CN115904912A - Intelligent information system efficiency evaluation method, system, computing device and storage medium - Google Patents
Intelligent information system efficiency evaluation method, system, computing device and storage medium Download PDFInfo
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Abstract
The application discloses an intelligent information system efficiency evaluation method, a system, a computing device and a storage medium, which relate to the technical field of computers, and the method comprises the following steps: acquiring an evaluation demand, and gradually decomposing the evaluation demand to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchy indexes and inclusion relationships between the hierarchy indexes; screening the hierarchy indexes, taking a plurality of screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; the index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes; assigning a corresponding index weight to each node of the index tree; generating a first evaluation questionnaire according to the terminal nodes of the index tree, and carrying out evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree; the method and the device have the effect of improving the accuracy of the evaluation result.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and a system for evaluating performance of an intelligent information system, a computing device, and a storage medium.
Background
The system efficiency refers to the ability of the system to achieve a specified use objective under specified conditions, the specified conditions refer to factors such as environmental conditions, time, personnel, use methods and the like, the specified use objective refers to the purpose to be achieved, the ability refers to the quantitative or qualitative degree of achieving the objective, and the system efficiency is a comprehensive reflection of the availability, credibility and inherent ability of the system.
At present, when a certain system is evaluated for performance, only the factors of the system are usually considered, that is, the evaluated related data comes from the system itself, and the source of the evaluated related data is single, so that the evaluated related data is difficult to sufficiently reflect the functions of the system, the final evaluation result is difficult to sufficiently reflect the performance of the system, and the evaluation result is not accurate enough.
Disclosure of Invention
In order to improve the accuracy of an evaluation result, the application provides an intelligent information system efficiency evaluation method, an intelligent information system efficiency evaluation system, a computing device and a storage medium.
In a first aspect, the present application provides an intelligent information system performance evaluation method, which adopts the following technical solution.
The intelligent information system efficiency evaluation method comprises the following steps,
acquiring an evaluation requirement, and gradually decomposing the evaluation requirement to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchy indexes and inclusion relationships between the hierarchy indexes;
screening the hierarchy indexes, taking a plurality of screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; the index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes;
assigning a corresponding index weight to each node of the index tree;
generating a first evaluation questionnaire according to the terminal nodes of the index tree, and carrying out evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value;
calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index;
acquiring multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data;
based on the evaluation requirement, constructing an evaluation tree according to a first target index, a second target index and a third target index corresponding to a root node of the index tree;
normalizing the first evaluation value, the second evaluation value and the third evaluation value to obtain a corresponding first evaluation value, a second evaluation value and a third evaluation value;
assigning a corresponding evaluation weight to each node of the evaluation tree;
calculating to obtain a validity value of each node of the evaluation tree according to the first evaluation value, the second evaluation value, the third evaluation value and the evaluation weight;
and generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
By adopting the technical scheme, when the efficiency evaluation is needed, firstly, the evaluation requirement is obtained, then, the evaluation requirement is gradually decomposed to obtain an index hierarchical structure, then, the hierarchical indexes are screened, a plurality of screened hierarchical indexes are used as first target indexes, an index tree is created according to the first target indexes, then, each node of the index tree is endowed with corresponding index weight, then, a first evaluation questionnaire is generated according to a terminal node of the index tree, evaluation is carried out according to the first evaluation questionnaire to obtain an evaluation value corresponding to the terminal node of the index tree, then, a first evaluation value corresponding to each node of the index tree is calculated according to the index tree, the index weight and the evaluation value, then, a scale generation interface is called to generate the evaluation scale, the evaluation is carried out according to the evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index, then multi-modal evaluation data are obtained, a third target index and a third evaluation value corresponding to the third target index are determined according to the multi-modal evaluation data, then an evaluation tree is constructed according to the first target index, the second target index and the third target index corresponding to a root node of the index tree based on evaluation requirements, then normalization processing is carried out on the first evaluation value, the second evaluation value and the third evaluation value to obtain a corresponding first evaluation value, a corresponding second evaluation value and a corresponding third evaluation value, then a corresponding evaluation weight is given to each node of the evaluation tree, then a validity value of each node of the evaluation tree is calculated according to the first evaluation value, the second evaluation value, the third evaluation value and the evaluation weight, and a validity evaluation report is generated according to the evaluation tree and each node of the evaluation tree. In the mode, the generated evaluation tree integrates the index tree, the evaluation result of the evaluation scale and multi-mode evaluation data, namely, the first target index, the second target index and the third target index, and the generation of the first target index, the second target index and the third target index is linked with the participation of evaluators, so that the limitation of the system is reduced, the efficiency evaluation report is more accurate to generate, and the accuracy of the evaluation result is improved.
