CN117236796B - CS-TOPSIS algorithm-based hospital logistics operation and maintenance evaluation method and system - Google Patents
CS-TOPSIS algorithm-based hospital logistics operation and maintenance evaluation method and system Download PDFInfo
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Abstract
The invention provides a hospital logistics operation and maintenance evaluation method and system based on a CS-TOPSIS algorithm, wherein hospital logistics data are acquired, and comprise static data and dynamic data; taking the hospital logistics data as an evaluation index in a way of calculation and direct use; constructing an evaluation matrix of each comparison object, constructing an adaptability function, and outputting and calculating an evaluation index weight vector through a cuckoo algorithm CS; carrying out calculation and evaluation by taking the calculation and evaluation index weight vector of the comparison object into a comprehensive evaluation model TOPSIS, obtaining a normalized score, and ranking; and is used to aid in decision making. The invention has high data accuracy, improves the reliability of the evaluation result, fully embodies the data value and practically assists the manager to make decisions.
Description
Technical Field
The invention belongs to the field of hospital logistics operation and maintenance, and particularly relates to a hospital logistics operation and maintenance evaluation method and system based on a CS-TOPSIS algorithm.
Background
The existing CS-TOPSIS algorithm-based hospital logistics operation and maintenance evaluation system has several defects:
(1) The evaluation process is strong in subjectivity and low in accuracy: at present, the evaluation mode of the logistic operation and maintenance effect of the hospital mainly designs an evaluation index scoring table for hospital management staff, subjective scoring is carried out by a management layer and staff in each department, the evaluation conclusion of the logistic operation and maintenance effect is obtained through score statistics, the problem that evaluation is too dependent on subjectivity is generated due to manual scoring, and data are manually processed, so that the accuracy of the data is low, and the scientificity of an evaluation result is directly influenced.
Based on the development of modern mathematical analysis methods, part of hospitals also carry out logistic operation and maintenance evaluation through mathematical methods, wherein a multi-objective decision method (AHP) is adopted, but a weighting method which takes subjective cognition of hospital experts as a core is adopted, and the problem of incomplete rationality of evaluation results is brought like manual subjective scoring.
(2) The existing objective evaluation mode is inconvenient to operate: whether the evaluation is performed by traditional personnel scoring or by an evaluation system developed by a modern mathematical analysis method, the problems of acquisition, arrangement and input of a large amount of logistic related data, which consume a large amount of manpower, are avoided, and the evaluation work efficiency is reduced. In addition, a large amount of parameter input is generated in the weighting process by adopting an analysis method based on AHP, so that the operation difficulty of an evaluation system is high, the personnel quality requirement is high, and the efficiency of the evaluation work is reduced.
Disclosure of Invention
The invention provides a hospital logistics operation and maintenance evaluation method and system based on a CS-TOPSIS algorithm, which have high data accuracy, improve the reliability of an evaluation result and improve the evaluation work efficiency.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a hospital logistics operation and maintenance evaluation method based on CS-TOPSIS algorithm comprises the following steps:
s1, acquiring hospital logistics data, wherein the hospital logistics data comprise static data and dynamic data; the static data are logistic management data obtained from a hospital management system, and the dynamic data are monitoring data obtained from hospital energy consumption detection equipment;
s2, taking the hospital logistics data as an evaluation index in a way of calculation and direct use; constructing an evaluation matrix of each comparison object, constructing an adaptability function, and outputting and calculating an evaluation index weight vector through a cuckoo algorithm CS; the comparison objects are a plurality of hospitals or different periods of one hospital;
and S3, carrying out calculation and evaluation by taking the calculation and evaluation index weight vector of the comparison object into a comprehensive evaluation model TOPSIS, obtaining a normalized score, and ranking.
