Health assessment method, device and electronic equipment

Abstract

The present application provides a health assessment method, apparatus, and electronic device. The method includes: obtaining a 1×k first matrix, the first matrix including k weight values ​​corresponding to k indicators, the sum of the k weight values ​​being 1; obtaining a k×m second matrix, the second matrix including k×m first memberships, the k×m first memberships including m memberships of each of the k indicators to m health assessment levels; multiplying the first matrix and the second matrix to obtain a 1×m third matrix, the third matrix including m second memberships, the m second memberships being the m memberships of the electronic device to the m health assessment levels; determining the health assessment level corresponding to the target membership as the health assessment result of the electronic device, the target membership being the membership with the largest membership among the m second memberships. The present application can improve the reliability of health assessments.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communication technology, and in particular to a health assessment method, device, and electronic device. Background Art

[0002] In the prior art, the health of electronic devices is generally evaluated by the usage of the processor, memory, or storage. The evaluation factors are single, which easily leads to low reliability of the evaluation. Summary of the Invention

[0003] The embodiments of the present application provide a health assessment method, device, and electronic device to solve the problem in the prior art of low reliability of electronic device health assessment due to a single evaluation factor.

[0004] To solve the above problems, this application is implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a health assessment method performed by an electronic device, wherein the electronic device includes at least a first indicator layer, the first indicator layer including k indicators; the electronic device corresponds to m health assessment levels, where m is an integer greater than 1;

[0006] The method comprises:

[0007] Obtain a first 1×k matrix, where the first matrix includes k weight values ​​corresponding to the k indicators, and the sum of the k weight values ​​is 1;

[0008] Obtaining a k×m second matrix, where the second matrix includes k×m first memberships, and the k×m first memberships include m memberships of each of the k indicators to the m health assessment levels;

[0009] Multiplying the first matrix and the second matrix to obtain a 1×m third matrix, where the third matrix includes m second memberships, and the m second memberships include the m memberships of the electronic device to the m health assessment levels;

[0010] A health evaluation level corresponding to a target membership is determined as a health evaluation result of the electronic device, wherein the target membership is a membership with the largest membership among the m second memberships.

[0011] In a second aspect, an embodiment of the present application provides a health assessment device, which is applied to an electronic device, wherein the electronic device includes at least a first indicator layer, wherein the first indicator layer includes k indicators; the electronic device corresponds to m health assessment levels, where m is an integer greater than 1;

[0012] The health assessment device comprises:

[0013] A first acquisition module is configured to acquire a first 1×k matrix, where the first matrix includes k weight values ​​corresponding to the k indicators, and the sum of the k weight values ​​is 1;

[0014] A second acquisition module is used to acquire a k×m second matrix, where the second matrix includes k×m first memberships, and the k×m first memberships include m memberships of each of the k indicators to the m health assessment levels;

[0015] a third acquisition module, configured to multiply the first matrix and the second matrix to obtain a 1×m third matrix, wherein the third matrix includes m second memberships, and the m second memberships include the m memberships of the electronic device to the m health assessment levels;

[0016] The determination module is configured to determine the health evaluation level corresponding to the target membership as the health evaluation result of the electronic device, wherein the target membership is the largest membership among the m second memberships.

[0017] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a transceiver, a memory, a processor, and a program stored in the memory and executable on the processor; the processor being characterized in that the processor is configured to read the program in the memory to implement the steps of the method described in the first aspect above.

[0018] In a fourth aspect, an embodiment of the present application further provides a readable storage medium for storing a program, which, when executed by a processor, implements the steps in the method described in the first aspect above.

[0019] In an embodiment of the present application, the electronic device includes at least a first indicator layer, the first indicator layer includes k indicators; the electronic device corresponds to m health evaluation levels, and m is an integer greater than 1. The electronic device obtains a 1×k first matrix, the first matrix includes k weight values ​​corresponding to the k indicators, and the sum of the k weight values ​​is 1; obtains a k×m second matrix, the second matrix includes k×m first memberships, and the k×m first memberships include the m memberships of each indicator in the k indicators to the m health evaluation levels; multiplies the first matrix and the second matrix to obtain a 1×m third matrix, the third matrix includes m second memberships, and the m second memberships include the m memberships of the electronic device to the m health evaluation levels; determines the health evaluation level corresponding to the target membership as the health evaluation result of the electronic device, and the target membership is the membership with the largest membership among the m second memberships. It can be seen that the embodiment of the present application integrates the membership of various indicators of the electronic device to m health evaluation levels through fuzzy mathematics to obtain the health evaluation result of the electronic device, thereby improving the reliability of the health evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 This is one of the flow charts of the health assessment method provided in the embodiment of the present application;

