CN111625526A - Fuzzy data processing method and system and terminal equipment - Google Patents

Fuzzy data processing method and system and terminal equipment Download PDF

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CN111625526A
CN111625526A CN202010460291.8A CN202010460291A CN111625526A CN 111625526 A CN111625526 A CN 111625526A CN 202010460291 A CN202010460291 A CN 202010460291A CN 111625526 A CN111625526 A CN 111625526A
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赵士欣
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Shijiazhuang Tiedao University
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Abstract

The invention is suitable for the technical field of data processing, and provides a fuzzy data processing method, a system and a terminal device, wherein the method comprises the following steps: acquiring the type of the fuzzy data to be processed and the number of determinant values of the fuzzy data to be processed, and determining input reduced values matched with the number of determinant values according to the type of the fuzzy data to be processed and the number of determinant values of the fuzzy data to be processed; inputting each input reduction value into a corresponding preset model after training respectively to obtain an output reduction value corresponding to each input reduction value respectively; and respectively calculating each output reduced value according to the inverse process of the calculation of the corresponding reduced value to obtain the prediction data of the fuzzy data to be processed. According to the method, the reversible characteristic of the reduced value is utilized, the predicted data can be obtained through direct calculation of the output reduced value, approximate calculation is avoided, too many manual intervention components are not needed, fuzzy data are not needed to be deformed into a series of interval values, information loss is prevented, and the accuracy of the result can be improved.

Description

Fuzzy data processing method and system and terminal equipment
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a fuzzy data processing method, a fuzzy data processing system and terminal equipment.
Background
In the data processing process, situations of processing fuzzy data are often encountered. If the fuzzy data is directly replaced by the clear value, a large amount of information is easily lost.
At present, the fuzzy data is generally processed by adopting an alpha-truncation method. However, after the fuzzy data is subjected to alpha-intercept processing, the fuzzy data is transformed into a series of interval values, the lost information is excessive, the alpha values are selected, the manual intervention components are excessive, and finally, the interval values are restored into the fuzzy values by adopting approximate calculation, so that the result accuracy is low.
Disclosure of Invention
In view of this, embodiments of the present invention provide a fuzzy data processing method, system and terminal device, so as to solve the problem in the prior art that the result accuracy is low due to more lost information, too much manual intervention and adoption of approximate calculation.
A first aspect of an embodiment of the present invention provides a fuzzy data processing method, including:
acquiring the type of the fuzzy data to be processed and the number of determinant values of the fuzzy data to be processed, and determining input reduced values matched with the number of determinant values according to the type of the fuzzy data to be processed and the number of determinant values of the fuzzy data to be processed;
inputting each input reduction value into a corresponding preset model after training respectively to obtain an output reduction value corresponding to each input reduction value respectively;
and respectively calculating each output reduced value according to the inverse process of the calculation of the corresponding reduced value to obtain the prediction data of the fuzzy data to be processed.
A second aspect of an embodiment of the present invention provides a fuzzy data processing system, including:
the input reduced value determining module is used for acquiring the type of the fuzzy data to be processed and the number of the determinant values of the fuzzy data to be processed, and determining input reduced values matched with the number of the determinant values according to the type of the fuzzy data to be processed and the number of the determinant values of the fuzzy data to be processed;
the output reduction value determining module is used for respectively inputting each input reduction value into the corresponding trained preset model to obtain the output reduction value corresponding to each input reduction value;
and the prediction data determining module is used for calculating each output reduced value according to the inverse process of the reduced value calculation corresponding to each output reduced value to obtain the prediction data of the fuzzy data to be processed.
A third aspect of the embodiments of the present invention provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the fuzzy data processing method according to the first aspect when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by one or more processors, implements the steps of the fuzzy data processing method according to the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: according to the embodiment of the invention, firstly, input reduced values matched with the number of decision values are determined according to the type of fuzzy data to be processed and the number of the decision values of the fuzzy data to be processed; then, inputting each input reduction value into the corresponding trained preset model respectively to obtain the output reduction value corresponding to each input reduction value respectively; and finally, calculating each output reduction value according to the inverse process of the calculation of the corresponding reduction value to obtain the prediction data of the fuzzy data to be processed. According to the embodiment of the invention, the reversible characteristic of the reduced value is utilized, the predicted data can be obtained by directly calculating the output reduced value, approximate calculation is avoided, too many manual intervention components are not needed, the fuzzy data is not needed to be deformed into a series of interval values, information loss is prevented, the accuracy of the result can be improved, and the calculation process is simple.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation of a fuzzy data processing method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a fuzzy data processing system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of an implementation of a fuzzy data processing method according to an embodiment of the present invention, and for convenience of description, only a part related to the embodiment of the present invention is shown. The execution main body of the embodiment of the invention can be terminal equipment.
As shown in fig. 1, the fuzzy data processing method may include the following steps:
s101: the method comprises the steps of obtaining the type of fuzzy data to be processed and the number of determinant values of the fuzzy data to be processed, and determining input reduced values matched with the number of determinant values according to the type of the fuzzy data to be processed and the number of determinant values of the fuzzy data to be processed.
