WO2016091102A1

WO2016091102A1 - Fuzzy neural network-based body area network health information monitoring and interaction system

Info

Publication number: WO2016091102A1
Application number: PCT/CN2015/096198
Authority: WO
Inventors: 程绍龙; 程寿惠; 郭强; 李新; 曹刚; 尹从明
Original assignee: 山东中弘信息科技有限公司
Priority date: 2014-12-10
Filing date: 2015-12-02
Publication date: 2016-06-16
Also published as: CN104484562B; CN104484562A

Abstract

A fuzzy neural network-based body area network health information monitoring and interaction system comprises collection ends, a fuzzy neural network analysis and detection system, an interaction module, a recognition unit and a display unit; the collection end comprises a collection module and a control module; the collection module is used for collecting related biological data collected by household medical equipment and sending the data to the control module; the control module is further used for controlling the collection module to collect data; the interaction module is used for building an interactive group, selecting the size of the interactive group and the number of the collection ends for mutual data interaction, and creating data sharing; the fuzzy neural network analysis and detection system comprises a model building module, a fuzzy neural network data analysis module and a ranking module. Timely uploading of health monitoring data and interaction within a selectable range are achieved, and information interaction is achieved on the basis of protecting the privacy of users and respecting the wishes of users.

Description

Instruction manual

Title: A Body Network Health Information Monitoring and Interaction System Based on Fuzzy Neural Network

Technical field

[0001] The present invention relates to a body network health information monitoring and interaction system based on a fuzzy neural network.

Background technique

[0002] Nowadays people's living standards are gradually improving, and home medical equipment is also widely used, but the test results are only known to the person or family. If there is an emergency, no family members or other accompanying personnel, the medical workers cannot know the patient's physical condition in time. There is no information such as medical history, and it is not easy to diagnose and treat in time. Therefore, monitoring data of home medical equipment is necessary for sharing and storage through the health care platform.

Summary of invention

technical problem

[0003] The existing medical health platform has the following problems:

[0004] (1) Information is mostly one-to-one interaction, that is, only the doctor and the uploader can see it themselves. In many cases, the information cannot be shared with the user's consent; there are many recessive, family inheritances. The problem cannot be discovered, which is not conducive to the doctor's understanding of the patient's condition;

[0005] ( 2) The data to be checked is too professional. Ordinary users cannot really understand whether their inspections are healthy or have problems, and cannot generate uniform indicators for users to refer to;

[0006] (3) The physical condition of the employees of the same unit or company, leaders and colleagues cannot understand that there may be misjudgments in the arrangement of work such as business trips and manual labor, which threaten the health of the body; Knowing how well your health ranks among your friends can't motivate users, ignore concerns about their health, and don't use home medical equipment to measure on time.

Problem solution

Technical solution

[0007] In order to solve the above problems, the present invention proposes a body network health information monitoring based on fuzzy neural network. Measuring and interacting system, the system uses fuzzy neural network algorithm to upload the user's health information in time, share and sort in the specified range, and make everyone pay attention to their health, and check regularly.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A body network health information monitoring and interaction system based on a fuzzy neural network, comprising a collecting end, a fuzzy neural network analyzing and detecting system, an interaction module, an identifying unit and a display unit;

[0010] The collection end includes an acquisition module and a control module, and the collection module is configured to collect related biological data collected by the home medical device, and send the data to the control module, where the control module is further configured to control the collection module to collect data;

[001 1] The interaction module is configured to establish an interaction group, select an interaction group size, a number of collection ends that perform data interaction with each other, and establish data sharing;

[0012] The fuzzy neural network analysis and detection system comprises a model building module, a fuzzy neural network data analysis module and a sorting module, wherein:

[0013] the model construction module receives the biological data transmitted in the interaction group in the interaction module, and uses it as an input variable to construct a membership function of the biological data through the input layer and the fuzzy layer module;

[0014] the fuzzy neural network data analysis module is configured to process the biological data by constructing a fuzzy inference layer, a layer back layer, a deblurring layer, and an output layer module, and calculate an output biological index value;

[0015] the sorting module is configured to perform data ranking in the interaction group according to the biometric index value;

[0016] The identification unit receives the data transmitted by the sorting module, reads the interaction group of the interaction module, identifies the collection module and the display unit in the same interaction group, and displays the biological data and the data ranking through the display unit.

[0017] The acquisition module monitors data uploaded by the home medical device in real time, including systolic blood pressure, diastolic blood pressure, and heart rate.