Optionally, the calculating, according to the index tree, the index weight, and the measured value, a first evaluation value corresponding to each node of the index tree includes:
for any one terminal node X1 of the index tree,
S1＝W*X
wherein, S1 is a first evaluation value corresponding to a terminal node X1 of the index tree, W is an index weight corresponding to the terminal node X1 of the index tree, and X is an evaluation value corresponding to the terminal node X1 of the index tree;
for any one non-terminal node X2 of the index tree,
wherein S2 is a first evaluation value corresponding to the non-terminal node X2 of the index tree, n is the number of child nodes contained in the non-terminal node X2 of the index tree, W i Is an index weight corresponding to a child node included in a non-terminal node X2 of the index tree, X i And the evaluation value is a first evaluation value corresponding to a child node contained in the non-terminal node X2 of the index tree.
Optionally, the normalizing the first evaluation value, the second evaluation value, and the third evaluation value includes:
wherein Z is x Is one of the first evaluation value, the second evaluation value and the third evaluation value, Z' x Is to Z x And performing normalization processing on the values, wherein m is the total number of the first evaluation value, the second evaluation value and the third evaluation value, and y is a preset range value.
The intelligent information system efficiency evaluation method comprises the following steps:
acquiring an evaluation requirement, and gradually decomposing the evaluation requirement to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchy indexes and inclusion relationships between the hierarchy indexes;
screening the hierarchy indexes, taking a plurality of screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; the index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes;
assigning a corresponding index weight to each node of the index tree;
generating a first evaluation questionnaire according to the terminal nodes of the index tree, and carrying out evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value;
calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index;
based on the evaluation requirement, constructing an evaluation tree according to a first target index and a second target index corresponding to a root node of the index tree;
normalizing the first evaluation value and the second evaluation value to obtain a corresponding first evaluation value and a corresponding second evaluation value; assigning a corresponding evaluation weight to each node of the evaluation tree;
calculating to obtain a validity value of each node of the evaluation tree according to the first evaluation value, the second evaluation value and the evaluation weight;
and generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
By adopting the technical scheme, the generated evaluation tree integrates the index tree and the evaluation result of the evaluation scale, namely, the first target index and the second target index are integrated, and the generation of the first target index and the second target index is linked with the participation of evaluators, so that the limitation of the system is reduced, the efficiency evaluation report is more accurate, and the accuracy of the evaluation result is improved.
The intelligent information system efficiency evaluation method comprises the following steps:
acquiring an evaluation requirement, and gradually decomposing the evaluation requirement to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchy indexes and inclusion relationships between the hierarchy indexes;
screening the hierarchy indexes, taking a plurality of screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; the index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes;
assigning a corresponding index weight to each node of the index tree;
generating a first evaluation questionnaire according to the terminal nodes of the index tree, and carrying out evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value;
acquiring multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data;
based on the evaluation requirement, constructing an evaluation tree according to a first target index and a third target index corresponding to a root node of the index tree;
normalizing the first evaluation value and the third evaluation value to obtain a corresponding first evaluation value and a corresponding third evaluation value; assigning a corresponding evaluation weight to each node of the evaluation tree;
calculating to obtain the effectiveness value of each node of the evaluation tree according to the first evaluation value, the third evaluation value and the evaluation weight;
and generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
By adopting the technical scheme, the generated evaluation tree integrates the index tree and the multi-mode evaluation data, namely, the first target index and the third target index are integrated, and the generation of the first target index and the third target index is linked with the participation of evaluators, so that the limitation of the system is reduced, the efficiency evaluation report is more accurate, and the accuracy of the evaluation result is improved.
The intelligent information system efficiency evaluation method comprises the following steps:
calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index;
acquiring multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data;
based on the evaluation requirement, constructing an evaluation tree according to the second target index and the third target index;
normalizing the second evaluation value and the third evaluation value to obtain a corresponding second evaluation value and a corresponding third evaluation value; assigning a corresponding evaluation weight to each node of the evaluation tree;
calculating to obtain a validity value of each node of the evaluation tree according to the second evaluation value, the third evaluation value and the evaluation weight;
and generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
By adopting the technical scheme, the generated evaluation tree integrates the evaluation result of the evaluation scale and the multi-mode evaluation data, namely, the second target index and the third target index, and the generation of the second target index and the third target index is linked with the participation of evaluators, so that the limitation of the system is reduced, the efficiency evaluation report is more accurate, and the accuracy of the evaluation result is improved.
