775361DEST_PATH_IMAGE011
Determining the expected degree of transfer of each ant in the ant colony from department i to department j
Figure 661278DEST_PATH_IMAGE012
；
According to the expected degree of transferring each ant from department i to department j
Figure 252796DEST_PATH_IMAGE013
Intensity of pheromone and transfer matrix of each department
Figure 681503DEST_PATH_IMAGE002
Determining the transition probability of each ant from department i to department j
Figure 677272DEST_PATH_IMAGE014
；
According to the transfer probability of each ant from department i to department j
Figure 457010DEST_PATH_IMAGE015
Determining paths of ants to reach all departments by roulette
Figure 586640DEST_PATH_IMAGE016
；
According to the expected degree of transferring each ant from department i to department j
Figure 869853DEST_PATH_IMAGE017
Determining the total time spent by each ant to reach all departments
Figure 816950DEST_PATH_IMAGE018
；
Completing the path from each ant to all departments with the minimum total time consumption as the optimization target
Figure 552825DEST_PATH_IMAGE019
To determine the optimal path
Figure 486145DEST_PATH_IMAGE020
；
In determining the optimal path
Figure 487513DEST_PATH_IMAGE021
Then, updating the intensity of the pheromone;
judging whether the iteration end condition is met or not,
if yes, determining the optimal path
Figure 215298DEST_PATH_IMAGE022
The medical staff is used as the department sequencing with the least total time consumption to realize the diagnosis guidance;
if not, the ant colony algorithm after the pheromone intensity is updated is continuously adopted to determine the optimal path
Figure 704048DEST_PATH_IMAGE023
。
The traditional ant colony algorithm is a probabilistic algorithm for searching an optimized path, each ant in an ant colony releases corresponding pheromone in the passing path, the moving direction of the ant is guided by sensing the existence and the strength of the pheromone on the path, the ant walks along the path with higher pheromone concentration, the amount of the pheromone released by the ant with shorter path is more, the pheromone concentration accumulated on the shorter path is gradually increased along with the advance of time, the number of the ants selecting the path is more and more, and finally, the whole ant is concentrated on the shortest path under the action of positive feedback. In the invention, the distance minimization target in the traditional ant colony algorithm is converted into the total time consumption minimization target by adaptively modifying the medical scene of the traditional ant colony algorithm, the whole diagnosis process is intelligently calculated by combining the clinic information of the diagnosis of the patient, such as the average single completion diagnosis time of each clinic, the upstream and downstream relation of the diagnosis process, the distance, the number of real-time waiting patients and other factors, the total time consumption of the whole diagnosis process is sorted by the clinic, the diagnosis of the patient is guided by the calculated clinic sorting, the diagnosis process of the patient is reasonably planned, the time consumption of the patient is saved, the queuing efficiency and the experience of the patient are improved, the intellectualization of the medical system data application of the medical service institution is realized, and the informatization management level of the medical service institution is improved. Meanwhile, the department ranking result obtained through the ant colony optimization calculation can be dynamically updated according to the real-time queuing condition of the medical staff or the departments, and the reasonability and the reliability of the obtained department ranking are further improved.
Further, according to the average time of single visit of each department
Figure 300114DEST_PATH_IMAGE024
Distance matrix between departments
Figure 557920DEST_PATH_IMAGE025
And the number of people waiting for a doctor in each department
Figure 456606DEST_PATH_IMAGE026
Determining the expected degree of transfer of each ant in the ant colony from department i to department j
Figure 42440DEST_PATH_IMAGE027
The method specifically comprises the following steps:
determining the expected degree of transfer of each ant in the ant colony from department i to department j by using the following calculation formula
Figure 317563DEST_PATH_IMAGE013
：
Figure 695455DEST_PATH_IMAGE028
。
In the present invention, the desired degree of transfer
Figure 624097DEST_PATH_IMAGE017
The method is not only related to the distance between departments, but also related to the average single-time completion visit time and the number of real-time waiting persons of each department, so that the ant colony algorithm is suitable for modification in a medical treatment guide scene, and various parameters related to the total time consumption of the visit are brought into a calculation consideration range, so that the final optimization result is more accurate and reliable, and the purpose of intelligently planning the visit process with the minimum total time consumption as a target is achieved.
Further, according to the expected degree of transferring each ant from department i to department j
Figure 87439DEST_PATH_IMAGE029
Intensity of pheromone and transfer matrix of each department
Figure 900674DEST_PATH_IMAGE030
Determining the transition probability of each ant from department i to department j
Figure 742859DEST_PATH_IMAGE031
The method specifically comprises the following steps:
determining the transition probability of each ant from department i to department j by adopting the following calculation formula
Figure 983348DEST_PATH_IMAGE032
：
Figure 933986DEST_PATH_IMAGE033
Wherein the content of the first and second substances,
Figure 550912DEST_PATH_IMAGE034
representing the probability of the transfer of an ant k from department i to department j in the ant colony at time t, k =1,2, … …, M representing the number of ants,
Figure 496872DEST_PATH_IMAGE035
indicates the intensity of the pheromone for department j at time t,
Figure 173841DEST_PATH_IMAGE036
indicating the desirability of transferring ant k from department i to department j in the colony,
Figure 424825DEST_PATH_IMAGE037
representing the set of departments in the ant colony that ant k has not reached, as the algorithm proceeds,
Figure 579862DEST_PATH_IMAGE038
the set is continually getting smaller, until empty,
Figure 645907DEST_PATH_IMAGE039
is an important process factor for the pheromone,
Figure 228198DEST_PATH_IMAGE040
the importance factor of the expected degree of transfer between departments determines which factor of pheromone and the expected degree of transfer between departments plays a greater role.