[0022] Figure 2 This is the second flow chart of the health assessment method provided in the embodiment of the present application;

[0023] Figure 3 It is a structural diagram of the health assessment device provided by the present application;

[0024] Figure 4 It is a structural diagram of an electronic device provided by the implementation of this application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] The terms "first", "second" etc. in the embodiments of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. In addition, the terms "comprise" and "have" and any deformation thereof are intended to cover non-exclusive inclusions, such as, the process, method, system, product or equipment comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or that are intrinsic to these processes, methods, products or equipment. In addition, "and / or" is used in the present application to represent at least one of connected objects, such as A and / or B and / or C, and represents comprising independent A, independent B, independent C, and A and B all exist, B and C all exist, A and C all exist, and 7 situations that A, B and C all exist.

[0027] The following describes the health assessment method provided in the embodiments of the present application.

[0028] The health assessment method provided in the embodiments of the present application can be performed by an electronic device. The electronic device meets the following requirements:

[0029] 1) The electronic device includes at least a first indicator layer, wherein the first indicator layer includes k indicators.

[0030] In practical applications, the electronic device may include P indicator layers, where the number of indicators included in the P indicator layers is greater than 1, and P is a positive integer.

[0031] It should be noted that when P is greater than 1, the sth indicator layer among the P indicator layers has a subordinate relationship with the s+1th indicator layer, and the sth indicator layer is the parent indicator layer of the s+1th indicator layer, and the s+1th indicator layer is the child indicator layer of the sth indicator layer, s is a positive integer less than P, and P is an integer greater than 1.

[0032] For example, when P is 2, the electronic device includes a first indicator layer and a second indicator layer having a subordinate relationship, and the first indicator layer is the parent indicator layer of the second indicator layer. When P is 3, the electronic device includes a first indicator layer, a second indicator layer, and a third indicator layer, the first indicator layer is the parent indicator layer of the second indicator layer, and the second indicator layer is the parent indicator layer of the third indicator layer.

[0033] In addition, when P is greater than 1, each indicator in the sth indicator layer corresponds to at least one indicator in the s+1th indicator layer, and different indicators in the sth indicator layer correspond to different indicators in the s+1th indicator layer. There is at least one indicator in the sth indicator layer that corresponds to two or more indicators in the s+1th indicator layer. Therefore, the number of indicators included in the sth indicator layer is less than the number of indicators included in the s+1th indicator layer.

[0034] The sum of the weight values ​​of the indicators in the first indicator layer of the P indicator layers is 1, and the sum of the weight values ​​of all indicators corresponding to the same indicator in the previous indicator layer in the other indicator layers is 1. In actual applications, the weight value of each indicator can be determined by the operating requirements of the electronic device, and this embodiment of the application does not limit this.

[0035] For easier understanding, the following is an example with reference to Table 1:

[0036] Figure 1 This is an example of the first and second indicator layers when the electronic device is a server. It is understood that the indicators included in each indicator layer may be expressed in different forms for electronic devices of different forms. In actual applications, the electronic device may be a server, a computer, a mobile phone, etc.

[0037] In Table 1, the first indicator layer includes three indicators: product status, operation status and historical status. The weight value of product status is 0.2, the weight value of operation status is 0.4, and the weight value of historical status is 0.4. The sum of the weight values ​​of product status, operation status and historical status is 1.

[0038] The second indicator layer includes:

[0039] The server brand and server type corresponding to the product status have a weight of 0.5 for the server brand and 0.5 for the server type. The sum of the weights of the server brand and server type is 1.

[0040] The processor utilization, memory utilization, disk utilization, server energy consumption, and computer room humidity temperature corresponding to the operating status are as follows: the weight of the processor utilization is 0.3, the weight of the memory utilization is 0.3, the weight of the disk utilization is 0.2, the weight of the server energy consumption is 0.1, and the weight of the computer room humidity temperature is 0.1. The sum of the processor utilization, memory utilization, disk utilization, server energy consumption, and computer room humidity temperature is 1;

[0041] The number of alarms and the duration of alarms corresponding to the historical status have a weight of 0.5 for the number of alarms and a weight of 0.5 for the duration of alarms. The sum of the weights of the number of alarms and the duration of alarms is 1.