In a second aspect, the present application provides an intelligent information system performance evaluation system, which adopts the following technical solution.
An intelligent information system performance evaluation system comprising:
the evaluation demand decomposition module is used for acquiring evaluation demands and gradually decomposing the evaluation demands to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchy indexes and inclusion relationships between the hierarchy indexes;
the index tree generation module is used for screening the hierarchy indexes, taking the screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; wherein the index tree comprises the first target index and an inclusion relationship between the first target index;
an index weight giving module, configured to give a corresponding index weight to each node of the index tree;
the first evaluation module is used for generating a first evaluation questionnaire according to the terminal nodes of the index tree and carrying out evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
the first evaluation value generation module is used for calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value;
the second evaluation value generation module is used for calling a scale generation interface to generate a test and evaluation scale and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index;
the third evaluation value generation module is used for acquiring multi-modal data and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data;
the evaluation tree generation module is used for constructing an evaluation tree according to the first target index, the second target index and the third target index corresponding to the root node of the index tree based on the evaluation requirement;
the normalization processing module is used for performing normalization processing on the first evaluation value, the second evaluation value and the third evaluation value to obtain a corresponding first evaluation value, a corresponding second evaluation value and a corresponding third evaluation value;
the evaluation weight endowing module is used for endowing each node of the evaluation tree with corresponding evaluation weight;
the effectiveness value generation module is used for calculating the effectiveness value of each node of the evaluation tree according to the first evaluation value, the second evaluation value, the third evaluation value and the evaluation weight;
and the efficiency evaluation report generation module is used for generating an efficiency evaluation report according to the evaluation tree and the efficiency value corresponding to each node of the evaluation tree.
In a third aspect, the present application provides a computing device, which adopts the following technical solution.
A computing device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, the processor when executing the computer program implementing the method of any of the first aspects.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions.
A computer readable storage medium storing a computer program that can be loaded by a processor and executed to perform the method of any of the first aspects.
In summary, the present application at least includes the following beneficial technical effects: in the application, the generated evaluation tree integrates the index tree, the evaluation result of the evaluation scale and the multi-mode evaluation data, namely, the first target index, the second target index and the third target index, and the generation of the first target index, the second target index and the third target index is linked with the participation of evaluators, so that the limitation of the system is reduced, the efficiency evaluation report is more accurately generated, and the accuracy of the evaluation result is improved.
Drawings
FIG. 1 is a flow chart of one implementation of example 1 of the present application.
Fig. 2 is a diagram of a visual representation of embodiment 1 of the present application.
Fig. 3 is a flow chart of another embodiment of example 1 of the present application.
Fig. 4 is a flowchart of another embodiment of example 1 of the present application.
Fig. 5 is a flowchart of another embodiment of example 1 of the present application.
Fig. 6 is a block diagram of the structure of embodiment 2 of the present application.
Fig. 7 is a block diagram of the structure of embodiment 3 of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Example 1:
the embodiment of the application discloses an intelligent information system efficiency evaluation method.
Referring to fig. 1, the method for evaluating the performance of an intelligent information system includes the following steps:
s1, acquiring an evaluation requirement, and gradually decomposing the evaluation requirement to obtain an index hierarchical structure.
The index hierarchy structure comprises a plurality of hierarchy indexes and inclusion relations among the hierarchy indexes.
It should be noted that, in the process of decomposing the evaluation requirement, the evaluator may determine the decomposition direction through the function displayed by the evaluated system interface or by querying the system designer, etc., to know the index requirement of the evaluated system, and define and name the corresponding index according to the index requirement, so as to obtain the inclusion relationship between the plurality of level indexes and the level indexes, i.e., the index level structure.
And S2, screening the hierarchical indexes, taking a plurality of screened hierarchical indexes as first target indexes, and creating an index tree according to the first target indexes.
The index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes.
It should be noted that there are two screening methods, one is subjective screening, that is, an evaluator screens a hierarchical index having a relatively high correlation with an evaluation requirement according to experience of the evaluator and suggestions of experts, and the other is algorithm screening, that is, the evaluator inputs the hierarchical index into a system, and the system performs screening by using a built-in screening algorithm, where a common screening algorithm is the delphire algorithm.
And S3, endowing each node of the index tree with corresponding index weight.