[0018] The model building module includes an input layer module and a fuzzification layer module, wherein the input layer module is a first layer module, and the input value is data uploaded after measurement by the home medical device, and the uploaded data includes shrinkage. Pressure SSY, diastolic pressure SZY and heart rate XL; only play the role of transmission, the input data is passed to the next module, the output value is: 0 _SSY ⁽¹ ) =SSY, 0 SZY ⁽¹⁾ =SZY, 0 _X L ("=XL; where 0 is the value of i of the output of the X-th layer module. [0019] The fuzzification layer module is a second layer module, and the input data is divided into a fuzzy subset, and the fuzzy subset of the systolic pressure SSY, the diastolic-pressure SZY, and the heart rate XL is {normal (M), high (H) ), very high (VH) }, membership degrees are _SSY (M, H, VH), μ _SZY (M, H, VH) and _XL (M, H, VH).

[0020] The fuzzification layer 2 module, for "normal (M)", uses a symmetric function of the Sigmoid function, and the membership degree is:

[]

=[1 + esp( (x― ,2 ] -1

[0021] For "polar 鬲 (VH)", the meter uses the Sigmoid function, and the membership is:

[] ^ -[! + es3⁄4p"- , Ι(χ— IF23⁄4j~ ^I

[0022] For "high (H)", a Gaussian function is used, and the membership is:

[]

[0023] Wl, W2 are systems, the initial value is arbitrary, and the network training and self-learning are continuously adjusted to minimize the error between the actual output value of the network and the tutor signal. The node output range of this layer is between 0 and 1.

[0024] For the membership function of the linguistic variables M and VH, wherein the function of the parameter W2 is to shift the membership function to the right in the horizontal axis; the role of W1 is to adjust the shape of the membership function; the membership function of the linguistic variable H W2 and W1 represent the center and width of the membership function, respectively. The closer the input sample is to the center of the node, the larger the output.

[0025] The final output of the second layer module is:

In the formula, y=l, 2, 3, and R have values of SSY, SZY, and XL, which correspond to the membership functions of systolic blood pressure, diastolic blood pressure, and heart rate, respectively.

[0027] The fuzzy neural network data analysis module includes a fuzzy inference layer module, which is set as a third layer module, and is used to establish a fuzzy inference rule base, wherein the language rule is a basis of fuzzy logic reasoning, and each fuzzy inference rule is Conclusion The parameters are fuzzy subsets {health (H), sub-health (SubH), unhealthy (UnH)} _; since the fuzzy sets of input data SSY, SZY and XL have 3 fuzzy markers, the fuzzy neural network model has a common inference rule 3X3X3 = 27, which is 27 output nodes; for any set of input data, a corresponding inference rule can be found; the applicability a(1) of each inference rule is:

[0028] where a(1) represents the applicability of the first inference rule, :1,2,3; j

1,2,3; k=l, 2, 3. The output of the third layer is:

[0029] The fuzzy neural network data analysis module includes a fourth layer module, that is, a layer one module, and the total number of nodes is the same as that of the third layer module, realizing normalization processing on the fourth layer output, preparing for deblurring , its output is:

[0030] where 1=1, 2-27.

[0031] The fuzzy neural network data analysis module further includes a deblurring layer module, configured to obtain, by fuzzy inference, membership degrees of different rules corresponding to different outputs (H, SubH, UnH), by deblurring The method obtains the membership of three health conditions, and the output of this layer is:

[]

[0032] wherein n represents the number of output nodes of the layer, n=l, 2, 3; the weight W _{nl is} introduced, and the relative importance of each fuzzy inference rule is discriminated by the change in the network.

[0033] The application of the weight W _nl optimizes the fuzzy inference rule and makes the fuzzy reasoning adaptive, thereby improving the intelligence degree of the fuzzy neural network model.

[0034] The fuzzy neural network data analysis module further includes an output layer module, which is a sixth layer module, configured to adjust three output blurs by using a weight center method by using a weight W i (i=l, 2, 3) The relative importance of the subset (H, SubH, UnH), taking W

That is, the output of the fuzzy subset (H) contributes to the health index value of 1, the sub-health (SubH) contributes to the health index value of 0.5, and the unhealthy (UnH) contributes to the health index value of 0; The health index value P is:

[0035] The fuzzy neural network data analysis module further includes a BP neural network learning training module, configured to train the network by using an improved BP algorithm with an additional momentum term, and adjust the weight by using a gradient descent algorithm of error back propagation. the weight in the objective function gradient along the reverse direction is adjusted to confirm the fuzzy neural network parameters Wl, W2 and W _nl, first adjust W _nl, then adjust Wl, W2. Ultimately, the total mean square error of the actual output of the network and the expected output is minimized.