[0042] Table 1: Server's first and second indicator layers

[0043]

[0044] 2) The electronic device corresponds to m health assessment levels, where m is an integer greater than 1. In practical applications, the m health assessment levels can be set by system default or selected by the user, depending on the actual situation and are not limited in this embodiment. Optionally, the m health assessment levels can include at least two of the following: good, normal, sub-healthy, and faulty.

[0045] See also Figure 1 , Figure 1 This is one of the flow charts of the health assessment method provided in the embodiment of the present application.

[0046] like Figure 1 As shown, the health assessment method may include the following steps:

[0047] Step 101: Obtain a first 1×k matrix, where the first matrix includes k weight values ​​corresponding to the k indicators, and the sum of the k weight values ​​is 1.

[0048] First matrix

[0049] in, represents the weight value of the first indicator among the k indicators, Represents the weight value of the second indicator among the k indicators, and so on. represents the weight value of the kth indicator among the k indicators.

[0050] For ease of understanding, we continue to illustrate with an example based on Table 1. In this case, k=3, First matrix = (0.2, 0.4, 0.4).

[0051] Step 102: Obtain a k×m second matrix, where the second matrix includes k×m first memberships, and the k×m first memberships include m memberships of each of the k indicators to the m health evaluation levels.

[0052]

[0053] Among them, B1 and (b 11 ,b 12 ,…,b 1m ) corresponds to the m membership of the first indicator among the k indicators to the m health evaluation levels, B2 and (b 21 ,b 21, …,b 2m ) corresponds to the m membership of the second indicator among the k indicators to the m health evaluation levels, and so on, B k with b k1 ,b k2,…,b km Correspondingly, it represents the m membership degrees of the kth indicator among the k indicators to the m health evaluation levels.

[0054] During specific implementation, the m degrees of membership of each of the k indicators to the m health evaluation levels may be related to the number of indicator layers included in the electronic device.

[0055] In the case where the electronic device includes only the first indicator layer, the m memberships of each of the k indicators to the m health evaluation levels can be calculated using a membership function of the indicator to the m health evaluation levels.

[0056] When the electronic device also includes a second indicator layer having an affiliation with the first indicator layer, and the first indicator layer is the parent indicator layer of the second indicator layer, the m affiliations of each of the k indicators to the m health evaluation levels can be calculated by calculating the affiliations of all indicators in the second indicator layer corresponding to the indicator to the m health evaluation levels.

[0057] It should be noted that, when P is greater than 2, the m memberships of each indicator in the Pth indicator layer of the P indicator layers to the m health evaluation levels can be calculated by using the membership function of the indicator to the m health evaluation levels. The m memberships of each indicator in other indicator layers of the P indicator layers to the m health evaluation levels can be calculated by using the m memberships of all indicators corresponding to the indicator in the next indicator layer of the indicator layer to the m health evaluation levels.

[0058] Step 103: multiply the first matrix and the second matrix to obtain a third matrix of 1×m, where the third matrix includes m second memberships, and the m second memberships include the m memberships of the electronic device to the m health assessment levels.

[0059]

[0060] Wherein, C1 represents the membership of the electronic device to the first health level among the m health evaluation levels, C2 represents the membership of the electronic device to the second health level among the m health evaluation levels, and so on. k Indicates the membership of the electronic device to the mth health level among the m health evaluation levels.

[0061] Step 104: Determine the health evaluation level corresponding to the target membership as the health evaluation result of the electronic device, wherein the target membership is the largest membership among the m second memberships.

[0062] For example: Assume C1, C2, ..., C m is C2, then the electronic device determines the health level corresponding to C2, that is, the second health level among the m health evaluation levels, as the health evaluation result of the electronic device.

[0063] In the health assessment method of this embodiment, the electronic device includes at least a first indicator layer, the first indicator layer includes k indicators; the electronic device corresponds to m health assessment levels, where m is an integer greater than 1. The electronic device obtains a 1×k first matrix, the first matrix includes k weight values ​​corresponding to the k indicators, and the sum of the k weight values ​​is 1; obtains a k×m second matrix, the second matrix includes k×m first memberships, and the k×m first memberships include the m memberships of each of the k indicators to the m health assessment levels; multiplies the first matrix and the second matrix to obtain a 1×m third matrix, the third matrix includes m second memberships, and the m second memberships include the m memberships of the electronic device to the m health assessment levels; and determines the health assessment level corresponding to the target membership as the health assessment result of the electronic device, and the target membership is the membership with the largest membership among the m second memberships. It can be seen that this embodiment integrates the membership of various indicators of the electronic device to m health evaluation levels through fuzzy mathematics to obtain the health evaluation result of the electronic device, thereby improving the reliability of the health evaluation.