In the embodiment of the invention, firstly, to-be-processed fuzzy data are obtained, the type of the to-be-processed fuzzy data and the number of the determinant values of the to-be-processed fuzzy data are determined, and then the input reduction value corresponding to the to-be-processed fuzzy data matched with the number of the determinant values of the to-be-processed fuzzy data is determined according to the to-be-processed fuzzy data, the type of the to-be-processed fuzzy data and the number of the determinant values of the to-be-processed fuzzy data.
In one embodiment of the present invention, the "determining the input reduced value matching with the number of decision values according to the type of the fuzzy data to be processed and the number of decision values of the fuzzy data to be processed" in S101 may include the steps of:
if the type of the fuzzy data to be processed is exponential fuzzy data, the determined value of the fuzzy data to be processed is one, and the input reduced value of the fuzzy data to be processed is the expected value of the fuzzy data to be processed;
if the type of the fuzzy data to be processed is normal fuzzy data, determining values of the fuzzy data to be processed are two, and input reduction values of the fuzzy data to be processed are an expected value and a variance of the fuzzy data to be processed respectively;
if the type of the fuzzy data to be processed is triangular fuzzy data, the number of the determined values of the fuzzy data to be processed is three, and the input brief values of the fuzzy data to be processed are an expected value, an optimistic key value and a pessimistic key value of the fuzzy data to be processed respectively;
if the type of the fuzzy data to be processed is trapezoidal fuzzy data, the decision values of the fuzzy data to be processed are four, and the input brief values of the fuzzy data to be processed are respectively an optimistic mean value, a pessimistic mean value, an optimistic key value and a pessimistic key value of the fuzzy data to be processed.
Specifically, if the fuzzy data to be processed is exponential fuzzy data, the number of the determined values is one, correspondingly, the input reduction value is also one, and the expected value is selected as the input reduction value; if the fuzzy data to be processed is normal fuzzy data, determining the number of values to be two, correspondingly, inputting two simple values, and selecting the expected value and the variance as the input simple values; if the fuzzy data to be processed is triangular fuzzy data, the number of the determined values is three, correspondingly, the number of the input simple values is also three, and the expected value, the optimistic key value and the pessimistic key value are selected as the input simple values; if the fuzzy data to be processed is trapezoidal fuzzy data, the number of the determined values is four, correspondingly, the number of the input brief values is also four, and an optimistic mean value, a pessimistic mean value, an optimistic key value and a pessimistic key value are selected as the input brief values.
In one embodiment of the invention, the type is an expectation value E of the fuzzy data to be processed of the exponential fuzzy data1(ξ) is E1(ξ) 1/(2 λ), where λ is the fuzzy data to be processed whose type is exponential fuzzy dataThe decision value of (a);
expectation value E of to-be-processed fuzzy data with type of normal fuzzy data2(ξ) is E2Mu in (ξ), and V (ξ) is sigma-sigma, and the variance V (ξ) of the blur data to be processed is normal type of blur data2(ii) a Wherein, mu and sigma are both determinant values of the fuzzy data to be processed with the type of normal fuzzy data;
expected value E of to-be-processed fuzzy data with type of triangular fuzzy data3(ξ) is:
Figure BDA0002510738850000051
Figure BDA0002510738850000052
optimistic key value of fuzzy data to be processed with type of triangular fuzzy data
Figure BDA0002510738850000053
Comprises the following steps:
Figure BDA0002510738850000054
pessimistic key value CV of to-be-processed fuzzy data with type of triangular fuzzy data3*(ξ) is CV3*(ξ)＝a2/(1+a2-a1) (ii) a Wherein, a1、a2And a3All the fuzzy data are determinant values of the fuzzy data to be processed with the type of triangular fuzzy data;
optimistic mean value E of fuzzy data to be processed with type of trapezoidal fuzzy data*(ξ) is E*(ξ)＝(b1+b2) Per 2, pessimistic mean value E of to-be-processed fuzzy data with type of trapezoid fuzzy data*(ξ) is E*(ξ)＝(b3+b4) Per 2, optimistic key value of fuzzy data to be processed with type of trapezoidal fuzzy data
Figure BDA0002510738850000055
Comprises the following steps:
Figure BDA0002510738850000056
type is trapezoidal fuzzyPessimistic key value CV of data to-be-processed fuzzy data4*(ξ) is CV4*(ξ)＝b2/(1+b2-b1) (ii) a Wherein, b1、b2、b3And b4All are decision values of the fuzzy data to be processed with the type of trapezoidal fuzzy data.
Specifically, if the type of the fuzzy data to be processed is exponential fuzzy data, the probability distribution is as follows: mu.s1ξ(x)＝e-λx，x∈R+(ii) a Accordingly, the formula for inputting the reduced value, i.e., the desired value, is: e1And (ξ) is 1/(2 lambda), wherein lambda is a determined value of the fuzzy data to be processed with the type of exponential fuzzy data.