Further, according to the expected degree of transferring each ant from department i to department j
Figure 887850DEST_PATH_IMAGE041
Determining the total time spent by each ant to reach all departments
Figure 456366DEST_PATH_IMAGE042
The method specifically comprises the following steps:
determining the total time spent by each ant to reach all departments by adopting the following calculation formula
Figure 517862DEST_PATH_IMAGE043
：
Figure 536634DEST_PATH_IMAGE044
。
Further, with the minimum total time consumption as an optimization target, completing the path from each ant to all departments
Figure 542636DEST_PATH_IMAGE045
To determine the optimal path
Figure 39477DEST_PATH_IMAGE046
The method specifically comprises the following steps:
the following calculation formula is adopted to find the leechesWhen ants complete the total consumption to all departments
Figure 955480DEST_PATH_IMAGE047
And obtains the corresponding ant number
Figure 754940DEST_PATH_IMAGE048
：
Figure 389184DEST_PATH_IMAGE049
；
According to the obtained ant number
Figure 689715DEST_PATH_IMAGE050
On each ant completing the path to all departments
Figure 319279DEST_PATH_IMAGE051
In determining the optimal path
Figure 679854DEST_PATH_IMAGE052
。
Further, the optimal path is determined
Figure 801393DEST_PATH_IMAGE053
And then, updating the intensity of the pheromone, which specifically comprises the following steps:
the pheromone intensity is updated using the following calculation:
Figure 515403DEST_PATH_IMAGE054
Figure 405998DEST_PATH_IMAGE055
wherein the content of the first and second substances,
Figure 671894DEST_PATH_IMAGE056
indicates the intensity of the pheromone for department j at time t,
Figure 139785DEST_PATH_IMAGE057
an empirical coefficient representing the range (0,1),
Figure 782119DEST_PATH_IMAGE058
show department
Figure 527221DEST_PATH_IMAGE060
Pheromone increments.
Further, the iteration end condition includes: and the iteration times reach the maximum iteration times or the pheromone intensity reaches the pheromone intensity threshold, wherein the iteration times are the updating times of the pheromone intensity.
In the invention, the iteration ending condition of the ant colony algorithm is set, so that the waste of computing resources and time caused by continuous computation of the ant colony algorithm is avoided, the computing burden of a system is increased, the complexity of the algorithm is effectively reduced, the diagnosis guiding rate is accelerated, and the diagnosis experience of the diagnosis personnel is improved.
On the other hand, another technical scheme adopted by the invention is that an intelligent diagnosis guide system based on an ant colony algorithm comprises:
a department information acquisition module for acquiring department information including average single visit time of each department
Figure 104964DEST_PATH_IMAGE001
Department room transfer matrix
Figure 669937DEST_PATH_IMAGE061
Distance matrix between departments
Figure 850383DEST_PATH_IMAGE003
And the number of people waiting for a doctor in each department
Figure 840205DEST_PATH_IMAGE062
Wherein i and j represent department numbers, i =1,2, …, N, j =1,2, …, N represents the number of departments, t represents time t,
Figure 447904DEST_PATH_IMAGE005
indicating the possibility of transfer to department j after the visit to department i is completed,
Figure 765752DEST_PATH_IMAGE063
represents the distance between department i and department j;
the initialization module is used for initializing pheromone intensity and ant colony in the ant colony algorithm;
an optimized path determining module for determining an optimal path by using the ant colony algorithm
Figure 625255DEST_PATH_IMAGE064
Determining the optimal path
Figure 79370DEST_PATH_IMAGE065
The medical staff is used as the department sequencing with the least total time consumption to realize the diagnosis guidance;
the optimized path determining module specifically includes:
a transfer expectation degree determining module for determining the average single visit time according to the departments
Figure 123550DEST_PATH_IMAGE066
Distance matrix between departments
Figure 53328DEST_PATH_IMAGE067
And the number of people waiting for a doctor in each department
Figure 841156DEST_PATH_IMAGE026
Determining the expected degree of transfer of each ant in the ant colony from department i to department j
Figure 149777DEST_PATH_IMAGE068
；
A transfer probability determining module for determining transfer expectation degree of each ant from department i to department j
Figure 240224DEST_PATH_IMAGE013
PheromoneIntensity and transfer matrix for each department
Figure 267086DEST_PATH_IMAGE069
Determining the transition probability of each ant from department i to department j
Figure 593025DEST_PATH_IMAGE014
；
A path determining module for determining the transfer probability of each ant from department i to department j
Figure 146366DEST_PATH_IMAGE015
Determining paths of ants to reach all departments by roulette
Figure 532348DEST_PATH_IMAGE016
；
A total time consumption determining module for determining the expected degree of transfer of each ant from department i to department j
Figure 46506DEST_PATH_IMAGE070
Determining the total time spent by each ant to reach all departments
Figure 51502DEST_PATH_IMAGE018
；
An optimal path determining module for completing the paths from the ants to all departments with the minimum total time consumption as the optimization target
Figure 334716DEST_PATH_IMAGE019
To determine the optimal path
Figure 157179DEST_PATH_IMAGE020
；
A pheromone intensity updating module for determining the optimal path
Figure 17687DEST_PATH_IMAGE071
Then, updating the intensity of the pheromone;
an iteration ending condition judging module for judging whether the current time is fullIf the iteration is enough to finish the condition, determining the determined optimal path
Figure 685429DEST_PATH_IMAGE072
The department ranking with the least total time consumption of the personnel in the clinic is used for realizing the diagnosis guide, if not, the ant colony algorithm after the pheromone intensity is updated is continuously adopted to determine the optimal path
Figure 88729DEST_PATH_IMAGE073
。
On the other hand, another technical solution adopted by the present invention is that a computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above-mentioned ant colony algorithm-based intelligent diagnosis guiding method when executing the computer program.