[0064] The following describes how to obtain the second matrix in the embodiment of the present application:

[0065] Scenario 1: The electronic device further includes a second indicator layer having a subordinate relationship with the first indicator layer, the first indicator layer is a parent indicator layer of the second indicator layer, and the second indicator layer includes n indicators, where n is an integer greater than k.

[0066] In scenario one, the m memberships of each of the k indicators to the m health evaluation levels can be calculated by calculating the memberships of all indicators in the second indicator layer corresponding to the indicator to the m health evaluation levels.

[0067] Optionally, obtaining the k×m second matrix includes:

[0068] Determine u indicators among the n indicators corresponding to the i-th indicator among the k indicators, where i is a positive integer less than or equal to k, and u is a positive integer greater than 1 and less than or equal to u;

[0069] Obtaining a 1×u fourth matrix, the fourth matrix including u weight values ​​corresponding to the u indicators, and the sum of the u weight values ​​is 1;

[0070] Obtaining a u×m fifth matrix, the fifth matrix including u×m third memberships, the u×m third memberships being m memberships of each indicator in the u indicators to the m health assessment levels;

[0071] Multiplying the fourth matrix and the fifth matrix to obtain a 1×m sixth matrix corresponding to the i-th index;

[0072] A k×m second matrix is ​​obtained according to the 1×m sixth matrix corresponding to the i-th index.

[0073] The fourth matrix = (a1, a2, ..., a u )

[0074] Among them, a1 represents the weight value of the first indicator among the u indicators, a2 represents the weight value of the second indicator among the u indicators, and so on. u Indicates the weight value of the u-th indicator among the u indicators.

[0075]

[0076] Among them, R1 and (r 11 ,r 12 ,…,r 1m ) corresponds to the m membership of the first indicator among the u indicators to the m health evaluation levels, R2 and (r 21 ,r 21, …,r 2m ) corresponds to the m membership of the second indicator among the u indicators to the m health evaluation levels, and so on, R u With r u1 ,r u2 ,…,r um Correspondingly, it represents the m membership degrees of the u-th indicator among the u indicators to the m health evaluation levels.

[0077]

[0078] In specific implementation, the m memberships of the i-th indicator among the k indicators to the m health evaluation levels can be obtained by the sixth matrix B i Therefore, k 1×m sixth matrices can be obtained, and a k×m second matrix can be obtained by combining k 1×m sixth matrices.

[0079] In scenario one, the m memberships of each of the u indicators to the m health evaluation levels are obtained in the same principle as the m memberships of each of the k indicators to the m health evaluation levels. For details, please refer to the above description and will not be repeated here.

[0080] Optionally, when the electronic device includes only the first indicator layer and the second indicator layer, obtaining the fifth matrix of u×m includes:

[0081] Obtain m first membership functions of the g-th indicator among the u indicators to the m health assessment levels, where g is a positive integer less than or equal to u;

[0082] Determine, according to the value of the g-th indicator and the m first membership functions, m third memberships corresponding to the g-th indicator, the m third memberships being the m memberships of the g-th indicator to the m health assessment levels;

[0083] Generate a 1×m seventh matrix corresponding to the g-th indicator according to the m third membership degrees corresponding to the g-th indicator;

[0084] A fifth matrix of u×m is generated according to the seventh matrix of 1×m corresponding to the g-th index.

[0085] In this optional embodiment, the second indicator layer is the Pth indicator layer among the P indicator layers. Therefore, the m memberships of each indicator in the u indicators to the m health evaluation levels can be calculated through the membership function of the indicator to the m health evaluation levels.

[0086] In a specific implementation, the electronic device uses different membership functions when calculating the membership of a certain indicator to different health assessment levels. Therefore, for each indicator, the electronic device can first obtain m membership functions of the indicator to m health assessment levels; then, the value of the indicator is substituted into the m membership functions to obtain m memberships, i.e., the m memberships of the indicator to the m health assessment levels. The membership functions corresponding to the indicators can be determined based on historical data.