Further, the step S2 specifically includes:
s201, setting n evaluation indexes in total and m comparison objects, and constructing an initial evaluation matrix A_in as follows:
；
normalizing the initial evaluation matrix A_in to obtain a normalized matrix A_no, wherein the processing method comprises the following steps:
；（i=1,2,...,m，j=1,2,...,n）；
wherein i represents the ith comparison object, j represents the jth evaluation index; a, a i,j An element representing an initial evaluation matrix a_in; a_no i,j Elements representing the normalized matrix a_no;
the normalized matrix A_no is forward processed to obtain an evaluation matrix A, and the processing method is as follows:
if A_no i,j Is of a very large scale, A i,j =A_no i,j ；
If A_no i,j Is a very small index, A i,j =1-A_no i,j ；
Wherein A is i,j Representing the elements of the evaluation matrix a;
s202, constructing a fitness function y:
the fitness function formula is as follows:
；（i=1,2,...,m，j=1,2,...,n）；
the j-th evaluation index weight;
s203, setting CS algorithm parameters including discovery probability, population scale and maximum iteration times;
s204, initializing a nest position and CS algorithm parameters, wherein the initialized nest position is randomly generatedEntering CS algorithm calculation; when the iteration number reaches the maximum iteration number, outputting the current +.>；
S205, outputting calculation evaluation index weight: the step S204 is performedThe normalization is carried out to obtain the calculated evaluation index weight, and the processing method is as follows:
；（j=1,2,...,n）；
and calculating the evaluation index weight representing the j-th evaluation index.
Further, the step S3 specifically includes:
s301, normalizing the evaluation matrix A to obtain a normalized decision matrix Z, wherein the processing method comprises the following steps:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is i,j An element representing a standardized decision matrix Z;
s302, recording a standardized decision matrix ZThe optimal value vector isThe worst value vector is +.>Wherein->，/>；
S303, calculating scores of all comparison objects and normalizing:
the distance between the ith comparison object and the optimal value vector is recorded asThe distance between the ith comparison object and the worst value vector is recorded as +.>；
The score of the ith comparison object isIts normalized score is->；
S304, ranking from high to low according to the normalized score of each comparison object.
The invention also provides a hospital logistics operation and maintenance evaluation system based on the CS-TOPSIS algorithm, which comprises a data acquisition module and a CS-TOPSIS algorithm server; the CS-TOPSIS algorithm server comprises a CS module and a TOPSIS module;
and a data acquisition module: acquiring hospital logistics data, wherein the hospital logistics data comprises static data and dynamic data; the static data are logistic management data obtained from a hospital management system, and the dynamic data are monitoring data obtained from hospital energy consumption detection equipment;
CS module: taking the hospital logistics data as an evaluation index in a way of calculation and direct use; constructing an evaluation matrix of each comparison object, constructing an adaptability function, and outputting and calculating an evaluation index weight vector through a cuckoo algorithm CS; the comparison objects are a plurality of hospitals or different periods of one hospital;
TOPSIS module: and (3) taking the evaluation index weight vector of the comparison object into a comprehensive evaluation model TOPSIS for calculation and evaluation, obtaining a normalized score, and ranking.
Further, the CS module includes:
matrix unit: let n evaluation indexes in total, m comparison objects, and construct an initial evaluation matrix a_in as follows:
；
normalizing the initial evaluation matrix A_in to obtain a normalized matrix A_no, wherein the normalization matrix A_no is processed as follows:
；（i=1,2,...,m，j=1,2,...,n）；
wherein i represents the ith comparison object, j represents the jth evaluation index; a, a i,j An element representing an initial evaluation matrix a_in; a_no i,j Elements representing the normalized matrix a_no;
the normalized matrix A_no is forward processed to obtain an evaluation matrix A, and the processing is as follows:
if A_no i,j Is of a very large scale, A i,j =A_no i,j ；
If A_no i,j Is a very small index, A i,j =1-A_no i,j ；
Wherein A is i,j Representing the elements of the evaluation matrix a;
fitness function unit: constructing a fitness function y:
the fitness function formula is as follows:
；（i=1,2,...,m，j=1,2,...,n）；
the j-th evaluation index weight;
parameter unit: setting CS algorithm parameters including discovery probability, population scale and maximum iteration times;
algorithm unit: initializing bird nest positions and CS algorithm parameters, wherein the initialized bird nest positions are generated randomlyEntering CS algorithm calculation; when the iteration number reaches the maximum iteration number, outputting the current +.>；
Weight vector unit: outputting the calculated evaluation index weight: the step S204 is performedThe normalization is carried out to obtain the calculated evaluation index weight, and the processing method is as follows:
；（j=1,2,...,n）；
and calculating the evaluation index weight representing the j-th evaluation index.