On the other hand, another technical solution adopted by the present invention is a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned intelligent diagnosis guiding method based on ant colony algorithm.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, through adaptive modification of medical scenes of a traditional ant colony algorithm, the distance minimized target in the traditional ant colony algorithm is converted into a total time consumption minimized target, and by combining the department information of the visit staff, such as the average single visit time of each department, the upstream and downstream relation, the distance, the number of real-time waiting people and other factors, the whole visit flow of the visit staff is intelligently calculated, the total time consumption is minimum, the department sequence is calculated, the visit staff is guided through the calculated department sequence, the visit journey of the visit staff is reasonably planned, the visit time consumption of the visit staff is favorably saved, the queuing efficiency and the experience of the visit staff are improved, the intellectualization of the data application of a medical system is realized, and the informatization management level of a medical service mechanism is improved; meanwhile, the department ranking result obtained by the ant colony algorithm optimization calculation can be dynamically updated according to the real-time queuing condition of the medical staff or departments, and the reasonability and reliability of the obtained department ranking are further improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a flowchart of step S3 of the method of the present invention.
FIG. 3 is a block diagram of the system of the present invention.
Description of reference numerals: the system comprises a department information acquisition module 100, an initialization module 200, an optimized path determination module 300, a transfer expectation degree determination module 310, a transfer probability determination module 320, a path determination module 330, a total time consumption determination module 340, an optimal path determination module 350, a pheromone intensity updating module 400 and an iteration ending condition judgment module 500.
Detailed Description
The drawings are only for purposes of illustration and are not to be construed as limiting the invention. For a better understanding of the following embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
Example 1
As shown in fig. 1, the present embodiment provides an intelligent diagnosis guiding method based on ant colony algorithm, including the following steps:
s1: acquiring department information;
specifically, in the present embodiment, department numbers i and j are represented, i =1,2, …, N, j =1,2, …, N represents the number of departments, and the department information includes:
(1) the position coordinates of each department position database are as follows:
Figure 691879DEST_PATH_IMAGE074
(2) the average single visit time of each department:
Figure 915050DEST_PATH_IMAGE075
(3) the transfer matrix of each department mainly reflects the upstream and downstream relationship of each department in the treatment process:
Figure 652062DEST_PATH_IMAGE076
to be provided with
Figure 909868DEST_PATH_IMAGE077
The elements of the matrix, i.e. the possibility of transfer to department j after the end of the visit at department i, are in particular:
Figure 667609DEST_PATH_IMAGE078
(4) each family room distance matrix:
Figure 643655DEST_PATH_IMAGE079
to be provided with
Figure 918778DEST_PATH_IMAGE080
Representing the distance between the elements in the matrix, i.e. department i to department j;
(5) the number of people waiting for the department at the current moment t:
Figure 906457DEST_PATH_IMAGE081
specifically, the real-time waiting number of people in each department can be counted by using a target detection technology based on computer vision, specifically, a camera near each department is accessed to a background server in a real-time picture, the number of people in the real-time picture is analyzed and discriminated by using a computer vision algorithm, and the real waiting number of people is counted;
s2, initializing pheromone intensity and ant colony in the ant colony algorithm;
specifically, in the present embodiment,
initializing pheromone intensities to assign a dimensionless one unit intensity to each departmentThe pheromone of degree, i.e. the pheromone intensity array, is specifically:
Figure 976044DEST_PATH_IMAGE082
the length of the array is the number N of departments;
and initializing an ant colony, setting an ant colony scale M,
Figure 173807DEST_PATH_IMAGE083
randomly distributing a path starting department for each ant in the ant colony;
in addition, in this embodiment, initializing the ant colony further includes initializing a maximum number of iterations;
s3, determining the optimal path by adopting the ant colony algorithm
Figure 377256DEST_PATH_IMAGE071
；
Specifically, as shown in fig. 2, step S3 includes:
s301, according to the average single visit time of each department
Figure 609654DEST_PATH_IMAGE009
Distance matrix between departments
Figure 850142DEST_PATH_IMAGE010
And the number of people waiting for a doctor in each department
Figure 410568DEST_PATH_IMAGE026
Determining the expected degree of transfer of each ant in the ant colony from department i to department j
Figure 27494DEST_PATH_IMAGE084
；
Further, step S301 specifically includes:
determining the expected degree of transfer of each ant in the ant colony from department i to department j by using the following calculation formula