It should be noted that there are two ways to assign the index weight, one is subjective weighting, that is, an evaluator assigns the corresponding index weight to a node of the index tree according to the importance degree of the first target index corresponding to the node of the index tree, and the other is algorithm weighting, and common weighting algorithms include a G1 method and a new G1 method.
It should be further explained that, in the index tree, the index weights corresponding to the child nodes belonging to the same node X should satisfy:
where i is the number of children of node X, W i And the index weight corresponding to the child node of the node X.
And S4, generating a first evaluation questionnaire according to the terminal nodes of the index tree, and evaluating according to the first evaluation questionnaire to obtain the evaluation values corresponding to the terminal nodes of the index tree.
It should be noted that the first evaluation questionnaire includes information of the first target index corresponding to the terminal node of the index tree, and in the evaluation process, the first target index is scored according to the importance degree of the first target index in the first evaluation questionnaire, and the scored score is the evaluation value.
And S5, calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the evaluation value.
Specifically, the calculating of the first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the evaluation value includes:
for any one terminal node X1 of the index tree,
S1＝W*X
wherein, S1 is a first evaluation value corresponding to a terminal node X1 of the index tree, W is an index weight corresponding to the terminal node X1 of the index tree, and X is an evaluation value corresponding to the terminal node X1 of the index tree;
for any one non-terminal node X2 of the index tree,
wherein S2 is a first evaluation value corresponding to the non-terminal node X2 of the index tree, n is the number of child nodes contained in the non-terminal node X2 of the index tree, and W i Is the index weight corresponding to the child node contained in the non-terminal node X2 of the index tree, X i Is the first evaluation value corresponding to the child node included in the non-terminal node X2 of the index tree.
And S6, calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index.
It should be noted that, in the evaluation system, tables such as a NASA _ TLX workload table, a SART situational awareness table, and a SUS availability table are built in, and after the table generation interface is called, the evaluation system generates a measurement and evaluation table according to the built-in tables; after the evaluation, the evaluation system processes the evaluation-related data to obtain a second target index and a corresponding second evaluation value.
And S7, acquiring multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data.
In this embodiment, the multi-modal evaluation data is generated by models such as a mathematical prototype, a VR prototype, a physical prototype, or a real prototype of the evaluated system; the evaluator can set the third target index and the index data corresponding to the third target index in a user-defined manner, and after the setting is completed, the evaluator calculates a third evaluation value corresponding to the third target index according to the index data corresponding to the third target index.
It should be further noted that the third target index includes comfort, visibility, accessibility, situational awareness, and the like, and the type of the index data corresponding to the third target index includes physiological, electroencephalogram, eye movement, interactive behavior, near infrared, environment, and the like.
And S8, constructing an evaluation tree according to the first target index, the second target index and the third target index corresponding to the root node of the index tree based on the evaluation requirement.
It should be noted that, in the process of constructing the evaluation tree, the first target index, the second target index and the third target index corresponding to the root node of the index tree are used as terminal nodes of the evaluation tree, and an evaluator can set non-terminal nodes of the evaluation tree based on evaluation requirements, for example, a combat effectiveness evaluation report of a human information physical system for unmanned aerial vehicle combat control has three sub-nodes of a human-computer cooperative combat capability coefficient, a situational awareness coefficient and an execution task coefficient, the human-computer cooperative combat capability coefficient has three sub-nodes of a system automation degree, a cognitive load degree, a training cost and an operation task efficiency, the situational awareness coefficient has a system SA and two sub-nodes of an operator SA, the execution task system has three sub-nodes of a system interference resistance, a system response time and a detection accuracy, and meanwhile, nine sub-nodes of the system automation degree, the cognitive load degree, the training cost, the operation task efficiency, the system SA, the operator SA, the system automation degree, the anti-interference system response time and the detection accuracy are all terminal nodes.
Step S9, performing normalization processing on the first evaluation value, the second evaluation value, and the third evaluation value to obtain a corresponding first evaluation value, a corresponding second evaluation value, and a corresponding third evaluation value.
Wherein, normalizing the first evaluation value, the second evaluation value and the third evaluation value includes:
wherein Z is x Z 'being one of the first evaluation value, the second evaluation value and the third evaluation value' x Is to Z x And (4) normalizing the values, wherein m is the total number of the first evaluation value, the second evaluation value and the third evaluation value, and y is a preset range value.
Y is a preset range value, Z' x Is between 0 and y. For example, when y is taken as 100, Z ′ x Is between 0 and 100.