Still further, the TOPSIS module includes:
decision matrix unit: the evaluation matrix A is standardized to obtain a standardized decision matrix Z, and the processing is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is i,j An element representing a standardized decision matrix Z;
vector unit: recording deviceThe optimal value vector of the standardized decision matrix Z isThe worst value vector is +.>Wherein->，/>；
A calculation unit: calculating and normalizing the scores of the comparison objects:
the distance between the ith comparison object and the optimal value vector is recorded asThe distance between the ith comparison object and the worst value vector is recorded as +.>；
The score of the ith comparison object isIts normalized score is->；
Ranking unit: ranking from high to low is performed according to the normalized score of each comparison object.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention is based on CS-TOPSIS algorithm, overcomes the incomplete rationality problem brought by the traditional evaluation method taking hospital expert cognition as the core, and improves the effectiveness and scientificity of the evaluation result.
(2) The invention can help the hospital logistics management layer to be clear in the future of logistics operation and maintenance in the aspect of improvement and promotion, fully embody the data value and practically assist the manager to make decisions.
(3) The invention has less input parameters and fully and automatically acquires the data, thereby reducing the use difficulty of the system, being simple and convenient to operate, avoiding the process of manually acquiring the data, having high data accuracy and improving the reliability of the evaluation result.
Drawings
Fig. 1 is a schematic diagram of a system structure according to an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
For the purpose of making the objects and features of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
As shown in fig. 1, the arrangement of the present system is completed in a hospital:
(1) The intelligent electric meter is installed in each layer of the hospital and each loop of the transformer substation, the gas flowmeter is installed in the gas inlet pipeline, the intelligent water meter is installed in each layer of the hospital, the building seat inlet and the courtyard inlet water pipe, the cold and heat meters (all meters are remotely transmissible) are installed in the outlet main pipe of the hospital energy station, the RS485 line is arranged to be connected to the intelligent gateway equipment, the gateway equipment is arranged between weak current of the hospital, the gateway performs primary processing and protocol conversion of data, and the data is transmitted to the server through the network.
(2) And carrying out data butt joint with a hospital property management system, an attendance management system and an HRP system through an API interface, and transmitting the data butt joint to a server through a network.
(3) The CS-TOPSIS algorithm server is deployed in a hospital data room, comprises a database and a CS-TOPSIS evaluation algorithm application program, and is connected with a display, a mouse and a keyboard and the like. All the logistic data are automatically converted into digital indexes for the logistic evaluation calculation of the hospitals. The CS-TOPSIS evaluation algorithm application program comprises a cuckoo algorithm CS and a comprehensive evaluation model TOPSIS.
(4) The user's operation is completed on the user's display screen, the user inputs parameters on the display screen, and the user only needs to input 3 parameters (discovery probability, population scale, maximum iteration number) of the TOPSIS model, or does not input, keeps default parameters (discovery probability default 0.25, population scale default 20, iteration number default 1000), and enters calculation. After the CS-TOPSIS algorithm server calculates, a numerical table for checking evaluation indexes, comprehensive scores of the operation and maintenance of the hospital, a year-over-year comparison result of the operation and maintenance scores of the hospital, a comparison result of the operation and maintenance scores of the hospital and other hospitals at the same level, and auxiliary decision-making suggestions of the operation and maintenance safety, intelligence, energy conservation, convenience and high efficiency of the hospital can be displayed on a display screen.
The system comprises:
1. and (3) automatically acquiring data:
hospital logistics data includes static data and dynamic data. Dynamic data, namely monitoring data, are arranged at each monitoring point of a hospital, an intelligent ammeter, a gas flowmeter, an intelligent water meter, a cold and hot meter (which are all remotely transmissible) and the like, the intelligent ammeter, the gas flowmeter, the intelligent water meter and the cold and hot meter are connected to intelligent gateway equipment through an RS485 line transmission mode, the gateway performs preliminary processing and protocol conversion on the data, the data is transmitted to a server through a network, and specific data are shown in a table 1-1. The acquisition time interval of the monitoring data can be determined according to the actual requirements and the requirements of the monitoring system. In general, acquisition time intervals may vary from a few seconds to a few minutes, with shorter time intervals providing finer data, but also increasing the burden of data processing and storage. Longer time intervals can reduce the amount of data, but can lead to loss of information, and in actual engineering, the dynamic data of energy consumption is collected once in 15 minutes. The static data, namely service data, is subjected to data butt joint with a hospital property management system, an attendance management system, an HRP system and the like through an API interface, and is transmitted to a server through a network, and the specific data are shown in tables 1-2. The static monitoring data acquisition time interval is 24 hours. After data acquisition, the data processing is carried out on the dynamic and static data by adopting a plurality of means such as missing value cleaning, redundant field cleaning, missing content supplementing, recollection, relevance verification and the like, so that the accuracy of the data is ensured.