[0036] The error signal BP neural network learning and training module of the network _{is: e (n) = P e} *

-P (n). Where P(n) is the actual output of the nth iteration and P is the desired output. The purpose of learning training is to minimize the error function.

[0037] In order to improve the learning speed and maintain the stability of the parameter change, a gradient descent method based on the generalized delta rule is adopted, that is, [0038] wherein

Wherein β is the learning rate, 0<β<1, n AW n)

Generally, the value is 0.9.

[0040] Thus, the adjustment process of the weight W is:

[0041] After the AW is obtained, the corrected weight W _nl can be obtained, that is, W _nl (n+l)= W _nl (n) +AW

[0042] Using a similar idea to derive Wl, W2,

[0043] So far, the optimization process of Wl, W2 and W _nl is completed, Wl and W2 are automatically generated as membership functions, and the change of W _nl is represented by the change of the relative importance of the corresponding fuzzy rules, and the accuracy of network fuzzy inference is finally improved through training. , making the actual output closer to the ideal output.

[0044] the sorting module reads an output value of the output layer module, reads an acquisition module corresponding to the output value, and confirms that the collection module collects other outputs in the interaction group through the interaction group selected by the control module in the interaction module. The output value of the layer module, and the data is ranked within the interaction group.

Advantageous effects of the invention

Beneficial effect

[0045] (1) realizing timely uploading of health measurement data, interaction within a selectable range, and performing information interaction on the basis of protecting user privacy and respecting the user's will;

[0046] (2) by means of a combination of fuzzy logic and a neural network, each of the medical device outputs in the body area network The health data is sent to the fuzzy neural network system model for analysis, and the health index value is obtained, and the health ranking is performed, which can stimulate the enthusiasm of the user, and let them pay attention to their own health problems in time, and can use the home medical equipment to measure on time;

[0047] (3) The system has good scalability, and can join more other types of health data for interaction.

, comprehensive evaluation.

Brief description of the drawing

DRAWINGS

1 is a schematic structural view of the present invention.

2 is a fuzzy neural network model diagram of the present invention.

3 is a graph of membership function of the present invention.

Invention embodiment

Embodiments of the invention

[0051] As shown in FIG. 1 , a body network health information monitoring and interaction system based on a fuzzy neural network includes a collection end, a fuzzy neural network analysis and detection system, an interaction module, an identification unit, and a display unit.

[0052] The collection end includes an acquisition module and a control module, and the collection module is configured to collect related biological data collected by the home medical device, and send the data to the control module, where the control module is further configured to control the collection module to collect data;

[0053] the interaction module is configured to establish an interaction group, select an interaction group size, a number of collection ends that perform data interaction with each other, and establish data sharing;

[0054] The fuzzy neural network analysis and detection system comprises a model construction module, a fuzzy neural network data analysis module and a sorting module, wherein:

[0055] the model building module receives the biological data transmitted in the interaction group in the interaction module, and uses it as an input variable to construct a membership function of the biological data through the input layer and the fuzzification layer module;

[0056] the fuzzy neural network data analysis module is configured to process the biological data by constructing a fuzzy inference layer, a layer back layer, a deblurring layer, and an output layer module, and calculate an output biological index value;

[0057] the sorting module is configured to perform data ranking in the interaction group according to the biometric index value;

[0058] the identification unit receives the data transmitted by the sorting module, reads the interaction group of the interaction module, identifies the collection module and the display unit in the same interaction group, and ranks the biological data and the data through the display unit. Do not.

[0059] The collection module monitors data uploaded by the home medical device in real time, including systolic blood pressure, diastolic blood pressure, and heart rate.

[0060] The fuzzy neural network model includes an input layer, a fuzzification layer, a fuzzy inference layer, a normalization layer, a deblurring layer, and an output layer, as shown in FIG. 2. For each layer of output variables, there are two functions, one for the inverse input of the training data and one for the actual output. The third layer is the fuzzy inference layer, and each node represents a fuzzy rule. Therefore, all Layer 3 nodes constitute a fuzzy rule base. The connection between the second layer and the third layer acts as a connection inference engine, avoiding the fuzzy implication reasoning process. The second layer of the connection defines the predecessor of the fuzzy rule, and the third layer of the connection defines the posterior of the fuzzy rule. The normal direction of propagation is from the first layer to the sixth layer, that is, from bottom to top. During the learning and training process, the signal propagates from the sixth layer to the first layer, that is, from top to bottom. The input and output relationship of each layer is as follows (0 (x) indicates the output of the Xth layer).