If the type of the fuzzy data to be processed is normal fuzzy data, the probability distribution is as follows:
Figure BDA0002510738850000057
x∈[0，1](ii) a Accordingly, the calculation formulas for inputting the reduced values, which are the expected value and the variance, respectively, are: e2(ξ)＝μ，V(ξ)＝σ2(ii) a Wherein, mu and sigma are both the determinant values of the fuzzy data to be processed with the type of normal fuzzy data.
If the type of the fuzzy data to be processed is triangular fuzzy data, the probability distribution is as follows:
Figure BDA0002510738850000058
correspondingly, the input simple values are respectively an expected value, an optimistic key value and a pessimistic key value, and the calculation formulas are respectively as follows:
Figure BDA0002510738850000059
Figure BDA00025107388500000510
CV3*(ξ)＝a2/(1+a2-a1) (ii) a Wherein, a1、a2And a3All are decision values of the fuzzy data to be processed with the type of triangular fuzzy data.
If fuzzy data is to be processedIf the type is trapezoidal fuzzy data, the probability distribution is as follows:
Figure BDA0002510738850000061
correspondingly, the input simple values are respectively an optimistic mean value, a pessimistic mean value, an optimistic key value and a pessimistic key value, and the calculation formulas are respectively as follows: e*(ξ)＝(b1+b2)/2，E*(ξ)＝(b3+b4)/2，
Figure BDA0002510738850000062
CV4*(ξ)＝b2/(1+b2-b1) (ii) a Wherein, b1、b2、b3And b4All are decision values of the fuzzy data to be processed with the type of trapezoidal fuzzy data.
S102: and respectively inputting each input reduction value into the corresponding trained preset model to obtain the output reduction value corresponding to each input reduction value.
In the embodiment of the invention, each input reduction value of each type of fuzzy data corresponds to a preset model. For example, for the triangular fuzzy data, the input reduction values are 3, and each input reduction value corresponds to one preset model, that is, there are 3 different preset models for the triangular fuzzy data. The preset model may be any model that can implement a corresponding function in machine learning, such as a neural network model.
And inputting the input reduced value into the corresponding trained preset model to obtain an output reduced value corresponding to the input reduced value.
S103: and respectively calculating each output reduced value according to the inverse process of the calculation of the corresponding reduced value to obtain the prediction data of the fuzzy data to be processed.
In an embodiment of the present invention, the step S103 may include the following steps:
if the type of the fuzzy data to be processed is exponential fuzzy data, the prediction data lambda' of the fuzzy data to be processed is as follows: λ '═ 1/(2E'1(ξ)), wherein E'1(ξ) outputting a reduced value for the fuzzy data to be processed, which is of the type of the exponential fuzzy data;
if the type of the fuzzy data to be processed is normal fuzzy data, the prediction data mu 'and sigma' of the fuzzy data to be processed are respectively: mu '═ E'2(ξ)，σ′2V '(ξ); wherein E'2(ξ) and V' (ξ) are both output reduction values of the fuzzy data to be processed with the type of normal fuzzy data;
if the type of the fuzzy data to be processed is triangular fuzzy data, prediction data a 'of the fuzzy data to be processed'1、a′2And a'3Respectively as follows: a'1＝D1/D，a′2＝D2/D，a′3＝D3D; wherein, D, D1、D2And D3Intermediate parameters corresponding to the fuzzy data to be processed with the type of triangular fuzzy data,
Figure BDA0002510738850000071
Figure BDA0002510738850000072
Figure BDA0002510738850000073
Figure BDA0002510738850000074
E′3(ξ)、
Figure BDA0002510738850000075
and CV'3*(ξ) the output reduced values of the fuzzy data to be processed are all triangular fuzzy data;
if the type of the fuzzy data to be processed is trapezoidal fuzzy data, predicting data b 'of the fuzzy data to be processed'1、b′2、b′3And b'4Respectively as follows: b'1＝D′1/D′，b′2＝D′2/D′，b′3＝D′3/D′，b′4＝D′4a,/D'; wherein, D 'and D'1、D′2、D′3And D'4Intermediate parameters corresponding to fuzzy data to be processed with the type of trapezoidal fuzzy data,
Figure BDA0002510738850000076
Figure BDA0002510738850000077
Figure BDA0002510738850000081
Figure BDA0002510738850000082
Figure BDA0002510738850000083
E*′(ξ)、E′*(ξ)、
Figure BDA0002510738850000084
and CV'4*(ξ) are output reduction values of the blur data to be processed, each of which is of the type of trapezoidal blur data.
In the embodiment of the invention, because the calculation process of the reduced value is reversible, the prediction data of the fuzzy data to be processed can be calculated according to the output reduced value according to the inverse process of the calculation process of the reduced value. The prediction data is prediction data of a decision value of the fuzzy data to be processed, and a clear value of the fuzzy data to be processed can be obtained according to the prediction data and the probability distribution of the fuzzy data to be processed.