TABLE 1-1 dynamic energy consumption monitoring data
Table 1-2 hospital static monitoring data
2. CS-TOPSIS algorithm server:
the CS-TOPSIS algorithm server comprises a database and a CS-TOPSIS evaluation algorithm application program. The hospital logistics data are transmitted to a server, and a database is constructed through measures such as data cleaning and data processing. The project database is mainly used for storing data, the requirement analysis of an algorithm server is already carried out in the early stage of the project, and the relation and constraint conditions between dynamic and static data are determined. The first step of database construction is database conceptual design, and the conceptual model construction is carried out on the relation between the abstract database entity and the data by using an ER model; the second step is logic design, which converts the conceptual model into a logic model which can be understood by a database management system; the third step is to convert the logic model into an actual database, and determine the specific implementation details such as the storage structure, index, file organization mode and the like of the database; the fourth step is that the design of the database is converted into an actual database, objects such as a table, an index, a view and the like of the database are required to be created, and hospital logistics data are imported into the database; and finally, daily operation and maintenance and optimization are carried out, the running state of the database is monitored, abnormal faults are handled in time, and optimization measures are adopted to solve the performance problem by analyzing the service condition and performance bottleneck of the database.
The CS-TOPSIS evaluation algorithm application program embeds a CS-TOPSIS evaluation algorithm, namely, adopts a CS algorithm (a cuckoo algorithm) to objectively solve the weight of an evaluation index, and brings the obtained weight vector of the calculated evaluation index into a TOPSIS model for calculation evaluation, wherein the specific flow is as follows:
the first flow is to objectively solve the weight of the evaluation index by adopting a CS algorithm (cuckoo algorithm):
step 1: constructing an evaluation matrix A;
m comparison objects (m hospitals or a logistic operation and maintenance evaluation of one hospital for m years) are arranged;
providing n evaluation indexes (wherein the evaluation indexes are derived from the hospital logistics data and comprise obtained by directly using the hospital logistics data or calculating through the hospital logistics data);
examples: the partial evaluation index and the source are shown in the following table:
the initial evaluation matrix a_in may be constructed as follows:
；
normalizing the initial evaluation matrix A_in to obtain a normalized matrix A_no, wherein the processing method comprises the following steps:
；（i=1,2,...,m，j=1,2,...,n）；
wherein i represents the ith comparison object, j represents the jth evaluation index; a, a i,j An element representing an initial evaluation matrix a_in; a_no i,j Elements representing the normalized matrix a_no;
the normalized matrix A_no is forward processed to obtain an evaluation matrix A, and the processing method is as follows:
if A_no i,j Is of a very large scale, A i,j =A_no i,j ；
If A_no i,j Is a very small index, A i,j =1-A_no i,j ；
Wherein A is i,j Representing the elements of the evaluation matrix a; (i=1, 2,) m, j=1, 2, n);
step 2: constructing a fitness function y;
the fitness function formula is as follows:
；
（i=1,2,...,m，j=1,2,...,n）；
the j-th evaluation index weight;
step 3: setting CS algorithm parameters including discovery probability, population scale and maximum iteration times;
the discovery probability is a parameter for algorithm control elite selection and balancing global and local search; the population scale is related to the searching speed of the CS algorithm, and the larger the population scale is, the faster the searching speed is; the maximum iteration number is a stop condition parameter of the CS algorithm, and if the iteration number reaches the maximum iteration number, the algorithm stops and outputs a calculation result. Based on a large amount of experimental experience of the hospital logistics operation and maintenance evaluation index weighting scene, the probability is found to be 0.25, the population scale is 20, and a good searching effect can be obtained.
Step 4: entering CS algorithm calculation;
based on the steps, the input parameters of the CS algorithm are obtained, and the CS algorithm is entered for calculation. The CS algorithm calculation steps are not described here in detail, only the main calculation steps are listed: (1) Initializing bird nest positionIs->Randomly generating an initial value; initializing CS algorithm parameters; (2) Calculating an adaptability function, and reserving the current optimal nest position to the next generation; (3) Changing the position of the bird nest, and comparing the position with the position of the bird nest of the previous generation to obtain a more excellent position of the bird nest; (4) Generating a random number and comparing the random number with the discovery probability, if the random number is larger than the discovery probability, randomly changing the position of the bird nest, otherwise, not changing the position of the bird nest; (5) Comparing the nest positions before and after modification, and selecting an optimal nest position; (6) If the iteration number reaches the maximum iteration number, outputting the current +.>。
Step 5: outputting a calculated evaluation index weight;
the step 4 is carried outThe normalization is carried out to obtain the calculated evaluation index weight, and the processing method is as follows:
；（j=1,2,...,n）；
and calculating the evaluation index weight representing the j-th evaluation index.