[0087] Seventh matrix R ig =(r ig-1 ,r ig-2 ,…,r ig-m )

[0088] Among them, R ig Indicates the membership of the gth indicator in the second layer of indicators corresponding to the i-th indicator in the first indicator layer to the m health evaluation levels. ig-1 represents the membership degree of the gth indicator in the second layer corresponding to the i-th indicator in the first indicator layer to the first health level in the m health evaluation levels, r ig-2It represents the membership of the gth indicator in the second layer of indicators corresponding to the ith indicator in the first indicator layer to the second health level in the m health evaluation levels, and so on, r ig-m It represents the membership of the gth indicator in the second layer of indicators corresponding to the i-th indicator in the first indicator layer to the m-th health level in the m health evaluation levels.

[0089] In specific implementation, the m memberships of the g-th indicator among the u indicators to the m health evaluation levels can be obtained by the seventh matrix R g Therefore, we can obtain u 1×m seventh matrices, and by combining u 1×m seventh matrices, we can obtain a u×m fifth matrix.

[0090] For easier understanding, the following example is given based on Table 1.

[0091] When the i-th indicator is in a historical state, the u indicators include the number of alarms and the alarm duration, and the fourth matrix = (0.5, 0.5).

[0092] Assume that the m health evaluation levels include {healthy, sub-healthy}.

[0093] The membership function of the number of alarms to “health” is:

[0094]

[0095] The membership function of the number of alarms to “sub-health” is:

[0096]

[0097] The membership degree R of the number of alarms to the m health evaluation levels 31 :

[0098] R 31 =(r 31-1 ,r 31-2 )

[0099] The membership degree B3 of the historical status to the m health evaluation levels:

[0100]

[0101] The membership degree C of the electronic device to the m health assessment levels is:

[0102]

[0103] In scenario one, the electronic device integrates the membership of each indicator in at least two indicator layers with affiliation to m health evaluation levels by means of fuzzy data to obtain m memberships of the electronic device to the m health evaluation levels, and determines the health evaluation level corresponding to the maximum value of the m memberships as the health evaluation result of the electronic device, thereby improving the reliability of the health evaluation.

[0104] Scenario 2: The electronic device only includes the first indicator layer.

[0105] In scenario 2, the m memberships of each of the k indicators to the m health evaluation levels can be calculated using a membership function of the indicator to the m health evaluation levels.

[0106] Optionally, obtaining the k×m second matrix includes:

[0107] Obtain m second membership functions of the j-th indicator among the k indicators to the m health assessment levels, where j is a positive integer less than or equal to k;

[0108] Determining m first memberships corresponding to the j-th indicator according to the value of the j-th indicator and the m second membership functions;

[0109] Generate a 1×m eighth matrix corresponding to the j-th indicator according to the m first membership degrees corresponding to the j-th indicator;

[0110] A k×m second matrix is ​​generated according to the 1×m eighth matrix corresponding to the j-th index.

[0111] The method for obtaining the second k×m matrix in this optional implementation is the same as the method for obtaining the fifth u×m matrix in scenario one. Please refer to the above description for details and will not be repeated here.

[0112] In scenario two, even if the electronic device only includes one indicator layer including multiple indicators, the membership of each indicator of this layer to m health evaluation levels is integrated through fuzzy mathematics to obtain the health evaluation result of the electronic device, thereby improving the reliability of the health evaluation.

[0113] It should be noted that the various optional implementation methods introduced in the embodiments of the present application can be implemented in combination with each other or separately if they do not conflict with each other, and the embodiments of the present application do not limit this.

[0114] For easier understanding, the following examples are provided:

[0115] In this example, the health degree calculation can be performed based on product data, operation data, and alarm data using a two-layer fuzzy hierarchy to obtain the service health degree membership.

[0116] The main process is as follows Figure 2 As shown:

[0117] Step 201: Obtain historical and real-time indicator data of the server, including product data, operation data, and alarm data, as health assessment indicators.

[0118] Step 202: Establish a two-layer evaluation factor set and perform preprocessing.

[0119] As shown in Table 1: The first-tier indicators include broad categories such as product status, operating status, and historical status. The second-tier indicators include factors such as server type, brand, resource utilization, room temperature and humidity, equipment energy consumption, and alarm data.

[0120] Step 203: Determine the health evaluation level.

[0121] Such as good, normal, sub-healthy, and faulty.

[0122] Step 204: Determine the indicator weight and membership.

[0123] In specific implementation, the evaluation is conducted based on a single factor (i.e., an indicator) of the bottom-level second-layer indicator to determine the degree of membership of each evaluation subject to the health evaluation level in step 203. Each indicator is quantified in turn, and an appropriate membership function is selected based on the distribution morphology characteristics of each evaluation factor and the determination principle of the historical function to determine the degree of membership of the evaluated subject to the health evaluation level. The corresponding weights are selected based on expert experience, and the sum of the weights of the first-layer indicators is 1, and the sum of the weights of all indicators in the second-layer indicators corresponding to the same indicator of the second-layer indicators is 1.