Figure 848819DEST_PATH_IMAGE085
：
Figure 384843DEST_PATH_IMAGE086
S302, according to the expected degree of transferring each ant from department i to department j
Figure 557198DEST_PATH_IMAGE013
Intensity of pheromone and transfer matrix of each department
Figure 977815DEST_PATH_IMAGE069
Determining the transition probability of each ant from department i to department j
Figure 529014DEST_PATH_IMAGE014
；
Further, step S302 specifically includes:
determining the transition probability of each ant from department i to department j by adopting the following calculation formula
Figure 376884DEST_PATH_IMAGE014
：
Figure 36535DEST_PATH_IMAGE087
Wherein the content of the first and second substances,
Figure 588739DEST_PATH_IMAGE088
representing the probability of the transfer of an ant k from department i to department j in the ant colony at time t, k =1,2, … …, M representing the number of ants,
Figure 650236DEST_PATH_IMAGE089
the intensity of the pheromone representing the department at time j,
Figure 669008DEST_PATH_IMAGE090
indicating the desirability of transferring ant k from department i to department j in the colony,
Figure 425742DEST_PATH_IMAGE091
representing the set of departments in the ant colony that ant k has not reached, as the algorithm proceeds,
Figure 922583DEST_PATH_IMAGE038
the set is continually getting smaller, until empty,
Figure 838586DEST_PATH_IMAGE092
is an important process factor for the pheromone,
Figure 152893DEST_PATH_IMAGE093
the importance factor of the expected degree of transfer between departments determines which factor of pheromone and the expected degree of transfer between departments plays a greater role.
S303, according to the transfer probability of each ant from department i to department j
Figure 787137DEST_PATH_IMAGE015
Determining paths of ants to reach all departments by roulette
Figure 822089DEST_PATH_IMAGE016
；
Specifically, in step S303, a roulette method is used to determine the path of each ant to reach all departments
Figure 479684DEST_PATH_IMAGE045
The method comprises the following steps:
calculating the sum of the transfer probabilities of each ant moving to other departments not reached yet in the current department:
Figure 840258DEST_PATH_IMAGE094
；
calculating the proportion probability of each transition probability:
Figure 430639DEST_PATH_IMAGE095
；
calculating the cumulative percentage probability of each transition probability:
Figure 659495DEST_PATH_IMAGE096
；
wherein each term in the cumulative percentage probability is the sum of all terms before the term in the percentage probability, which may be, for example: when the ratio probability sequence is
Figure 550091DEST_PATH_IMAGE097
The cumulative percentage probability of which is
Figure 81566DEST_PATH_IMAGE098
；
Taking a random number with a value range between (0,1)
Figure 424823DEST_PATH_IMAGE099
Finding the first one greater than the cumulative ratio probability sequence
Figure 676944DEST_PATH_IMAGE100
And returning the index, namely the next target department to be visited by the ant, and updating the department in the path
Figure 422046DEST_PATH_IMAGE101
Performing the following steps;
repeating the roulette method until each ant completes the path to all departments
Figure 124423DEST_PATH_IMAGE102
。
S304, according to the expected degree of transferring each ant from department i to department j
Figure 814030DEST_PATH_IMAGE041
Determining the total time spent by each ant to reach all departments
Figure 260055DEST_PATH_IMAGE103
；
Further, step S304 specifically includes: determining the total time spent by each ant to reach all departments by adopting the following calculation formula
Figure 125243DEST_PATH_IMAGE104
：
Figure 873887DEST_PATH_IMAGE105
S305, with the minimum total time consumption as an optimization target, completing the path from each ant to all departments
Figure 660577DEST_PATH_IMAGE106
To determine the optimal path
Figure 910293DEST_PATH_IMAGE071
；
Further, step S305 specifically includes:
the following calculation formula is adopted to find the total time spent by each ant to reach all departments
Figure 754621DEST_PATH_IMAGE107
And obtains the corresponding ant number
Figure 533221DEST_PATH_IMAGE108
：
Figure 72787DEST_PATH_IMAGE109
；
According to the obtained ant number
Figure 735981DEST_PATH_IMAGE110
On each ant completing the path to all departments
Figure 44602DEST_PATH_IMAGE101
In determining the optimal path
Figure 259683DEST_PATH_IMAGE111
。
S4, determining the optimal path
Figure 676758DEST_PATH_IMAGE112
Then, updating the intensity of the pheromone;
further, step S4 specifically includes:
the pheromone intensity is updated using the following calculation:
Figure 2697DEST_PATH_IMAGE113
Figure 431404DEST_PATH_IMAGE114
wherein the content of the first and second substances,
Figure 692752DEST_PATH_IMAGE115
indicates the intensity of the pheromone for department j at time t,
Figure 941331DEST_PATH_IMAGE116
an empirical coefficient representing the range (0,1),
Figure 70961DEST_PATH_IMAGE117
representing department j pheromone increments.
S5, judging whether the iteration end condition is met, if yes, executing a step S6, and if not, returning to the step S3;
in this embodiment, the iteration ending condition is specifically that the iteration number reaches the maximum iteration number, and the iteration number is the update number of the pheromone intensity;
s6, determining the optimal path
Figure 744388DEST_PATH_IMAGE112
The medical guidance is realized as the sequencing of the departments with the least total time consumption of the medical staff.