[0061] (1) The first layer is an input layer. The input value is the data uploaded after measurement by the home medical device. The uploaded data includes systolic blood pressure SSY, diastolic blood pressure SZY and heart rate XL. This layer only serves as a pass, passing the input data to the next layer. The output is:

[0063] (2) The second layer is a fuzzification layer. This layer divides the input data into fuzzy subsets. The fuzzy subsets of systolic pressure SSY, diastolic pressure SZY and heart rate XL are {normal (M), high (H), very high (VH)}, and the membership is W _SSi. (M, H, VH), _SZY (M, H, VH) and μ _Χί (Μ, Η, νΗ). (The implementation case of this patent is mainly for the health status of high blood pressure people, where the low blood pressure and low heart rate are attributed to the normal range)

[0064] For "normal (Μ)", a symmetric function using the Sigmoid function, the membership is:

[]

= [L + € ¾ (Ι : (-, 2 Peng ^_Ι

[0065] For "very high (VH)", the Sigmoid function is used, and the membership is:

[]

[0066] For "higher (H)", a Gaussian function is used, and the membership is:

[0067] Wl, W2 initial value is arbitrary, continuously adjusted through network training and self-learning, so that the actual output value of the network and a

The tutor's signal error is minimal, and the adjusted weight can be used for actual health assessment. The node output range is between 0. For the membership function of the linguistic variables M and VH, the role of the parameter W2 is to shift the membership function to the right in the horizontal axis; the role of W1 is to adjust the shape of the membership function, and the larger W1 makes the function approximate the step. W1 makes the function flatter. For the membership function of the linguistic variable H, W2 and W1 represent the center and width of the membership function, respectively. The closer the input sample is to the center of the node, the larger the output.

[0068] The final output of the second layer is:

[0070] (3) The third layer is a fuzzy inference layer. The most important process in fuzzy reasoning is to establish a fuzzy inference rule base, in which the linguistic rules are the basis of fuzzy logic reasoning, and the conclusion parameters of each fuzzy inference rule are fuzzy subsets {health (H), sub-health (SubH), unhealthy (UnH) }. Since the fuzzy sets of the input data SSY, SZY and XL have three fuzzy marks, the fuzzy neural network model has a common inference rule of 3 X 3 X 3=27 . The applicability of each inference rule a (l) is:

In the formula, a (l) represents the applicability of the first inference rule, 1=1, 2-27; i=l, 2, 3; j=l, 2, 3; As shown in Table 1, fuzzy rules are expressed in terms of linguistic variables:

[0073] Rulel: IF SSY is M and SZY is M and XL is M then C is H.

[0074] Rule2 _: IF SSY is M and SZY is H and XL is M then C is H.

[0075]

[0076] Rule26: IF SSY is VH and SZY is H and XL is VH then C is UnH.

[0077] Rule27: IF SSY is VH and SZY is VH and XL is VH then C is UnH.

Where C is the probability of being in a certain state of health.

Table 1

[]

[Table 1]

[0080] The output of the third layer is:

[0081] (4) The fourth layer is a normalized layer. The total number of nodes is the same as that of the third layer, and the normalized processing of the fourth layer output is implemented to prepare for the deblurring. The output is:

Where 1=1, 2···27 ο

[0083] (5) The fifth layer is a deblurring layer. The fuzzy inference results in the membership of different rules corresponding to different outputs (H, SubH, Un H), and the degree of membership of the three health conditions needs to be obtained by deblurring. The output of this layer is:

[0084] wherein n represents the number of output nodes of the layer, n=l, 2, 3. The weight W _{nl is} introduced, and the relative importance of each fuzzy inference rule is discriminated by its change in the network. The application of weight W _nl optimizes the fuzzy inference rules and makes the fuzzy reasoning adaptive, thus improving the intelligence of the fuzzy neural network model.