[0124] For example, two levels of health evaluation are determined: {healthy, sub-healthy}, and multi-layer evaluation indicators are used, with weights shown in Table 1.

[0125] By evaluating the nine evaluation factors separately, three fuzzy relationship matrices can be obtained.

[0126] Step 205: Build a model.

[0127] In specific implementation, a model is established based on the membership of the evaluated object (i.e., indicator) to the health evaluation level to obtain the fuzzy comprehensive evaluation results of the electronic device for each health evaluation level: C = (C1, C2,…, Cm), where Ci corresponds to the membership of each evaluation level.

[0128] (a) For a single indicator i of the first level, its membership degree Bi to m health evaluation levels is:

[0129]

[0130] Among them, u represents the number of indicators corresponding to this indicator in the second layer.

[0131] Assuming that the first-level indicators have k influencing factors, we can get:

[0132]

[0133] Combining the above B with the weight of the first layer matrix, we get:

[0134]

[0135] Step 206: Output the evaluation result.

[0136] In specific implementation, according to the fuzzy comprehensive evaluation results of each evaluation object obtained above: C = (C1, C2, ..., Cm), the health level corresponding to the maximum membership degree Ci is selected as the health evaluation result of the server.

[0137] In the embodiments of the present application, from the perspective of multi-factor integration, a detailed analysis is conducted on various indicators. At the same time, the characteristics of various indicators in specific practice are combined, different analysis strategies are matched, and fuzzy mathematics methods are innovatively used to integrate various indicators to obtain the health level of the server. Then, according to the corresponding health level, the server health is objectively and comprehensively evaluated, and the health status of the server is correctly quantified and evaluated.

[0138] See also Figure 3 , Figure 3 This is a structural diagram of a health assessment device provided in an embodiment of the present application. Applied to an electronic device, the electronic device includes at least a first indicator layer, which includes k indicators; the electronic device corresponds to m health assessment levels, where m is an integer greater than 1.

[0139] like Figure 3 As shown, the health assessment device 300 includes:

[0140] The health assessment device comprises:

[0141] A first acquisition module 301 is configured to acquire a 1×k first matrix, where the first matrix includes k weight values ​​corresponding to the k indicators, and the sum of the k weight values ​​is 1;

[0142] A second acquisition module 302 is configured to acquire a k×m second matrix, where the second matrix includes k×m first memberships, and the k×m first memberships include m memberships of each of the k indicators to the m health assessment levels;

[0143] A third acquisition module 303 is configured to multiply the first matrix and the second matrix to obtain a 1×m third matrix, where the third matrix includes m second memberships, and the m second memberships include the m memberships of the electronic device to the m health assessment levels;

[0144] The determination module 304 is configured to determine the health evaluation level corresponding to the target membership as the health evaluation result of the electronic device, wherein the target membership is the largest membership among the m second memberships.

[0145] Optionally, the electronic device further includes a second indicator layer having a subordinate relationship with the first indicator layer, the first indicator layer is a parent indicator layer of the second indicator layer, and the second indicator layer includes n indicators, where n is an integer greater than k;

[0146] The second acquisition module 302 includes:

[0147] a determining unit, configured to determine u indicators among the n indicators corresponding to the i-th indicator among the k indicators, where i is a positive integer less than or equal to k, and u is a positive integer greater than 1 and less than or equal to u;

[0148] A first acquiring unit is configured to acquire a 1×u fourth matrix, where the fourth matrix includes u weight values ​​corresponding to the u indicators, and the sum of the u weight values ​​is 1;

[0149] A second acquisition unit is configured to acquire a u×m fifth matrix, where the fifth matrix includes u×m third memberships, where the u×m third memberships are the m memberships of each indicator in the u indicators to the m health assessment levels;

[0150] a third obtaining unit, configured to multiply the fourth matrix and the fifth matrix to obtain a 1×m sixth matrix corresponding to the i-th index;

[0151] The fourth acquisition unit is configured to obtain a k×m second matrix according to a 1×m sixth matrix corresponding to the i-th indicator.