It is further noted that each terminal node of the evaluation tree corresponds to one or more of the first evaluation value, the second evaluation value, and the third evaluation value.
And step S10, endowing each node of the evaluation tree with corresponding evaluation weight.
It should be noted that there are two ways to assign the evaluation weight, one is subjective weighting, and the other is algorithmic weighting, and the specific weighting requirement is the same as that in step S3.
And S11, calculating to obtain the effectiveness value of each node of the evaluation tree according to the first evaluation value, the second evaluation value, the third evaluation value and the evaluation weight.
Wherein, for any one terminal node Y1 of the evaluation tree,
E＝W*Y
for convenience of expression, the first evaluation value, the second evaluation value and the third evaluation value are collectively referred to as evaluation values; e is a validity value corresponding to a terminal node Y1 of the index tree, W is an evaluation weight corresponding to the terminal node Y1 of the index tree, and Y is an evaluation value corresponding to the terminal node Y1 of the index tree;
for any one non-terminal node Y2 of the evaluation tree,
wherein E is the validity value corresponding to the non-terminal node Y2 of the evaluation tree, n is the number of child nodes contained in the non-terminal node Y2 of the evaluation tree, and W i For evaluating evaluation weights corresponding to child nodes comprised by the non-terminal node Y2 of the tree, E i To evaluate the significance values corresponding to the child nodes contained in the non-terminal node Y2 of the tree.
And S12, generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
It can be understood that, for convenience of expression, the first target index, the second target index and the third target index corresponding to the node of the evaluation tree are collectively referred to as target indexes, and the efficiency evaluation report includes the target indexes and the effectiveness values corresponding to the target indexes; the performance evaluation report includes a visual representation such as a table, a bar chart, etc., and referring to fig. 2, the evaluator can understand the performance of the evaluated system through fig. 2.
In the above embodiment, when performance evaluation is required, an evaluation requirement is obtained, then the evaluation requirement is gradually decomposed to obtain an index hierarchical structure, then hierarchical indexes are screened, a plurality of screened hierarchical indexes are used as first target indexes, an index tree is created according to the first target indexes, then each node of the index tree is given a corresponding index weight, then a first evaluation questionnaire is generated according to a terminal node of the index tree, evaluation is performed according to the first evaluation questionnaire to obtain a measurement value corresponding to the terminal node of the index tree, then a first evaluation value corresponding to each node of the index tree is calculated according to the index tree, the index weights and the measurement values, then a scale generation interface is called to generate an evaluation scale, and the evaluation scale is performed according to the evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index, then multi-modal evaluation data are obtained, a third target index and a third evaluation value corresponding to the third target index are determined according to the multi-modal evaluation data, then an evaluation tree is constructed according to the first target index, the second target index and the third target index corresponding to a root node of the index tree based on evaluation requirements, then normalization processing is carried out on the first evaluation value, the second evaluation value and the third evaluation value to obtain a corresponding first evaluation value, a corresponding second evaluation value and a corresponding third evaluation value, then a corresponding evaluation weight is given to each node of the evaluation tree, then a validity value of each node of the evaluation tree is calculated according to the first evaluation value, the second evaluation value, the third evaluation value and the evaluation weight, and a validity evaluation report is generated according to the evaluation tree and each node of the evaluation tree. In the mode, the generated evaluation tree integrates the index tree, the evaluation result of the evaluation scale and multi-mode evaluation data, namely, the first target index, the second target index and the third target index, and the generation of the first target index, the second target index and the third target index is linked with the participation of evaluators, so that the limitation of the system is reduced, the efficiency evaluation report is more accurate to generate, and the accuracy of the evaluation result is improved.
Referring to fig. 3, as another embodiment of the performance evaluation method, the performance evaluation method includes the steps of:
and S20, acquiring the evaluation requirement, and gradually decomposing the evaluation requirement to obtain an index hierarchical structure.
The index hierarchy structure comprises a plurality of hierarchy indexes and inclusion relations among the hierarchy indexes.
And S21, screening the hierarchical indexes, taking a plurality of screened hierarchical indexes as first target indexes, and creating an index tree according to the first target indexes.
The index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes.
Step S22, corresponding index weight is given to each node of the index tree.
And S23, generating a first evaluation questionnaire according to the terminal nodes of the index tree, and evaluating according to the first evaluation questionnaire to obtain the evaluation values corresponding to the terminal nodes of the index tree.
And step S24, calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value.
And S25, calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index.