Specifically, if the type of the fuzzy data to be processed is exponential fuzzy data, the prediction data λ' of the fuzzy data to be processed is prediction data of a determination value λ thereof; if the type of the fuzzy data to be processed is normalIf the fuzzy data is fuzzy data, the prediction data mu 'and sigma' of the fuzzy data to be processed are prediction data of the determinacy values mu and sigma respectively; if the type of the fuzzy data to be processed is triangular fuzzy data, prediction data a 'of the fuzzy data to be processed'1、a′2And a'3Respectively, determine the value a1、a2And a3The prediction data of (2); if the type of the fuzzy data to be processed is trapezoidal fuzzy data, predicting data b 'of the fuzzy data to be processed'1、b′2、b′3And b'4Respectively, is its determined value b1、b2、b3And b4The prediction data of (1).
In an embodiment of the present invention, before S102, the following steps may be further included:
acquiring training sample data corresponding to each type of fuzzy data, wherein the training sample data comprises a plurality of training fuzzy data and training clear data corresponding to each training fuzzy data;
calculating an input training reduced value of each training fuzzy data in each training sample data and an output training reduced value of each corresponding training clear data according to the type of the fuzzy data corresponding to each training sample data;
and training preset models respectively corresponding to the brief values of the fuzzy data of each type according to the input training brief values and the output training brief values corresponding to the input training brief values of the fuzzy data of each training sample data to obtain the trained preset models corresponding to the brief values of the fuzzy data of each type.
The training process is described by taking triangular fuzzy data as an example. Firstly, training sample data of triangular fuzzy data is obtained, wherein the training sample data comprises a plurality of triangular training fuzzy data and clear training data corresponding to the triangular training fuzzy data. The data contained in the training sample data are decision values corresponding to the training fuzzy data or the training clear data.
Then, based on the training sample data, input training brief values of training fuzzy data of the respective triangles (formula calculation expectation value, optimistic key value, and pessimistic key value, which calculate the input brief values based on the aforementioned triangular fuzzy data) and output training brief values of training clear data of the respective triangles (formula calculation expectation value, optimistic key value, and pessimistic key value, which calculate the input brief values based on the aforementioned triangular fuzzy data) are calculated.
Finally, taking input training brief values belonging to one class as the input of a corresponding preset model, taking output training brief values corresponding to the input training brief values as the output of the preset model, training the preset model by adopting the existing training method to obtain the trained preset model, and specifically, training the preset model corresponding to the expected values of the triangular fuzzy data by using expected values of a plurality of input training brief values and expected values of a plurality of corresponding output training brief values; training a preset model corresponding to the optimistic key value of the triangular fuzzy data through the optimistic key values of the input training reduced values and the optimistic key values of the output training reduced values; and training a preset model corresponding to the pessimistic key value of the triangular fuzzy data through the pessimistic key values of the plurality of input training brief values and the pessimistic key values of the plurality of output training brief values.
The training process of other types of fuzzy data is similar to that of triangular fuzzy data, and only the difference of the number of preset models is needed, which is not described herein again.
In an embodiment of the present invention, after the "obtaining the trained preset model corresponding to each reduced value of each type of fuzzy data" mentioned above, the method may further include the following steps:
acquiring test data corresponding to each type of fuzzy data, wherein the test data comprises test fuzzy data and test clear data corresponding to the test fuzzy data;
calculating an input test reduction value of test fuzzy data in the first test data according to the type of the fuzzy data corresponding to the first test data; the first test data is test data corresponding to any type of fuzzy data;
inputting each input test reduced value corresponding to the test fuzzy data in the first test data into the corresponding trained preset model respectively to obtain an output test reduced value corresponding to each input test reduced value respectively;
calculating an output test reduced value corresponding to the first test data and an error value of test clear data in the first test data;
and if the error value is larger than the preset error, reselecting training sample data of the type of the fuzzy data corresponding to the first test data, and re-training each preset model of the type of the fuzzy data corresponding to the first test data according to the training sample data until the error value obtained by the test is smaller than or equal to the preset error.
The test procedure is described by taking triangular fuzzy data as an example. Firstly, test data corresponding to triangular fuzzy data are obtained, and the test data comprise the triangular fuzzy data and test clear data corresponding to the triangular fuzzy data. The data contained in the test data are all decision values corresponding to the test fuzzy data or the test clear data.
Then, calculating input test brief values of the test fuzzy data according to a calculation formula for calculating the input brief values of the triangular fuzzy data, and respectively inputting each calculated input test brief value into a corresponding preset model which is trained, so as to obtain output test brief values corresponding to each input test brief value. Specifically, inputting an expected value of an input test reduced value into a preset model corresponding to the expected value of the triangular fuzzy data to obtain an expected value of an output test reduced value; inputting the optimistic key value of the input test reduced value into a preset model corresponding to the optimistic key value of the triangular fuzzy data to obtain the optimistic key value of the output test reduced value; and inputting the pessimistic key value of the input test brief value into a preset model corresponding to the pessimistic key value of the triangular fuzzy data to obtain the pessimistic key value of the output test brief value.
Finally, the output test reduction value (c) is calculated1、c2、c3) And test clear data (c'1、c′2、c′3) Error value of (C ═ C)1-c′1)2+(c2-c′2)2+(c3-c′3)2. If the error value is smaller than or equal to the preset error, three preset models corresponding to the triangular fuzzy data are trained; if the error value is larger than the preset error, the training sample data corresponding to the triangular fuzzy data is reselected, and then three preset models corresponding to the triangular fuzzy data are trained until the error value obtained by testing is smaller than or equal to the preset error.