Example 2
The embodiment provides an intelligent diagnosis guiding method based on an ant colony algorithm, which includes the steps that the specific steps are basically the same as those in embodiment 1, except that in this embodiment, initializing an ant colony further includes initializing an pheromone intensity threshold instead of initializing the maximum iteration number, and an iteration ending condition specifically includes that the pheromone intensity reaches the pheromone intensity threshold, but not the iteration number reaches the maximum iteration number.
Example 3
As shown in fig. 3, the present embodiment provides an intelligent diagnosis guiding system based on ant colony algorithm, including:
a department information obtaining module 100 for obtaining department information,
specifically, in this embodiment, department numbers i and j are represented, where i =1,2, …, N, j =1,2, …, N, and N represent the number of departments, and the obtained department information includes:
(1) the position coordinates of each department position database are as follows:
Figure 301271DEST_PATH_IMAGE118
(2) the average single visit time of each department:
Figure 302725DEST_PATH_IMAGE119
(3) the transfer matrix of each department mainly reflects the upstream and downstream relationship of each department in the treatment process:
Figure 845833DEST_PATH_IMAGE120
to be provided with
Figure 249133DEST_PATH_IMAGE121
The elements of the matrix, i.e. the possibility of transfer to department j after the end of the visit at department i, are in particular:
Figure 976917DEST_PATH_IMAGE122
(4) each family room distance matrix:
Figure 324722DEST_PATH_IMAGE123
to be provided with
Figure 61734DEST_PATH_IMAGE124
Representing the distance between the elements in the matrix, i.e. department i to department j;
(5) the number of people waiting for the department at the current moment t:
Figure 319540DEST_PATH_IMAGE125
specifically, the real-time waiting number of people in each department can be counted by using an AI counting module based on a computer vision target detection technology, specifically, a camera near each department is accessed to a background server in real time, the number of people in the real-time picture is analyzed and distinguished by using a computer vision algorithm, and the real waiting number of people is counted;
an initialization module 200, configured to initialize pheromone intensities and ant colonies in an ant colony algorithm;
specifically, in the present embodiment,
initializing pheromone intensity, and distributing dimensionless pheromones with one unit intensity to each department, namely the pheromone intensity array specifically comprises the following steps:
Figure 218226DEST_PATH_IMAGE126
the length of the array is the number N of departments;
and initializing an ant colony, setting an ant colony scale M,
Figure 804059DEST_PATH_IMAGE127
randomly distributing a path starting department for each ant in the ant colony;
in addition, in this embodiment, initializing the ant colony further includes initializing a maximum number of iterations;
an optimized path determination module 300 for determining an optimal path using the ant colony algorithm
Figure 79183DEST_PATH_IMAGE128
Determining the optimal path
Figure 191495DEST_PATH_IMAGE129
The medical staff is used as the department sequencing with the least total time consumption to realize the diagnosis guidance;
the optimized path determining module 300 specifically includes:
a transfer expectation degree determination module 310 for determining the average single visit time according to the departments
Figure 651295DEST_PATH_IMAGE066
Distance matrix between departments
Figure 583479DEST_PATH_IMAGE067
And the number of people waiting for a doctor in each department
Figure 662294DEST_PATH_IMAGE026
Determining the expected degree of transfer of each ant in the ant colony from department i to department j
Figure 504479DEST_PATH_IMAGE068
；
Further, the transfer expectation determining module 310 specifically determines the transfer expectation of each ant in the ant colony from department i to department j by using the following calculation formula
Figure 744967DEST_PATH_IMAGE090
：
Figure 430027DEST_PATH_IMAGE130
A transfer probability determining module 320 for determining the expected degree of transfer of each ant from department i to department j
Figure 171587DEST_PATH_IMAGE131
Intensity of pheromone and transfer matrix of each department
Figure 258491DEST_PATH_IMAGE069
Determining the transition probability of each ant from department i to department j
Figure 669881DEST_PATH_IMAGE132
；
Further, the transition probability determining module 320 specifically determines the transition probability of each ant from department i to department j by using the following calculation formula
Figure 717603DEST_PATH_IMAGE014
：
Figure 138220DEST_PATH_IMAGE133
Wherein the content of the first and second substances,
Figure 814052DEST_PATH_IMAGE088
representing the probability of the transfer of an ant k from department i to department j in the ant colony at time t, k =1,2, … …, M representing the number of ants,
Figure 786556DEST_PATH_IMAGE089
the intensity of the pheromone representing the department at time j,
Figure 446207DEST_PATH_IMAGE090
indicating the desirability of transferring ant k from department i to department j in the colony,
Figure 404936DEST_PATH_IMAGE091
representing the set of departments in the ant colony that ant k has not reached, as the algorithm proceeds,
Figure 76220DEST_PATH_IMAGE038
the set is continually getting smaller, until empty,
Figure 829412DEST_PATH_IMAGE092
is an important process factor for the pheromone,
Figure 976360DEST_PATH_IMAGE093
the importance factor of the expected degree of transfer between departments determines which factor of pheromone and the expected degree of transfer between departments plays a greater role.