And S26, constructing an evaluation tree according to the first target index and the second target index corresponding to the root node of the index tree based on the evaluation requirement.
Step S27, perform normalization processing on the first evaluation value and the second evaluation value to obtain a corresponding first evaluation value and a corresponding second evaluation value.
And step S28, endowing each node of the evaluation tree with corresponding evaluation weight.
And S29, calculating to obtain the effectiveness value of each node of the evaluation tree according to the first evaluation value, the second evaluation value and the evaluation weight.
And step S30, generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
In the above embodiment, the generated evaluation tree fuses the index tree and the evaluation result of the evaluation scale, that is, the first target index and the second target index, and the generation of the first target index and the second target index is linked with the participation of the evaluator, so that the limitation of the system is reduced, the efficiency evaluation report is generated more accurately, and the accuracy of the evaluation result is improved.
Referring to fig. 4, as another embodiment of the performance evaluation method, the performance evaluation method includes the steps of:
and S40, acquiring the evaluation requirement, and gradually decomposing the evaluation requirement to obtain an index hierarchical structure.
The index hierarchy structure comprises a plurality of hierarchy indexes and inclusion relations among the hierarchy indexes.
And S41, screening the hierarchical indexes, taking a plurality of screened hierarchical indexes as first target indexes, and creating an index tree according to the first target indexes.
The index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes.
Step S42, corresponding index weight is given to each node of the index tree.
And S43, generating a first evaluation questionnaire according to the terminal nodes of the index tree, and evaluating according to the first evaluation questionnaire to obtain the evaluation values corresponding to the terminal nodes of the index tree.
And S44, calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value.
And S45, acquiring multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data.
And S46, constructing an evaluation tree according to the first target index and the third target index corresponding to the root node of the index tree based on the evaluation requirement.
Step S47, performing normalization processing on the first evaluation value and the third evaluation value to obtain a corresponding first evaluation value and a corresponding third evaluation value.
And step S48, endowing each node of the evaluation tree with corresponding evaluation weight.
And S49, calculating to obtain the effectiveness value of each node of the evaluation tree according to the first evaluation value, the third evaluation value and the evaluation weight.
And S50, generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
In the above embodiment, the generated evaluation tree integrates the index tree and the multi-modal evaluation data, that is, the first target index and the third target index are integrated, and the generation of the first target index and the third target index is linked with the participation of evaluators, so that the limitation of the system is reduced, the efficiency evaluation report is generated more accurately, and the accuracy of the evaluation result is improved.
Referring to fig. 5, as another embodiment of the performance evaluation method, the performance evaluation method includes the steps of:
and step S60, calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index.
Step S61, obtaining multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data.
And S62, constructing an evaluation tree according to the second target index and the third target index based on the evaluation requirement.
Step S63, performing normalization processing on the second evaluation value and the third evaluation value to obtain a corresponding second evaluation value and a corresponding third evaluation value.
And step S64, endowing each node of the evaluation tree with corresponding evaluation weight.
And step S65, calculating to obtain the effectiveness value of each node of the evaluation tree according to the second evaluation value, the third evaluation value and the evaluation weight.
And S66, generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
In the embodiment, the generated evaluation tree integrates the evaluation result of the evaluation scale and the multi-mode evaluation data, namely, the second target index and the third target index, and the generation of the second target index and the third target index is linked with the participation of evaluators, so that the limitation of the system is reduced, the efficiency evaluation report is more accurately generated, and the accuracy of the evaluation result is improved.
Example 2:
the embodiment of the application discloses an intelligent information system efficiency evaluation system.
