Equipment status evaluation method and system based on artificial intelligence

Abstract

The present invention provides an equipment state evaluation method and system based on artificial intelligence. The method comprises obtaining and determining a first weight and a second weight; adjusting the first weight and the second weight to determine a comprehensive weight; determining a feature mapping set, judging whether a feature mapping in the feature mapping set meets a default condition, and if the feature mapping in the feature mapping set meets the default condition, updating the corresponding feature mapping in the feature mapping set; fusing the updated feature mapping set to obtain a state evaluation value, determining an interval mapping relationship between the state evaluation value in an equipment evaluation interval and a state mapping table, and outputting a corresponding equipment evaluation state. The present invention can improve the rationality of data and the expression ability of feature mapping for equipment state, improve the stability of equipment state evaluation, and can timely and effectively evaluate the operating state of the equipment, with high evaluation efficiency and accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment status evaluation, and specifically relates to an equipment status evaluation method and system based on artificial intelligence. Background Art

[0002] With the development of science and technology, power equipment, wind power equipment, fire-fighting equipment, monitoring equipment, etc. are widely used in various industries. With the popularization of intelligent equipment, the evaluation of equipment status can play an early warning role in the use, operation and maintenance of equipment. However, the existing equipment status evaluation usually records the equipment operation data manually and regularly, and evaluates the equipment status based on the experience of the staff. This will result in problems such as low accuracy and low efficiency of status evaluation. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides an equipment status evaluation method and system based on artificial intelligence, which are used to solve the technical problems in the prior art.

[0004] On the one hand, the present invention provides the following technical solution, a method for evaluating equipment status based on artificial intelligence, comprising:

[0005] Acquire a historical operation data set of the device before the current moment, and determine a first weight and a second weight based on the historical operation data set of the device;

[0006] Adjusting the first weight and the second weight to obtain a first adjusted weight and a second adjusted weight, and determining a comprehensive weight based on the first adjusted weight and the second adjusted weight;

[0007] Establishing a device evaluation interval and state mapping table, determining a feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table, judging whether a feature mapping in the feature mapping set meets a dishonesty condition, and if a feature mapping in the feature mapping set meets the dishonesty condition, updating the corresponding feature mapping in the feature mapping set to obtain an updated feature mapping set;

[0008] The updated feature mapping set is fused to obtain a state evaluation value, and an interval mapping relationship between the state evaluation value in the device evaluation interval and the state mapping table is determined to output a corresponding device evaluation state.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first obtains a historical data set of equipment operation before the current moment, and determines a first weight and a second weight based on the historical data set of equipment operation; then the first weight and the second weight are adjusted to obtain a first adjustment weight and a second adjustment weight, and a comprehensive weight is determined based on the first adjustment weight and the second adjustment weight; then an equipment evaluation interval and a state mapping table is established, a feature mapping set is determined based on the comprehensive weight and the equipment evaluation interval and the state mapping table, and it is judged whether the feature mapping in the feature mapping set meets the untrustworthy condition. If the feature mapping in the feature mapping set meets the untrustworthy condition, the corresponding feature mapping in the feature mapping set is updated to obtain an updated feature mapping set; finally, the updated feature mapping set is fused to obtain a state evaluation value, and an interval mapping relationship of the state evaluation value in the equipment evaluation interval and the state mapping table is determined to output the corresponding equipment evaluation state. The present invention can improve the rationality of data and the ability of the feature mapping to express the equipment state, and improve the stability of the equipment state evaluation by adjusting and fusing the weights. At the same time, the present invention can timely and effectively evaluate the operation state of the equipment, and the evaluation efficiency is high and the accuracy is high.

[0010] Preferably, the step of determining the first weight and the second weight based on the equipment historical operation data set includes:

[0011] A data evaluation matrix is ​​constructed based on the historical operation data set of the equipment, and a first weight is determined based on the data evaluation matrix :

[0012] ;

[0013] In the formula, Represents the first Line The elements of the column, represents the number of row vectors in the data evaluation matrix, represents the gap regulation factor;

[0014] Determine the first The data value of the data :

[0015] ;

[0016] Based on The data value of the data Calculate the second weight :

[0017] .

[0018] Preferably, the first weight and the second weight are adjusted to obtain a first adjusted weight and a second adjusted weight, and the step of determining a comprehensive weight based on the first adjusted weight and the second adjusted weight includes:

[0019] Determine a safe operating range for each data in the device historical operating data set, and adjust the first weight based on the safe operating range to obtain a first adjusted weight :

[0020] ;

[0021] In the formula, is the first weight, is the first adjustment factor, Indicates the number of the historical operation data set of the device data, , Respectively represent the upper and lower limits of the safe operating range;

[0022] The second weight is adjusted to obtain a second adjusted weight :

[0023] ;

[0024] In the formula, Represents the first Line The elements of the column, is the second weight, represents the second adjustment factor;

[0025] Determine a comprehensive weight based on the first adjustment weight and the second adjustment weight :

[0026] .