Step two, the obtained calculated evaluation index weight vector is brought into a TOPSIS model for calculation evaluation:
in the first flow, the CS algorithm is adopted to obtain the calculated evaluation index weight vector, and the calculated evaluation index weight vector is brought into a formula when the distance between the comparison object and the optimal value vector and the distance between the comparison object and the worst value vector are calculated in the TOPSIS method. The TOPSIS model calculation method is as follows:
step 1: the evaluation matrix A is standardized to obtain a standardized decision matrix Z, and the processing method is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is i,j An element representing a standardized decision matrix Z;
step 2: the optimal value vector of the standardized decision matrix Z is recorded asThe worst value vector is +.>Wherein->，/>。
Step 3: the score of each comparison object is calculated and normalized.
The distance between the ith comparison object and the optimal value vector is recorded asThe distance between the ith comparison object and the worst value vector is recorded as +.>；
The score of the ith comparison object isIts normalized score is->。
Step 4: ranking from high to low according to the normalized score of each comparison object, the higher the score, the better the hospital logistics operation and maintenance effect of the comparison object.
The invention builds a server based on CS-TOPSIS algorithm, embeds CS-TOPSIS algorithm quotation program, can objectively determine the evaluation index weight by using CS algorithm, and calculates and evaluates by using TOPSIS model based on good and bad solution distance method, thereby overcoming the incomplete rationality problem brought by the traditional evaluation method taking hospital expert cognition as the core, and improving the effectiveness and scientificity of the evaluation result.
The invention also builds a decision suggestion module to convert the data into the suggestion of the auxiliary decision of the logistical operation and maintenance of the hospital, thereby solving the problem that the current logistical evaluation result cannot assist in decision making.
The invention only needs to input three weighted calculation parameters (discovery probability, population scale and maximum iteration number), or maintain the default setting of the system, and can perform evaluation calculation. Meanwhile, the system automatically acquires the logistic data required by all evaluation through the intelligent gateway and the API interface, and the convenience and accuracy of the evaluation process are improved.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (2)
1. The utility model provides a hospital logistics operation and maintenance evaluation method based on CS-TOPSIS algorithm, which is characterized by comprising the following steps:
s1, acquiring hospital logistics data, wherein the hospital logistics data comprise static data and dynamic data; the static data are logistic management data obtained from a hospital management system, and the dynamic data are monitoring data obtained from hospital energy consumption detection equipment;
s2, taking the hospital logistics data as an evaluation index in a way of calculation and direct use; constructing an evaluation matrix of each comparison object, constructing an adaptability function, and outputting and calculating an evaluation index weight vector through a cuckoo algorithm CS; the comparison objects are a plurality of hospitals or different periods of one hospital;
s3, carrying the calculated evaluation index weight vector of the comparison object into a comprehensive evaluation model TOPSIS for calculation evaluation, obtaining a normalized score, and ranking;
the step S2 specifically includes:
s201, setting n evaluation indexes in total and m comparison objects, and constructing an initial evaluation matrix A_in as follows:
；
normalizing the initial evaluation matrix A_in to obtain a normalized matrix A_no, wherein the processing method comprises the following steps:
；（i=1,2,...,m，j=1,2,...,n）；
wherein i represents the ith comparison object, j represents the jth evaluation index; a, a i,j An element representing an initial evaluation matrix a_in; a_no i,j Elements representing the normalized matrix a_no;
the normalized matrix A_no is forward processed to obtain an evaluation matrix A, and the processing method is as follows:
if A_no i,j Is of a very large scale, A i,j =A_no i,j ；
If A_no i,j Is a very small index, A i,j =1-A_no i,j ；
Wherein A is i,j Representing the elements of the evaluation matrix a;
s202, constructing a fitness function y:
the fitness function formula is as follows:
；（i=1,2,...,m，j=1,2,...,n）；
the j-th evaluation index weight;
s203, setting CS algorithm parameters including discovery probability, population scale and maximum iteration times;
s204, initializing a nest position and CS algorithm parameters, wherein the initialized nest position is randomly generatedEntering CS algorithm calculation; when the iteration number reaches the maximum iteration number, outputting the current +.>；
S205, outputting calculation evaluation index weight: the step S204 is performedNormalization results in computationThe evaluation index weight is processed by the following steps:
；（j=1,2,...,n）；
calculating an evaluation index weight representing a j-th evaluation index;
the step S3 specifically includes:
s301, normalizing the evaluation matrix A to obtain a normalized decision matrix Z, wherein the processing method comprises the following steps:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is i,j An element representing a standardized decision matrix Z;
s302, recording the optimal value vector of the standardized decision matrix Z asThe worst value vector isWherein->，/>；
S303, calculating scores of all comparison objects and normalizing:
the distance between the ith comparison object and the optimal value vector is recorded asThe distance between the ith comparison object and the worst value vector is recorded as +.>；
The score of the ith comparison objectIts normalized score is->；
S304, ranking from high to low according to the normalized score of each comparison object.