Referring to fig. 6, the intelligent information system performance evaluation system includes:
the evaluation demand decomposition module 1 is used for acquiring evaluation demands and decomposing the evaluation demands step by step to obtain an index hierarchical structure; the index hierarchical structure comprises a plurality of hierarchical indexes and inclusion relations among the hierarchical indexes;
the index tree generation module 2 is used for screening the hierarchical indexes, taking the screened hierarchical indexes as first target indexes, and creating an index tree according to the first target indexes; the index tree comprises a first target index and an inclusion relation between the first target indexes;
an index weight assigning module 3, configured to assign a corresponding index weight to each node of the index tree;
the first evaluation module 4 is configured to generate a first evaluation questionnaire according to the terminal nodes of the index tree, and perform evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
the first evaluation value generation module 5 is configured to calculate a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight, and the measured evaluation value;
the second evaluation value generation module 6 is used for calling the scale generation interface, generating a test and evaluation scale comprising a plurality of second target indexes, and performing test and evaluation according to the test and evaluation scale to obtain a second evaluation value corresponding to the second target index;
the third evaluation value generation module 7 is configured to acquire multi-modal data, and determine a third target index and a third evaluation value corresponding to the third target index according to the multi-modal data;
the evaluation tree generation module 8 is used for constructing an evaluation tree according to a first target index, a second target index and a third target index corresponding to a root node of the index tree based on the evaluation requirement;
a normalization processing module 9, configured to perform normalization processing on the first evaluation value, the second evaluation value, and the third evaluation value to obtain a corresponding first evaluation value, a corresponding second evaluation value, and a corresponding third evaluation value;
an evaluation weight assigning module 10, configured to assign a corresponding evaluation weight to each node of the evaluation tree;
the validity value generation module 11 is configured to calculate a validity value of each node of the evaluation tree according to the first evaluation value, the second evaluation value, the third evaluation value and the evaluation weight;
and a performance evaluation report generating module 12, configured to generate a performance evaluation report according to the evaluation tree and the validity value corresponding to each node of the evaluation tree.
The intelligent information system efficiency evaluation system can realize any one of the intelligent information system efficiency evaluation methods, and the specific working process of the intelligent information system efficiency evaluation system can refer to the corresponding process in the intelligent information system efficiency evaluation method.
Example 3:
the embodiment of the application discloses a computing device.
Referring to fig. 7, a computing device comprises a memory having stored thereon a computer program operable on a processor, and a processor that, when executing the computer program, implements the method of any of the above.
Example 4:
the embodiment of the application discloses a computer readable storage medium.
A computer-readable storage medium storing a computer program that can be loaded by a processor and executes any of the methods described above, the computer-readable storage medium of this embodiment comprising: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The foregoing is a preferred embodiment of the present application and is not intended to limit the scope of the application in any way, and any features disclosed in this specification (including the abstract and drawings) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Claims (9)
1. The intelligent information system performance evaluation method is characterized by comprising the following steps,
acquiring an evaluation demand, and gradually decomposing the evaluation demand to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchical indexes and inclusion relationships between the hierarchical indexes;
screening the hierarchy indexes, taking a plurality of screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; the index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes;
assigning a corresponding index weight to each node of the index tree;
generating a first evaluation questionnaire according to the terminal nodes of the index tree, and evaluating according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value;
calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index;
acquiring multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data;
based on the evaluation requirement, constructing an evaluation tree according to a first target index, a second target index and a third target index corresponding to a root node of the index tree;
normalizing the first evaluation value, the second evaluation value and the third evaluation value to obtain a corresponding first evaluation value, a second evaluation value and a third evaluation value;
assigning a corresponding evaluation weight to each node of the evaluation tree;
calculating to obtain a validity value of each node of the evaluation tree according to the first evaluation value, the second evaluation value, the third evaluation value and the evaluation weight;
and generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
2. The method according to claim 1, wherein the calculating a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the evaluation value comprises:
for any one terminal node X1 of the metric tree,
S1＝W*X
the method comprises the following steps that S1 is a first evaluation value corresponding to a terminal node X1 of an index tree, W is an index weight corresponding to the terminal node X1 of the index tree, and X is an evaluation value corresponding to the terminal node X1 of the index tree;
for any non-terminal node X2 of the index tree,
wherein S2 is a first evaluation value corresponding to the non-terminal node X2 of the index tree, n is the number of child nodes contained in the non-terminal node X2 of the index tree, W i The index weight corresponding to the child node contained in the non-terminal node X2 of the index tree is X i And the evaluation value is a first evaluation value corresponding to a child node contained in the non-terminal node X2 of the index tree.
3. The intelligent information system performance evaluation method according to claim 1, wherein the normalizing the first, second, and third evaluation values comprises:
wherein, Z x Is one of the first evaluation value, the second evaluation value and the third evaluation value, Z' x Is to Z x And normalizing the value, wherein m is the total number of the first evaluation value, the second evaluation value and the third evaluation value, and y is a preset range value.