The testing process of other types of fuzzy data is similar to that of triangular fuzzy data, and only the difference of the number of preset models is needed, which is not described herein again.
It should be noted that, in the embodiment of the present invention, an input reduced value used in a training process is referred to as an input training reduced value, and an output reduced value used in the training process is referred to as an output training reduced value; the input reduced value used in the test process is referred to as an input test reduced value, and the output reduced value used in the test process is referred to as an output test reduced value.
As can be seen from the above description, the embodiment of the present invention utilizes the reversible property of the reduced value, can obtain the prediction data by directly calculating the output reduced value, avoids approximate calculation, does not need too many manual intervention components, does not need to transform the fuzzy data into a series of interval values, prevents information loss, can improve the accuracy of the result, has a simple calculation process and consumes less time, and breaks through the limitation of the existing method on the type of the fuzzy data.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 2 is a schematic structural diagram of a fuzzy data processing system according to an embodiment of the present invention, and for convenience of description, only the relevant parts according to the embodiment of the present invention are shown.
Referring to FIG. 2, fuzzy data processing system 200 may comprise: an input reduced value determination module 201, an output reduced value determination module 202, and a prediction data determination module 203.
The input reduction value determining module 201 is configured to obtain the type of the to-be-processed fuzzy data and the number of decision values of the to-be-processed fuzzy data, and determine an input reduction value matched with the number of decision values according to the type of the to-be-processed fuzzy data and the number of decision values of the to-be-processed fuzzy data;
an output reduction value determining module 202, configured to input each input reduction value into a corresponding trained preset model, respectively, to obtain an output reduction value corresponding to each input reduction value;
and the prediction data determining module 203 is configured to calculate each output reduction value according to an inverse process of the respective corresponding reduction value calculation to obtain prediction data of the to-be-processed fuzzy data.
Optionally, the fuzzy data processing system 200 may further comprise: and a training module.
The training module is used for acquiring training sample data corresponding to each type of fuzzy data, and the training sample data comprises a plurality of training fuzzy data and training clear data corresponding to each training fuzzy data; calculating an input training reduced value of each training fuzzy data in each training sample data and an output training reduced value of each corresponding training clear data according to the type of the fuzzy data corresponding to each training sample data; and training preset models respectively corresponding to the brief values of the fuzzy data of each type according to the input training brief values and the output training brief values corresponding to the input training brief values of the fuzzy data of each training sample data to obtain the trained preset models corresponding to the brief values of the fuzzy data of each type.
Optionally, the fuzzy data processing system 200 may further comprise: and a testing module.
The test module is used for acquiring test data corresponding to each type of fuzzy data, and the test data comprises test fuzzy data and test clear data corresponding to the test fuzzy data; calculating an input test reduction value of test fuzzy data in the first test data according to the type of the fuzzy data corresponding to the first test data; the first test data is test data corresponding to any type of fuzzy data; inputting each input test reduced value corresponding to the test fuzzy data in the first test data into the corresponding trained preset model respectively to obtain an output test reduced value corresponding to each input test reduced value respectively; calculating an output test reduced value corresponding to the first test data and an error value of test clear data in the first test data; and if the error value is larger than the preset error, reselecting training sample data of the type of the fuzzy data corresponding to the first test data, and re-training each preset model of the type of the fuzzy data corresponding to the first test data according to the training sample data until the error value obtained by the test is smaller than or equal to the preset error.
Optionally, the input reduction value determination module 201 may be further configured to:
if the type of the fuzzy data to be processed is exponential fuzzy data, the determined value of the fuzzy data to be processed is one, and the input reduced value of the fuzzy data to be processed is the expected value of the fuzzy data to be processed;
if the type of the fuzzy data to be processed is normal fuzzy data, determining values of the fuzzy data to be processed are two, and input reduction values of the fuzzy data to be processed are an expected value and a variance of the fuzzy data to be processed respectively;
if the type of the fuzzy data to be processed is triangular fuzzy data, the number of the determined values of the fuzzy data to be processed is three, and the input brief values of the fuzzy data to be processed are an expected value, an optimistic key value and a pessimistic key value of the fuzzy data to be processed respectively;
if the type of the fuzzy data to be processed is trapezoidal fuzzy data, the decision values of the fuzzy data to be processed are four, and the input brief values of the fuzzy data to be processed are respectively an optimistic mean value, a pessimistic mean value, an optimistic key value and a pessimistic key value of the fuzzy data to be processed.