2. The hospital logistics operation and maintenance evaluation system based on the CS-TOPSIS algorithm is characterized by comprising a data acquisition module and a CS-TOPSIS algorithm server; the CS-TOPSIS algorithm server comprises a CS module and a TOPSIS module;
and a data acquisition module: acquiring hospital logistics data, wherein the hospital logistics data comprises static data and dynamic data; the static data are logistic management data obtained from a hospital management system, and the dynamic data are monitoring data obtained from hospital energy consumption detection equipment;
CS module: taking the hospital logistics data as an evaluation index in a way of calculation and direct use; constructing an evaluation matrix of each comparison object, constructing an adaptability function, and outputting and calculating an evaluation index weight vector through a cuckoo algorithm CS; the comparison objects are a plurality of hospitals or different periods of one hospital;
TOPSIS module: carrying the evaluation index weight vector of the comparison object into a comprehensive evaluation model TOPSIS for calculation and evaluation to obtain a normalized score for ranking;
the CS module includes:
matrix unit: let n evaluation indexes in total, m comparison objects, and construct an initial evaluation matrix a_in as follows:
；
normalizing the initial evaluation matrix A_in to obtain a normalized matrix A_no, wherein the normalization matrix A_no is processed as follows:
；（i=1,2,...,m，j=1,2,...,n）；
wherein i represents the ith comparison object, j represents the jth evaluation index; a, a i,j An element representing an initial evaluation matrix a_in; a_no i,j Elements representing the normalized matrix a_no;
the normalized matrix A_no is forward processed to obtain an evaluation matrix A, and the processing is as follows:
if A_no i,j Is of a very large scale, A i,j =A_no i,j ；
If A_no i,j Is a very small index, A i,j =1-A_no i,j ；
Wherein A is i,j Representing the elements of the evaluation matrix a;
fitness function unit: constructing a fitness function y:
the fitness function formula is as follows:
；（i=1,2,...,m，j=1,2,...,n）；
the j-th evaluation index weight;
parameter unit: setting CS algorithm parameters including discovery probability, population scale and maximum iteration times;
algorithm unit: initializing bird nest positions and CS algorithm parameters, wherein the initialized bird nest positions are generated randomlyEntering CS algorithm calculation; when the iteration number reaches the maximum iteration number, outputting the current +.>；
Weight vector unit: outputting the calculated evaluation index weight: the step S204 is performedThe normalization is carried out to obtain the calculated evaluation index weight, and the processing method is as follows:
；（j=1,2,...,n）；
calculating an evaluation index weight representing a j-th evaluation index;
the TOPSIS module comprises:
decision matrix unit: the evaluation matrix A is standardized to obtain a standardized decision matrix Z, and the processing is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is i,j An element representing a standardized decision matrix Z;
vector unit: the optimal value vector of the standardized decision matrix Z is recorded asThe worst value vector is +.>Wherein->，/>；
A calculation unit: calculating and normalizing the scores of the comparison objects:
the distance between the ith comparison object and the optimal value vector is recorded asThe distance between the ith comparison object and the worst value vector is recorded as +.>；
The score of the ith comparison objectIts normalized score is->；
Ranking unit: ranking from high to low is performed according to the normalized score of each comparison object.
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