[0085] (6) The sixth layer is an output layer. Using the area center of gravity method, the relative importance of the three output fuzzy subsets (H, SubH, UnH) is adjusted by the weight W i ( i=l, 2, 3 ), taking WW ₂ =0. 5, W ₃ =0 Output The contribution in the fuzzy subset (H) to the health index value is 1, and the contribution of sub-health (SubH) to the health index value is 0.5, and the unhealthy (UnH) contribution to the health index value is zero. Its final output health index value P is:

[]

[0086] Preferably, a learning training process of the BP neural network is also included. The parameters that need to be determined in the fuzzy neural network are Wl, W2, and W _nl . The network is trained using an improved BP algorithm with additional momentum terms. According to the rule learning, the gradient descent algorithm of error back propagation is used to adjust the weight, so that the weight is adjusted in the opposite direction of the gradient of the objective function. Therefore, W _{nl is} adjusted first, then Wl and W2 are adjusted. Ultimately, the total mean square error of the actual output of the network and the expected output is minimized. The specific network learning algorithm operation process is as follows

[0087] The error signal of the network is: e (n) = P _e * _P (n). Where P (n) is the actual output of the nth iteration, P

For the desired output. The purpose of learning training is to minimize the error function.

. In order to improve the learning speed and maintain the stability of the parameter change, a gradient descent method based on the generalized delta rule is adopted.

. among them

Where β is the learning rate, ο<β<1, n A W

(n) is a dynamic term, n is a momentum coefficient, ο <η <ι, generally takes a value of 0.9. [0088] Thus, the adjustment process of the weight W _^ is:

[]

Δ (η) = - ^ + (-) = - β ^ + 》

[] ,,

, * _ ρ, ν / ^u i

= Β ^ - ^¾) + flat - «

[0089] After the AW is obtained, the modified weight W _nl can be obtained, that is, W _nl (n+l)= W _nl (n+l)+ AW _{1o, and} a similar idea is used to derive Wl, W2o.

ΔΙΤί(ϊΐ) = -Ρ— - t|A i( )

[0090] This completes Wl, W2 and W _nl optimization process, and Wl, W2 performance to automatically generate membership functions, W _n] changes the performance of the corresponding changes in the relative importance of fuzzy rules, through training and ultimately improve the network fuzzy reasoning Precision, making the actual output closer to the ideal output.

[0091] In other embodiments of the invention, the input variables may also be blood oxygenation values, blood glucose values, and the like.

[0092] the sorting module reads an output value of the output layer module, reads an acquisition module corresponding to the output value, and confirms that the collection module collects other outputs in the interaction group through the interaction group selected by the control module in the interaction module. The output value of the layer module, and the data is ranked within the interaction group.

The above description of the specific embodiments of the present invention has been described with reference to the accompanying drawings, but is not intended to limit the scope of the present invention. It should be understood by those skilled in the art that Various modifications or variations that can be made by the creative work are still within the scope of the invention.

Claims

Claim

[Claim 1] A fuzzy neural network based body area network health information monitoring and interaction system, characterized in that

: including an acquisition end, a fuzzy neural network analysis detection system, an interaction module, an identification unit, and a display unit;

The collection end includes an acquisition module and a control module, and the collection module is configured to collect related biological data collected by the home medical device, and send the data to the control module, where the control module is further configured to control the collection module to collect data;

The interaction module is configured to establish an interaction group, select an interaction group size, and the number of collection ends that perform data interaction with each other to establish data sharing;

The fuzzy neural network analysis and detection system comprises a model building module, a fuzzy neural network data analysis module and a sorting module, wherein:

The model building module receives the biological data transmitted in the interaction group in the interaction module, and uses it as an input variable to construct a membership function of the biological data through the input layer and the fuzzy layer module;

The fuzzy neural network data analysis module is configured to process the biological data by constructing a fuzzy inference layer, a layer back layer, a deblurring layer and an output layer module, and calculate an output biometric value value sorting module, which is used according to the biological index value And performing data ranking in the interaction group; the identification unit receives data transmitted by the sorting module, reads an interaction group of the interaction module, identifies an acquisition module and a display unit in the same interaction group, and displays the biological data and the data by displaying The unit is displayed.

[Claim 2] The system of claim 1, wherein: the acquisition module monitors data uploaded by the home medical device in real time, including systolic blood pressure, diastolic blood pressure, and heart rate.

[Claim 3] The system according to claim 1, wherein: the model building module comprises an input layer module and an obfuscation layer module, wherein the input layer module is a first layer module, and an input value thereof For the data uploaded after measurement of the home medical device, the uploaded data includes systolic pressure SSY, diastolic pressure SZY and heart rate XL; only for the transfer function, the input data is transferred to the next module, the output value is: 0 _SSY (" =SSY, 0 SZY ⁽¹⁾ =SZY, 0 _XL (» =XL ; where, 0 represents the i value of the output of the xth layer module.