Optionally, the input reduction value determination module 201 may be further configured to:
is of the typeExpectation value E of fuzzy data to be processed of exponential fuzzy data1(ξ) is E1(ξ) ═ 1/(2 λ), where λ is the determinant value of the fuzzy data to be processed whose type is exponential fuzzy data;
expectation value E of to-be-processed fuzzy data with type of normal fuzzy data2(ξ) is E2Mu in (ξ), and V (ξ) is sigma-sigma, and the variance V (ξ) of the blur data to be processed is normal type of blur data2(ii) a Wherein, mu and sigma are both determinant values of the fuzzy data to be processed with the type of normal fuzzy data;
expected value E of to-be-processed fuzzy data with type of triangular fuzzy data3(ξ) is:
Figure BDA0002510738850000131
Figure BDA0002510738850000132
optimistic key value of fuzzy data to be processed with type of triangular fuzzy data
Figure BDA0002510738850000133
Comprises the following steps:
Figure BDA0002510738850000134
pessimistic key value CV of to-be-processed fuzzy data with type of triangular fuzzy data3*(ξ) is CV3*(ξ)＝a2/(1+a2-a1) (ii) a Wherein, a1、a2And a3All the fuzzy data are determinant values of the fuzzy data to be processed with the type of triangular fuzzy data;
optimistic mean value E of fuzzy data to be processed with type of trapezoidal fuzzy data*(ξ) is E*(ξ)＝(b1+b2) Per 2, pessimistic mean value E of to-be-processed fuzzy data with type of trapezoid fuzzy data*(ξ) is E*(ξ)＝(b3+b4) Per 2, optimistic key value of fuzzy data to be processed with type of trapezoidal fuzzy data
Figure BDA0002510738850000135
Comprises the following steps:
Figure BDA0002510738850000136
pessimistic key value CV of to-be-processed fuzzy data with type of trapezoid fuzzy data4*(ξ) is CV4*(ξ)＝b2/(1+b2-b1) (ii) a Wherein, b1、b2、b3And b4All are decision values of the fuzzy data to be processed with the type of trapezoidal fuzzy data.
Optionally, the prediction data determining module 203 is specifically configured to:
if the type of the fuzzy data to be processed is exponential fuzzy data, the prediction data lambda' of the fuzzy data to be processed is as follows: λ '═ 1/(2E'1(ξ)), wherein E'1(ξ) outputting a reduced value for the fuzzy data to be processed, which is of the type of the exponential fuzzy data;
if the type of the fuzzy data to be processed is normal fuzzy data, the prediction data mu 'and sigma' of the fuzzy data to be processed are respectively: mu '═ E'2(ξ)，σ′2V '(ξ); wherein E'2(ξ) and V' (ξ) are both output reduction values of the fuzzy data to be processed with the type of normal fuzzy data;
if the type of the fuzzy data to be processed is triangular fuzzy data, prediction data a 'of the fuzzy data to be processed'1、a′2And a'3Respectively as follows: a'1＝D1/D，a′2＝D2/D，a′3＝D3D; wherein, D, D1、D2And D3Intermediate parameters corresponding to the fuzzy data to be processed with the type of triangular fuzzy data,
Figure BDA0002510738850000141
Figure BDA0002510738850000142
Figure BDA0002510738850000143
Figure BDA0002510738850000144
E′3(ξ)、
Figure BDA0002510738850000145
and CV'3*(ξ) the output reduced values of the fuzzy data to be processed are all triangular fuzzy data;
if the type of the fuzzy data to be processed is trapezoidal fuzzy data, predicting data b 'of the fuzzy data to be processed'1、b′2、b′3And b'4Respectively as follows: b'1＝D′1/D′，b′2＝D′2/D′，b′3＝D′3/D′，b′4＝D′4a,/D'; wherein, D 'and D'1、D′2、D′3And D'4Intermediate parameters corresponding to fuzzy data to be processed with the type of trapezoidal fuzzy data,
Figure BDA0002510738850000151
Figure BDA0002510738850000152
Figure BDA0002510738850000153
Figure BDA0002510738850000154
Figure BDA0002510738850000155
E*′(ξ)、E′*(ξ)、
Figure BDA0002510738850000156
and CV'4*(ξ) are output reduction values of the blur data to be processed, each of which is of the type of trapezoidal blur data.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing functional units and modules are merely illustrated in terms of division, and in practical applications, the foregoing functional allocation may be performed by different functional units and modules as needed, that is, the internal structure of the fuzzy data processing system is divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 3, the terminal device 300 of this embodiment includes: one or more processors 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processors 301. The processor 301, when executing the computer program 303, implements the steps in the above-described respective fuzzy data processing method embodiments, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor 301, when executing the computer program 303, implements the functions of each module/unit in the above-described fuzzy data processing system embodiments, such as the functions of the modules 201 to 203 shown in fig. 2.
Illustratively, the computer program 303 may be partitioned into one or more modules/units that are stored in the memory 302 and executed by the processor 301 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 303 in the terminal device 300. For example, the computer program 303 may be divided into an input reduced value determination module, an output reduced value determination module, and a prediction data determination module, and each module may specifically function as follows:
the input reduced value determining module is used for acquiring the type of the fuzzy data to be processed and the number of the determinant values of the fuzzy data to be processed, and determining input reduced values matched with the number of the determinant values according to the type of the fuzzy data to be processed and the number of the determinant values of the fuzzy data to be processed;
the output reduction value determining module is used for respectively inputting each input reduction value into the corresponding trained preset model to obtain the output reduction value corresponding to each input reduction value;
and the prediction data determining module is used for calculating each output reduced value according to the inverse process of the reduced value calculation corresponding to each output reduced value to obtain the prediction data of the fuzzy data to be processed.