[0152] Optionally, when the electronic device includes only the first indicator layer and the second indicator layer, the second acquiring unit is specifically configured to:

[0153] Obtain m first membership functions of the g-th indicator among the u indicators to the m health assessment levels, where g is a positive integer less than or equal to u;

[0154] Determine, according to the value of the g-th indicator and the m first membership functions, m third memberships corresponding to the g-th indicator, the m third memberships being the m memberships of the g-th indicator to the m health assessment levels;

[0155] Generate a 1×m seventh matrix corresponding to the g-th indicator according to the m third membership degrees corresponding to the g-th indicator;

[0156] A fifth matrix of u×m is generated according to the seventh matrix of 1×m corresponding to the g-th index.

[0157] Optionally, when the electronic device only includes the first indicator layer, the second acquisition module 302 includes:

[0158] a fifth obtaining unit, configured to obtain m second membership functions of the j-th indicator among the k indicators to the m health assessment levels, where j is a positive integer less than or equal to k;

[0159] a determining unit, configured to determine m first memberships corresponding to the j-th indicator according to the value of the j-th indicator and the m second membership functions;

[0160] A first generating unit is configured to generate a 1×m eighth matrix corresponding to the j-th indicator according to the m first membership degrees corresponding to the j-th indicator;

[0161] The second generating unit is configured to generate a k×m second matrix according to the 1×m eighth matrix corresponding to the j-th indicator.

[0162] The health assessment device 300 can implement each process of the method embodiment in the embodiment of the present application and achieve the same beneficial effects. To avoid repetition, it will not be described here.

[0163] The present application also provides an electronic device. Figure 4 The electronic device may include a processor 401, a memory 402, and a program 4021 stored in the memory 402 and executable on the processor 401. When the program 4021 is executed by the processor 401, any steps in the method embodiment may be implemented and the same beneficial effects may be achieved, which will not be described in detail here.

[0164] Those skilled in the art will appreciate that all or part of the steps in implementing the above-described method embodiments can be accomplished by hardware associated with program instructions, and the program can be stored in a readable medium. The present application also provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can implement any of the steps in the method embodiments and achieve the same technical effects. To avoid repetition, these steps will not be described here.

[0165] The storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0166] The above is a preferred implementation of the embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A health assessment method, performed by an electronic device, characterized in that: The electronic device comprises at least a first indicator layer, wherein the first indicator layer comprises k indicators; the electronic device corresponds to m health evaluation levels, where m is an integer greater than 1; The method comprises: Obtain a first 1×k matrix, where the first matrix includes k weight values ​​corresponding to the k indicators, and the sum of the k weight values ​​is 1; Obtaining a k×m second matrix, where the second matrix includes k×m first memberships, and the k×m first memberships include m memberships of each of the k indicators to the m health assessment levels; Multiplying the first matrix and the second matrix to obtain a 1×m third matrix, where the third matrix includes m second memberships, and the m second memberships include the m memberships of the electronic device to the m health assessment levels; Determining a health evaluation level corresponding to a target membership as a health evaluation result of the electronic device, wherein the target membership is the largest membership among the m second memberships; The electronic device further includes a second indicator layer having a subordinate relationship with the first indicator layer, the first indicator layer being a parent indicator layer of the second indicator layer, the second indicator layer including n indicators, where n is an integer greater than k; The obtaining of the second k×m matrix includes: Determine u indicators among the n indicators corresponding to the i-th indicator among the k indicators, where i is a positive integer less than or equal to k, and u is a positive integer greater than 1 and less than or equal to u; Obtaining a 1×u fourth matrix, the fourth matrix including u weight values ​​corresponding to the u indicators, and the sum of the u weight values ​​is 1; Obtaining a u×m fifth matrix, the fifth matrix including u×m third memberships, the u×m third memberships being m memberships of each indicator in the u indicators to the m health assessment levels; Multiplying the fourth matrix and the fifth matrix to obtain a 1×m sixth matrix corresponding to the i-th index; A k×m second matrix is ​​obtained according to the 1×m sixth matrix corresponding to the i-th index.

2. The method according to claim 1, characterized in that In a case where the electronic device includes only the first indicator layer and the second indicator layer, obtaining a fifth matrix of u×m includes: Obtain m first membership functions of the g-th indicator among the u indicators to the m health assessment levels, where g is a positive integer less than or equal to u; Determine, according to the value of the g-th indicator and the m first membership functions, m third memberships corresponding to the g-th indicator, the m third memberships being the m memberships of the g-th indicator to the m health assessment levels; Generate a 1×m seventh matrix corresponding to the g-th indicator according to the m third membership degrees corresponding to the g-th indicator; A fifth matrix of u×m is generated according to the seventh matrix of 1×m corresponding to the g-th index.