[Claim 4] The system according to claim 3, wherein: the fuzzification layer module is a second layer module that divides input data into a fuzzy subset, systolic pressure SSY, diastolic pressure SZY, and heart rate XL The fuzzy subset is {normal (M), high (H), extremely high (VH) }, and the membership is w _SSY

(M, H, VH), μ _SZY (M, H, VH) and _XL (M, H, VH).

[Claim 5] The system according to claim 4, wherein: the fuzzification layer module, for "normal (M)", adopts a symmetric function of a Sigmoid function, and the membership degree is: = [i + e3⁄4 ( - Paste - ¹ For "Extremely High (VH)", using the Sigmoid function, the membership is:

/ - [l + ejEp(-iri( - lf 23⁄4 ] ^_i for "high (H)", using a Gaussian function, membership is:

Wl, W2 are the system, the initial value is arbitrary, and the network training and self-learning are continuously adjusted to minimize the error between the actual output value of the network and the tutor signal. The node output range of this layer is between 0~1;

The membership function of the linguistic variables M and VH, where the role of the parameter W2 is to shift the membership function to the right in the horizontal axis; the role of W1 is to adjust the shape of the membership function; the membership function of the linguistic variable H, W2 and W1 represents the center and width of the membership function, respectively. The closer the input sample is to the center of the node, the larger the output;

The final output of the second layer module is:

In the formula, y=l, 2, 3, and R are SSY, SZY, and XL, which correspond to the membership functions of systolic blood pressure, diastolic blood pressure, and heart rate, respectively.

[Clave 6] The system of claim 1 wherein:

The block, including the fuzzy inference layer module, is set as a third layer module for establishing a fuzzy inference rule base, wherein the language rule is the basis of fuzzy logic reasoning, and the conclusion parameter of each fuzzy inference rule is a fuzzy subset {health (H Sub-health (SubH), unhealthy (UnH)} _; since the fuzzy sets of the input data SSY, SZY and XL have 3 fuzzy marks, the fuzzy neural network model has a total inference rule of 3X3X3=27, which is 27 output nodes. For any set of input data, a corresponding inference rule can be found; the applicability a(1) of each inference rule is: : ₂ % where a(1) indicates the application of the first inference rule Degree, 1=1, 2-27; i=l,2,3; J=l 2, 3; k=l, 2, 3; The output of the third layer is:

[Claim 7] The system according to claim 1, wherein: the fuzzy neural network data analysis module comprises a fourth layer module, that is, a layer-by-layer module, and the total number of nodes is the same as that of the third layer module, and the pair is implemented. The normalization of the fourth layer of output, in preparation for deblurring, the output is:

1=1 l=!

Where 1=1, 2-27;

The fuzzy neural network data analysis module further includes a deblurring layer module, which is used to obtain fuzzy membership in which the membership degrees of different rules corresponding to different outputs (H, SubH, UnH) are obtained by deblurring. The degree of membership of a health condition, the output of this layer is:

Where n is the number of output nodes of the layer, n=l, 2, 3; the weight W _{nl is} introduced, and the relative importance of each fuzzy inference rule is determined by its variation in the network.

[Claim 8] The system according to claim 1, wherein: the fuzzy neural network data analysis module further comprises an output layer module, which is a sixth layer module, configured to use an area center of gravity method, and pass the weight W i ( i=l,2,3) adjusts the relative importance of the three output fuzzy subsets (H, SubH, UnH), taking WW ₂ =0. 5, W =0, ie output fuzzy subset health (H) The contribution to the health index value is 1, the sub-health (SubH) contribution to the health index value is 0.5, the unhealthy (UnH) contribution to the health index value is 0; and its final output health index value P is:

[Claim 9] The system according to claim 1, wherein: the fuzzy neural network data analysis module further comprises a BP neural network learning training module, configured to improve the network by using an additional momentum item BP algorithm Training, using the gradient descent algorithm of error back propagation The weighting value is adjusted according to the direction opposite to the gradient of the objective function. It is confirmed that the parameters in the fuzzy neural network are Wl, W2 and W _nl , W _{nl is} adjusted first, then Wl and W2 are adjusted, and finally the actual output of the network is made. The total mean square error of the error with the desired output is minimal.