Other modules or units can refer to the description of the embodiment shown in fig. 2, and are not described again here.
The terminal device 300 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device 300 includes, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that fig. 3 is only one example of a terminal device 300 and does not constitute a limitation of the terminal device 300, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device 300 may further include an input device, an output device, a network access device, a bus, etc.
The Processor 301 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 302 may be an internal storage unit of the terminal device 300, such as a hard disk or a memory of the terminal device 300. The memory 302 may also be an external storage device of the terminal device 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 300. Further, the memory 302 may also include both an internal storage unit of the terminal device 300 and an external storage device. The memory 302 is used for storing the computer program 303 and other programs and data required by the terminal device 300. The memory 302 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (10)

1. A fuzzy data processing method, comprising:
acquiring the type of fuzzy data to be processed and the number of determinant values of the fuzzy data to be processed, and determining input reduced values matched with the number of determinant values according to the type of the fuzzy data to be processed and the number of determinant values of the fuzzy data to be processed;
inputting each input reduction value into a corresponding preset model after training respectively to obtain an output reduction value corresponding to each input reduction value respectively;
and calculating each output reduced value according to the inverse process of the calculation of the corresponding reduced value to obtain the prediction data of the fuzzy data to be processed.
2. The fuzzy data processing method of claim 1, wherein before the inputting each input reduction value into the corresponding trained preset model to obtain the corresponding output reduction value, further comprises:
acquiring training sample data corresponding to each type of fuzzy data, wherein the training sample data comprises a plurality of training fuzzy data and training clear data corresponding to each training fuzzy data;
calculating an input training reduced value of each training fuzzy data in each training sample data and an output training reduced value of each corresponding training clear data according to the type of the fuzzy data corresponding to each training sample data;
and training preset models respectively corresponding to the brief values of the fuzzy data of each type according to the input training brief values and the output training brief values corresponding to the input training brief values of the fuzzy data of each training sample data to obtain the trained preset models corresponding to the brief values of the fuzzy data of each type.
3. The fuzzy data processing method of claim 2, further comprising, after obtaining the trained preset model corresponding to each reduced value of each type of fuzzy data:
acquiring test data corresponding to each type of fuzzy data, wherein the test data comprises test fuzzy data and test clear data corresponding to the test fuzzy data;
calculating an input test reduction value of test fuzzy data in first test data according to the type of the fuzzy data corresponding to the first test data; the first test data is test data corresponding to any type of fuzzy data;
inputting each input test reduction value corresponding to the test fuzzy data in the first test data into a corresponding trained preset model respectively to obtain an output test reduction value corresponding to each input test reduction value respectively;
calculating an output test reduced value corresponding to the first test data and an error value of test clear data in the first test data;
and if the error value is larger than the preset error, reselecting training sample data of the fuzzy data of the type corresponding to the first test data, and training each preset model of the fuzzy data of the type corresponding to the first test data again according to the training sample data until the error value obtained by the test is smaller than or equal to the preset error.
4. The fuzzy data processing method of claim 1, wherein said determining the input reduction value matching with the decision value number according to the type of the fuzzy data to be processed and the decision value number of the fuzzy data to be processed comprises:
if the type of the fuzzy data to be processed is exponential fuzzy data, the determined value of the fuzzy data to be processed is one, and the input reduced value of the fuzzy data to be processed is the expected value of the fuzzy data to be processed;
if the type of the fuzzy data to be processed is normal fuzzy data, determining values of the fuzzy data to be processed are two, and input reduction values of the fuzzy data to be processed are an expected value and a variance of the fuzzy data to be processed respectively;
if the type of the fuzzy data to be processed is triangular fuzzy data, the number of the determined values of the fuzzy data to be processed is three, and the input simple values of the fuzzy data to be processed are respectively an expected value, an optimistic key value and a pessimistic key value of the fuzzy data to be processed;
if the type of the fuzzy data to be processed is trapezoidal fuzzy data, the number of the deterministic values of the fuzzy data to be processed is four, and the input brief values of the fuzzy data to be processed are respectively an optimistic mean value, a pessimistic mean value, an optimistic key value and a pessimistic key value of the fuzzy data to be processed.