3. The method according to claim 1, characterized in that In a case where the electronic device includes only the first indicator layer, obtaining the k×m second matrix includes: Obtain m second membership functions of the j-th indicator among the k indicators to the m health assessment levels, where j is a positive integer less than or equal to k; Determining m first memberships corresponding to the j-th indicator according to the value of the j-th indicator and the m second membership functions; Generate a 1×m eighth matrix corresponding to the j-th indicator according to the m first membership degrees corresponding to the j-th indicator; A k×m second matrix is ​​generated according to the 1×m eighth matrix corresponding to the j-th index.

4. A health assessment device, applied to electronic equipment, characterized in that: The electronic device comprises at least a first indicator layer, wherein the first indicator layer comprises k indicators; the electronic device corresponds to m health evaluation levels, where m is an integer greater than 1; The health assessment device comprises: A first acquisition module is configured to acquire a first 1×k matrix, where the first matrix includes k weight values ​​corresponding to the k indicators, and the sum of the k weight values ​​is 1; A second acquisition module is used to acquire a k×m second matrix, where the second matrix includes k×m first memberships, and the k×m first memberships include m memberships of each of the k indicators to the m health assessment levels; a third acquisition module, configured to multiply the first matrix and the second matrix to obtain a 1×m third matrix, wherein the third matrix includes m second memberships, and the m second memberships include the m memberships of the electronic device to the m health assessment levels; a determination module, configured to determine a health evaluation level corresponding to a target membership as a health evaluation result of the electronic device, wherein the target membership is a membership with the largest membership among the m second memberships; The electronic device further includes a second indicator layer having a subordinate relationship with the first indicator layer, the first indicator layer being a parent indicator layer of the second indicator layer, the second indicator layer including n indicators, where n is an integer greater than k; The second acquisition module includes: a determining unit, configured to determine u indicators among the n indicators corresponding to the i-th indicator among the k indicators, where i is a positive integer less than or equal to k, and u is a positive integer greater than 1 and less than or equal to u; A first acquiring unit is configured to acquire a 1×u fourth matrix, where the fourth matrix includes u weight values ​​corresponding to the u indicators, and the sum of the u weight values ​​is 1; A second acquisition unit is configured to acquire a u×m fifth matrix, where the fifth matrix includes u×m third memberships, where the u×m third memberships are the m memberships of each indicator in the u indicators to the m health assessment levels; a third obtaining unit, configured to multiply the fourth matrix and the fifth matrix to obtain a 1×m sixth matrix corresponding to the i-th index; The fourth acquisition unit is configured to obtain a k×m second matrix according to a 1×m sixth matrix corresponding to the i-th indicator.

5. The health assessment device according to claim 4, characterized in that: In a case where the electronic device includes only the first indicator layer and the second indicator layer, the second acquiring unit is specifically configured to: Obtain m first membership functions of the g-th indicator among the u indicators to the m health assessment levels, where g is a positive integer less than or equal to u; Determine, according to the value of the g-th indicator and the m first membership functions, m third memberships corresponding to the g-th indicator, the m third memberships being the m memberships of the g-th indicator to the m health assessment levels; Generate a 1×m seventh matrix corresponding to the g-th indicator according to the m third membership degrees corresponding to the g-th indicator; A fifth matrix of u×m is generated according to the seventh matrix of 1×m corresponding to the g-th index.

6. The health assessment device according to claim 4, characterized in that: In the case that the electronic device only includes the first indicator layer, the second acquisition module includes: a fifth obtaining unit, configured to obtain m second membership functions of the j-th indicator among the k indicators to the m health assessment levels, where j is a positive integer less than or equal to k; a determining unit, configured to determine m first memberships corresponding to the j-th indicator according to the value of the j-th indicator and the m second membership functions; A first generating unit is configured to generate a 1×m eighth matrix corresponding to the j-th indicator according to the m first membership degrees corresponding to the j-th indicator; The second generating unit is configured to generate a k×m second matrix according to the 1×m eighth matrix corresponding to the j-th indicator.

7. An electronic device comprising: A transceiver, a memory, a processor, and a program stored in the memory and executable on the processor; wherein the processor is configured to read the program in the memory to implement the steps of the health assessment method as described in any one of claims 1 to 3.

8. A readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the steps of the health assessment method according to any one of claims 1 to 3 are implemented.

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