A path determining module 330, configured to determine a transition probability of each ant from department i to department j according to the determined transition probability
Figure 597834DEST_PATH_IMAGE015
Determining paths of ants to reach all departments by roulette
Figure 513837DEST_PATH_IMAGE016
；
Specifically, the path determining module 330 determines paths of ants to reach all departments by using a roulette method
Figure 703510DEST_PATH_IMAGE045
The method comprises the following steps:
calculating the sum of the transfer probabilities of each ant moving to other departments not reached yet in the current department:
Figure 72175DEST_PATH_IMAGE134
；
calculating the proportion probability of each transition probability:
Figure 248072DEST_PATH_IMAGE135
；
calculating the cumulative percentage probability of each transition probability:
Figure 18582DEST_PATH_IMAGE136
；
wherein each term in the cumulative percentage probability is the sum of all terms before the term in the percentage probability, which may be, for example: when the ratio probability sequence is
Figure 379156DEST_PATH_IMAGE137
The cumulative percentage probability of which is
Figure 359751DEST_PATH_IMAGE138
；
Taking a random number with a value range between (0,1)
Figure 198394DEST_PATH_IMAGE139
Finding the first one greater than the cumulative ratio probability sequence
Figure 88989DEST_PATH_IMAGE140
And returning the index, namely the next target department to be visited by the ant, and updating the department in the path
Figure 495831DEST_PATH_IMAGE019
Performing the following steps;
repeating the roulette method until each ant completes the path to all departments
Figure 573508DEST_PATH_IMAGE045
。
A total time consumption determining module 340 for determining the expected degree of transferring each ant from department i to department j
Figure 215842DEST_PATH_IMAGE041
Determining the total time spent by each ant to reach all departments
Figure 85578DEST_PATH_IMAGE141
；
Further, the total time consumption determining module 340 specifically determines the total time consumption of each ant reaching all departments by using the following calculation formula
Figure 522376DEST_PATH_IMAGE104
：
Figure 352928DEST_PATH_IMAGE142
An optimal path determining module 350, configured to complete paths from the ants to all departments with minimum total time consumption as an optimization goal
Figure 408740DEST_PATH_IMAGE143
To determine the optimal path
Figure 8349DEST_PATH_IMAGE144
；
Further, the optimal path determination module 350 determines the optimal path
Figure 881627DEST_PATH_IMAGE145
The method specifically comprises the following steps:
the following calculation formula is adopted to find the total time spent by each ant to reach all departments
Figure 324110DEST_PATH_IMAGE107
And obtains the corresponding ant number
Figure 308246DEST_PATH_IMAGE108
：
Figure 762361DEST_PATH_IMAGE146
；
According to the obtained ant number
Figure 681907DEST_PATH_IMAGE110
On each ant completing the path to all departments
Figure 221473DEST_PATH_IMAGE101
In determining the optimal path
Figure 9300DEST_PATH_IMAGE147
。
A pheromone intensity update module 400 for determining the optimal path
Figure 708135DEST_PATH_IMAGE148
Then, updating the intensity of the pheromone;
further, the pheromone intensity updating module 400 specifically updates the pheromone intensity by using the following calculation formula:
Figure 657636DEST_PATH_IMAGE149
Figure 950077DEST_PATH_IMAGE150
wherein the content of the first and second substances,
Figure 151382DEST_PATH_IMAGE115
indicates the intensity of the pheromone for department j at time t,
Figure 580090DEST_PATH_IMAGE116
an empirical coefficient representing the range (0,1),
Figure 966072DEST_PATH_IMAGE117
representing department j pheromone increments.
An iteration end condition determining module 500, configured to determine whether an iteration end condition is met, and if yes, determine the determined optimal path
Figure 604863DEST_PATH_IMAGE151
The department sequence with the least total time consumption of the medical personnel is used for guiding the medical treatment, if not, the optimal path is determined by the optimal path determining module 300 by adopting the ant colony algorithm after the pheromone intensity is updated
Figure 468914DEST_PATH_IMAGE152
。
In this embodiment, the iteration ending condition is specifically that the iteration number reaches the maximum iteration number, and the iteration number is the update number of the pheromone strength.
Example 4
The embodiment provides an intelligent diagnosis guiding system based on an ant colony algorithm, which has a similar structure to that of embodiment 3, except that in this embodiment, the initialization ant colony of the ant colony algorithm initialization module 200 further includes an initialization pheromone intensity threshold instead of the initialization maximum iteration number, and the iteration ending condition determined by the iteration ending condition determination module 500 is specifically that the pheromone intensity reaches the pheromone intensity threshold, but not the iteration number reaches the maximum iteration number.
Example 5
The present embodiment provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the ant colony algorithm-based intelligent diagnosis guiding method in embodiment 1 described above when executing the computer program.
Example 6
The present embodiment provides a storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the above-mentioned ant colony algorithm-based intelligent diagnosis guiding method.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific embodiments of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention claims should be included in the protection scope of the present invention claims.