4. The intelligent information system efficiency evaluation method is characterized by comprising the following steps:
acquiring an evaluation requirement, and gradually decomposing the evaluation requirement to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchy indexes and inclusion relationships between the hierarchy indexes;
screening the hierarchy indexes, taking a plurality of screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; the index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes;
assigning a corresponding index weight to each node of the index tree;
generating a first evaluation questionnaire according to the terminal nodes of the index tree, and carrying out evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value;
calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index;
based on the evaluation requirement, constructing an evaluation tree according to a first target index and a second target index corresponding to a root node of the index tree;
normalizing the first evaluation value and the second evaluation value to obtain a corresponding first evaluation value and a corresponding second evaluation value; assigning a corresponding evaluation weight to each node of the evaluation tree;
calculating to obtain the effectiveness value of each node of the evaluation tree according to the first evaluation value, the second evaluation value and the evaluation weight;
and generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
5. The intelligent information system efficiency evaluation method is characterized by comprising the following steps:
acquiring an evaluation requirement, and gradually decomposing the evaluation requirement to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchy indexes and inclusion relationships between the hierarchy indexes;
screening the hierarchy indexes, taking a plurality of screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; the index tree comprises a plurality of first target indexes and inclusion relations among the first target indexes;
assigning a corresponding index weight to each node of the index tree;
generating a first evaluation questionnaire according to the terminal nodes of the index tree, and carrying out evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
calculating to obtain a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value;
acquiring multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data;
based on the evaluation requirement, constructing an evaluation tree according to a first target index and a third target index corresponding to a root node of the index tree;
normalizing the first evaluation value and the third evaluation value to obtain a corresponding first evaluation value and a corresponding third evaluation value; assigning a corresponding evaluation weight to each node of the evaluation tree;
calculating to obtain the effectiveness value of each node of the evaluation tree according to the first evaluation value, the third evaluation value and the evaluation weight;
and generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
6. The intelligent information system efficiency evaluation method is characterized by comprising the following steps:
calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index;
acquiring multi-modal evaluation data, and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-modal evaluation data;
constructing an evaluation tree according to the second target index and the third target index based on the evaluation requirement;
normalizing the second evaluation value and the third evaluation value to obtain a corresponding second evaluation value and a corresponding third evaluation value; assigning a corresponding evaluation weight to each node of the evaluation tree;
calculating to obtain a validity value of each node of the evaluation tree according to the second evaluation value, the third evaluation value and the evaluation weight;
and generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
7. Intelligent information system efficiency evaluation system, its characterized in that includes:
the evaluation demand decomposition module (1) is used for acquiring evaluation demands and decomposing the evaluation demands step by step to obtain an index hierarchical structure; wherein the index hierarchy comprises a number of hierarchical indexes and inclusion relationships between the hierarchical indexes;
the index tree generation module (2) is used for screening the hierarchy indexes, taking the screened hierarchy indexes as first target indexes, and creating an index tree according to the first target indexes; wherein the index tree comprises the first target index and an inclusion relationship between the first target index;
an index weight assignment module (3) for assigning a corresponding index weight to each node of the index tree;
the first evaluation module (4) is used for generating a first evaluation questionnaire according to the terminal nodes of the index tree and carrying out evaluation according to the first evaluation questionnaire to obtain evaluation values corresponding to the terminal nodes of the index tree;
a first evaluation value generation module (5) for calculating a first evaluation value corresponding to each node of the index tree according to the index tree, the index weight and the measured evaluation value;
the second evaluation value generation module (6) is used for calling a scale generation interface to generate a test and evaluation scale, and performing test and evaluation according to the test and evaluation scale to obtain a second target index and a second evaluation value corresponding to the second target index;
the third evaluation value generation module (7) is used for acquiring multi-mode data and determining a third target index and a third evaluation value corresponding to the third target index according to the multi-mode evaluation data;
an evaluation tree generation module (8) for constructing an evaluation tree according to the first target index, the second target index and the third target index corresponding to the root node of the index tree based on the evaluation requirement;
a normalization processing module (9) for performing normalization processing on the first evaluation value, the second evaluation value and the third evaluation value to obtain a corresponding first evaluation value, a corresponding second evaluation value and a corresponding third evaluation value;
an evaluation weight assignment module (10) for assigning a corresponding evaluation weight to each node of the evaluation tree;
a validity value generation module (11) for calculating a validity value of each node of the evaluation tree according to the first evaluation value, the second evaluation value, the third evaluation value and the evaluation weight;
a performance evaluation report generation module (12) for generating a performance evaluation report according to the evaluation tree and the effectiveness value corresponding to each node of the evaluation tree.
8. A computing device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, the processor implementing the method of any of claims 1 to 7 when executing the computer program.
9. A computer-readable storage medium characterized by: a computer program loadable by a processor and adapted to perform the method of any of claims 1 to 7.
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