5. The fuzzy data processing method of claim 4, wherein the type of the expected value E of the fuzzy data to be processed is exponential fuzzy data1(ξ) is E1(ξ) ═ 1/(2 λ), where λ is the determinant value of the fuzzy data to be processed whose type is exponential fuzzy data;
expectation value E of to-be-processed fuzzy data with type of normal fuzzy data2(ξ) is E2(ξ) mu, type Normal fuzzy dataThe variance V (ξ) of the fuzzy data is V (ξ) ═ sigma2(ii) a Wherein, mu and sigma are both determinant values of the fuzzy data to be processed with the type of normal fuzzy data;
expected value E of to-be-processed fuzzy data with type of triangular fuzzy data3(ξ) is:
Figure FDA0002510738840000031
Figure FDA0002510738840000032
optimistic key value of fuzzy data to be processed with type of triangular fuzzy data
Figure FDA0002510738840000033
Comprises the following steps:
Figure FDA0002510738840000034
pessimistic key value CV of to-be-processed fuzzy data with type of triangular fuzzy data3*(ξ) is CV3*(ξ)＝a2/(1+a2-a1) (ii) a Wherein, a1、a2And a3All the fuzzy data are determinant values of the fuzzy data to be processed with the type of triangular fuzzy data;
optimistic mean value E of fuzzy data to be processed with type of trapezoidal fuzzy data*(ξ) is E*(ξ)＝(b1+b2) Per 2, pessimistic mean value E of to-be-processed fuzzy data with type of trapezoid fuzzy data*(ξ) is E*(ξ)＝(b3+b4) Per 2, optimistic key value of fuzzy data to be processed with type of trapezoidal fuzzy data
Figure FDA0002510738840000035
Comprises the following steps:
Figure FDA0002510738840000036
pessimistic key value CV of to-be-processed fuzzy data with type of trapezoid fuzzy data4*(ξ) is CV4*(ξ)＝b2/(1+b2-b1) (ii) a Wherein, b1、b2、b3And b4All are decision values of the fuzzy data to be processed with the type of trapezoidal fuzzy data.
6. The fuzzy data processing method of any one of claims 1 to 5, wherein the calculating each output reduction value according to the inverse process of the respective corresponding reduction value to obtain the prediction data of the fuzzy data to be processed comprises:
if the type of the fuzzy data to be processed is exponential fuzzy data, the prediction data lambda' of the fuzzy data to be processed is as follows: λ '═ 1/(2E'1(ξ)), wherein E'1(ξ) outputting a reduced value for the fuzzy data to be processed, which is of the type of the exponential fuzzy data;
if the type of the fuzzy data to be processed is normal fuzzy data, the prediction data mu 'and sigma' of the fuzzy data to be processed are respectively: mu '═ E'2(ξ)，σ′2V '(ξ); wherein E'2(ξ) and V' (ξ) are both output reduction values of the fuzzy data to be processed with the type of normal fuzzy data;
if the type of the fuzzy data to be processed is triangular fuzzy data, prediction data a 'of the fuzzy data to be processed'1、a′2And a'3Respectively as follows: a'1＝D1/D，a′2＝D2/D，a′3＝D3D; wherein, D, D1、D2And D3Intermediate parameters corresponding to the fuzzy data to be processed with the type of triangular fuzzy data,
Figure FDA0002510738840000041
Figure FDA0002510738840000042
Figure FDA0002510738840000043
Figure FDA0002510738840000044
E′3(ξ)、
Figure FDA0002510738840000045
and CV'3*(ξ) the output reduced values of the fuzzy data to be processed are all triangular fuzzy data;
if the type of the fuzzy data to be processed is trapezoidal fuzzy data, the prediction data b 'of the fuzzy data to be processed'1、b′2、b′3And b'４Respectively as follows: b'1＝D′1/D′，b′2＝D′2/D′，b′3＝D′3/D′，b′4＝D′4a,/D'; wherein, D 'and D'1、D′2、D′3And D'4Intermediate parameters corresponding to fuzzy data to be processed with the type of trapezoidal fuzzy data,
Figure FDA0002510738840000046
Figure FDA0002510738840000047
Figure FDA0002510738840000051
Figure FDA0002510738840000052
Figure FDA0002510738840000053
E*′(ξ)、E′*(ξ)、
Figure FDA0002510738840000054
and CV'4*(ξ) are output reduction values of the blur data to be processed, each of which is of the type of trapezoidal blur data.
7. A fuzzy data processing system comprising:
an input reduction value determining module, configured to obtain a type of to-be-processed fuzzy data and a number of decision values of the to-be-processed fuzzy data, and determine an input reduction value matching the number of decision values according to the type of the to-be-processed fuzzy data and the number of decision values of the to-be-processed fuzzy data;
the output reduction value determining module is used for respectively inputting each input reduction value into the corresponding trained preset model to obtain the output reduction value corresponding to each input reduction value;
and the prediction data determining module is used for calculating each output reduced value according to the inverse process of the reduced value calculation corresponding to each output reduced value to obtain the prediction data of the fuzzy data to be processed.
8. The fuzzy data processing system of claim 7 further comprising:
the training module is used for acquiring training sample data corresponding to each type of fuzzy data, and the training sample data comprises a plurality of training fuzzy data and training clear data corresponding to each training fuzzy data; calculating an input training reduced value of each training fuzzy data in each training sample data and an output training reduced value of each corresponding training clear data according to the type of the fuzzy data corresponding to each training sample data; and training preset models respectively corresponding to the brief values of the fuzzy data of each type according to the input training brief values and the output training brief values corresponding to the input training brief values of the fuzzy data of each training sample data to obtain the trained preset models corresponding to the brief values of the fuzzy data of each type.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the fuzzy data processing method according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by one or more processors, implements the steps of the fuzzy data processing method of any one of claims 1 to 6.
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