Claims (10)

1. An intelligent diagnosis guiding method based on an ant colony algorithm is characterized by comprising the following steps:
acquiring department information which comprises the average single-time completion visit time of each department
Figure 351053DEST_PATH_IMAGE001
Department room transfer matrix
Figure 44203DEST_PATH_IMAGE002
Distance matrix between departments
Figure 964754DEST_PATH_IMAGE003
And the number of people waiting for a doctor in each department
Figure 717947DEST_PATH_IMAGE004
Wherein i and j represent department numbers, i =1,2, …, N, j =1,2, …, N represents the number of departments, t represents time t,
Figure 474681DEST_PATH_IMAGE005
indicating the possibility of transfer to department j after the visit to department i is completed,
Figure 237101DEST_PATH_IMAGE006
represents the distance between department i and department j;
initializing pheromone intensity and ant colony in the ant colony algorithm;
determining an optimal path using the ant colony algorithm
Figure 153104DEST_PATH_IMAGE007
Determining the optimal path
Figure 342777DEST_PATH_IMAGE008
As the sequencing of the departments with the least total time consumption of the staff for seeing a doctor, the method for realizing the diagnosis guide specifically comprises the following steps:
according to the average time of single visit of each department
Figure 836075DEST_PATH_IMAGE009
Distance matrix between departments
Figure 871027DEST_PATH_IMAGE010
And the number of people waiting for a doctor in each department
Figure 907117DEST_PATH_IMAGE011
Determining the expected degree of transfer of each ant in the ant colony from department i to department j
Figure 877478DEST_PATH_IMAGE012
；
According to the expected degree of transferring each ant from department i to department j
Figure 733438DEST_PATH_IMAGE013
Intensity of pheromone and transfer matrix of each department
Figure 837661DEST_PATH_IMAGE002
Determining the transition probability of each ant from department i to department j
Figure 852890DEST_PATH_IMAGE014
；
According to the transfer probability of each ant from department i to department j
Figure 384365DEST_PATH_IMAGE015
Determining paths of ants to reach all departments by roulette
Figure 462043DEST_PATH_IMAGE016
；
According to the expected degree of transferring each ant from department i to department j
Figure 979743DEST_PATH_IMAGE017
Determining the total time spent by each ant to reach all departments
Figure 459266DEST_PATH_IMAGE018
；
Completing the path from each ant to all departments with the minimum total time consumption as the optimization target
Figure 161643DEST_PATH_IMAGE019
To determine the optimal path
Figure 851250DEST_PATH_IMAGE020
；
In determining the optimal path
Figure 297275DEST_PATH_IMAGE021
Then, updating the intensity of the pheromone;
judging whether the iteration end condition is met or not,
if yes, determining the optimal path
Figure 896883DEST_PATH_IMAGE022
The medical staff is used as the department sequencing with the least total time consumption to realize the diagnosis guidance;
if not, the ant colony algorithm after the pheromone intensity is updated is continuously adopted to determine the optimal path
Figure 645528DEST_PATH_IMAGE023
。
2. The ant colony algorithm-based intelligent diagnosis guiding method according to claim 1, wherein the diagnosis time is determined according to the average single-time completion visit time of each department
Figure 963377DEST_PATH_IMAGE024
Distance matrix between departments
Figure 947513DEST_PATH_IMAGE003
And the number of people waiting for a doctor in each department
Figure 526262DEST_PATH_IMAGE011
Determining the expected degree of transfer of each ant in the ant colony from department i to department j
Figure 304862DEST_PATH_IMAGE025
The method specifically comprises the following steps:
determining the expected degree of transfer of each ant in the ant colony from department i to department j by using the following calculation formula
Figure 110007DEST_PATH_IMAGE013
：
Figure 773201DEST_PATH_IMAGE026
。
3. The method as claimed in claim 1, wherein the ant colony algorithm-based intelligent diagnosis guidance method is implemented according to the expected degree of transfer of each ant from department i to department j
Figure 347402DEST_PATH_IMAGE027
Intensity of pheromone and transfer matrix of each department
Figure 296903DEST_PATH_IMAGE028
Determining the transition probability of each ant from department i to department j
Figure 713978DEST_PATH_IMAGE029
The method specifically comprises the following steps:
determining the transition probability of each ant from department i to department j by adopting the following calculation formula
Figure 39917DEST_PATH_IMAGE030
：
Figure 203045DEST_PATH_IMAGE031
Wherein the content of the first and second substances,
Figure 729972DEST_PATH_IMAGE032
represents the slave department of ant k in the ant colony at the time ti to department j, k =1,2, … …, M representing the number of ants,
Figure 244130DEST_PATH_IMAGE033
indicates the intensity of the pheromone for department j at time t,
Figure 108181DEST_PATH_IMAGE034
indicating the desirability of transferring ant k from department i to department j in the colony,
Figure 781608DEST_PATH_IMAGE035
representing the set of departments in the ant colony that ant k has not reached, as the algorithm proceeds,
Figure 338491DEST_PATH_IMAGE036
the set is continually getting smaller, until empty,
Figure 339945DEST_PATH_IMAGE037
is an important process factor for the pheromone,
Figure 273266DEST_PATH_IMAGE038
is an importance factor for transferring desired degrees between departments.
4. The method as claimed in claim 1, wherein the ant colony algorithm-based intelligent diagnosis guidance method is implemented according to the expected degree of transfer of each ant from department i to department j
Figure 298072DEST_PATH_IMAGE039
Determining the total time spent by each ant to reach all departments
Figure 25856DEST_PATH_IMAGE040
The method specifically comprises the following steps:
determining the total time spent by each ant to reach all departments by adopting the following calculation formula
Figure 373661DEST_PATH_IMAGE040
：
Figure 845093DEST_PATH_IMAGE041
。
5. The method as claimed in claim 1, wherein the path from each ant to all departments is optimized with minimum total time consumption
Figure 102899DEST_PATH_IMAGE042
To determine the optimal path
Figure 267165DEST_PATH_IMAGE043
The method specifically comprises the following steps:
the following calculation formula is adopted to find the total time spent by each ant to reach all departments
Figure 118577DEST_PATH_IMAGE044
And obtains the corresponding ant number
Figure 862542DEST_PATH_IMAGE045
：
Figure 240434DEST_PATH_IMAGE046
；
According to the obtained ant number
Figure 169076DEST_PATH_IMAGE047
On each ant completing the path to all departments
Figure 632418DEST_PATH_IMAGE048
In determining the optimal path
Figure 445653DEST_PATH_IMAGE049
。
6. The ant colony algorithm-based intelligent diagnosis guiding method according to claim 1, wherein the optimal path is determined
Figure 553418DEST_PATH_IMAGE050
And then, updating the intensity of the pheromone, which specifically comprises the following steps:
the pheromone intensity is updated using the following calculation:
Figure 59485DEST_PATH_IMAGE051
Figure 478965DEST_PATH_IMAGE052
wherein the content of the first and second substances,
Figure 220525DEST_PATH_IMAGE053
indicates the intensity of the pheromone for department j at time t,
Figure 41851DEST_PATH_IMAGE054
an empirical coefficient representing the range (0,1),
Figure 718820DEST_PATH_IMAGE055
show department
Figure 766541DEST_PATH_IMAGE057
Pheromone increments.
7. The ant colony algorithm-based intelligent diagnosis guiding method according to claim 1, wherein the iteration end condition comprises: and the iteration times reach the maximum iteration times or the pheromone intensity reaches the pheromone intensity threshold, wherein the iteration times are the updating times of the pheromone intensity.
8. An intelligent diagnosis guide system based on an ant colony algorithm is characterized by comprising:
a department information acquisition module for acquiring department information including average single visit time of each department
Figure 656000DEST_PATH_IMAGE001
Department room transfer matrix
Figure 862990DEST_PATH_IMAGE058
Distance matrix between departments
Figure 835494DEST_PATH_IMAGE003
And the number of people waiting for a doctor in each department
Figure 495146DEST_PATH_IMAGE059
Wherein i and j represent department numbers, i =1,2, …, N, j =1,2, …, N represents the number of departments, t represents time t,
Figure 453875DEST_PATH_IMAGE005
indicating the possibility of transfer to department j after the visit to department i is completed,
Figure 859579DEST_PATH_IMAGE060
represents the distance between department i and department j;
the initialization module is used for initializing pheromone intensity and ant colony in the ant colony algorithm;
an optimized path determining module for determining an optimal path by using the ant colony algorithm
Figure 878351DEST_PATH_IMAGE061
Determining the optimal path
Figure 759719DEST_PATH_IMAGE062
The medical staff is used as the department sequencing with the least total time consumption to realize the diagnosis guidance;
the optimized path determining module specifically includes:
a transfer expectation degree determining module for determining the average single visit time according to the departments
Figure 646773DEST_PATH_IMAGE009
Distance matrix between departments
Figure 562776DEST_PATH_IMAGE063
And the number of people waiting for a doctor in each department
Figure 486870DEST_PATH_IMAGE011
Determining the expected degree of transfer of each ant in the ant colony from department i to department j
Figure 730900DEST_PATH_IMAGE064
；
A transfer probability determining module for determining transfer expectation degree of each ant from department i to department j
Figure 31432DEST_PATH_IMAGE013
Intensity of pheromone and transfer matrix of each department
Figure 67521DEST_PATH_IMAGE065
Determining the transition probability of each ant from department i to department j
Figure 21570DEST_PATH_IMAGE014
；
A path determining module for determining the transfer probability of each ant from department i to department j
Figure 143110DEST_PATH_IMAGE015
Determining paths of ants to reach all departments by roulette
Figure 981753DEST_PATH_IMAGE016
；
A total time consumption determining module for determining the expected degree of transfer of each ant from department i to department j
Figure 482136DEST_PATH_IMAGE017
Determining the total time spent by each ant to reach all departments
Figure 13611DEST_PATH_IMAGE018
；
An optimal path determining module for completing the paths from the ants to all departments with the minimum total time consumption as the optimization target
Figure 356868DEST_PATH_IMAGE019
To determine the optimal path
Figure 123836DEST_PATH_IMAGE066
；
A pheromone intensity updating module for determining the optimal path
Figure 868938DEST_PATH_IMAGE067
Then, updating the intensity of the pheromone;
an iteration ending condition judging module for judging whether the iteration ending condition is satisfied, if so, the determined optimal path
Figure 571314DEST_PATH_IMAGE068
The department ranking with the least total time consumption of the personnel in the clinic is used for realizing the diagnosis guide, if not, the ant colony algorithm after the pheromone intensity is updated is continuously adopted to determine the optimal path
Figure 11654DEST_PATH_IMAGE069
。
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 7.
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