[0027] Preferably, the step of determining the feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table comprises:

[0028] Identify the device evaluation interval and the number of state intervals in the state mapping table , and according to the number of state intervals Determine the feature map matrix :

[0029] ;

[0030] ;

[0031] In the formula, Represents the feature map matrix Middle Line The elements of the column, Indicates The upper and lower bounds of the state interval, Indicates the number of the historical operation data set of the device The data is mapped to the equipment evaluation interval and the state in the table. The membership degree of each state interval;

[0032] Based on the feature mapping matrix With the comprehensive weight Determine the feature map set :

[0033] ;

[0034] In the formula, Represents the feature map matrix Middle row vectors, Represents a feature map set Middle feature maps.

[0035] Preferably, the step of determining whether a feature map in the feature map set meets the untrustworthy condition, and if the feature map in the feature map set meets the untrustworthy condition, updating the corresponding feature map in the feature map set to obtain an updated feature map set includes:

[0036] Building a mapping function , and determine the feature map set Each feature map in the feature value after being mapped by the mapping function ,like , then the corresponding feature map is retained. If , then the corresponding feature map is removed from the feature map set Eliminate from the , to obtain the eliminated feature map set ,in, To remove feature map set The number of feature maps in ;

[0037] The feature map set is removed The feature maps in the total extraction are processed according to different quantities times, to get Value Mapping Set ,in, Indicates The first feature maps, For the The number of feature maps in the sampled map, ;

[0038] Calculate each of the extracted value mapping sets The default value , and calculate all the eliminated feature map sets The default value The average :

[0039] ;

[0040] ;

[0041] Based on the untrustworthy value of the extracted mapping set, it is judged whether the extracted mapping set meets the untrustworthy condition. If the extracted mapping set meets the untrustworthy condition, the mapping set to be updated is determined and updated to obtain an updated feature mapping set.

[0042] Preferably, the step of judging whether the extracted value mapping set meets the untrustworthy condition based on the untrustworthy value of the extracted value mapping set, and if the extracted value mapping set meets the untrustworthy condition, determining the mapping set to be updated and updating the mapping set to be updated to obtain an updated feature mapping set comprises:

[0043] Determine whether the default value of the sampling mapping set is greater than the average value , if the default value of the sampling mapping set is less than the average value , then the corresponding extracted value mapping set does not meet the default condition and the corresponding extracted value mapping set is used as the mapping set to be updated ;

[0044] Calculate the trust factor of the mapping set to be updated :

[0045] ;

[0046] In the formula, Indicates of the mappings to be updated feature maps, Indicates The trust factor of the mapping set to be updated;

[0047] Based on the trust factor Performing an update process on the mapping set to be updated to obtain an updated mapping set :

[0048] ;

[0049] Update the mapping set by Replace the mapping set to be updated in the sampling mapping set to obtain an updated feature mapping set.

[0050] Preferably, the step of fusing the updated feature mapping set to obtain a state evaluation value, determining an interval mapping relationship between the state evaluation value in the device evaluation interval and the state mapping table to output a corresponding device evaluation state includes:

[0051] The updated feature map set is fused to obtain a state evaluation value :

[0052] ;

[0053] In the formula, Indicates A set of updated feature maps;

[0054] Determine the upper and lower bounds of each state interval in the device evaluation interval and state mapping table, and based on the state evaluation value The specific value of determines the state evaluation value The corresponding state interval;

[0055] The state evaluation value The corresponding state interval is used as the final state interval, and the device evaluation state corresponding to the final state interval is output.

[0056] In a second aspect, the present invention provides the following technical solution, an artificial intelligence-based equipment status assessment system, the system comprising:

[0057] A weight determination module, used to obtain a data set of historical operation of the device before the current moment, and determine a first weight and a second weight based on the data set of historical operation of the device;

[0058] A weight adjustment module, configured to adjust the first weight and the second weight to obtain a first adjusted weight and a second adjusted weight, and determine a comprehensive weight based on the first adjusted weight and the second adjusted weight;

[0059] An updating module, used to establish a device evaluation interval and state mapping table, determine a feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table, judge whether a feature mapping in the feature mapping set meets the untrustworthy condition, and if the feature mapping in the feature mapping set meets the untrustworthy condition, update the corresponding feature mapping in the feature mapping set to obtain an updated feature mapping set;

[0060] The output module is used to fuse the updated feature mapping set to obtain a state evaluation value, determine the interval mapping relationship between the state evaluation value in the device evaluation interval and the state mapping table, and output the corresponding device evaluation state.

[0061] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the artificial intelligence-based equipment status assessment method as described above is implemented.

[0062] In a fourth aspect, the present invention provides the following technical solution: a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned artificial intelligence-based device status assessment method. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A flowchart of an artificial intelligence-based device status assessment method provided in Embodiment 1 of the present invention;

[0065] Figure 2 A structural block diagram of an artificial intelligence-based equipment status assessment system provided in Embodiment 2 of the present invention;

[0066] Figure 3 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0067] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0069] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0070] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0071] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0072] Embodiment 1

[0073] In the first embodiment of the present invention, Figure 1 As shown, an artificial intelligence-based equipment status assessment method includes:

[0074] S1. Obtain a historical data set of equipment operation before the current moment, and determine a first weight and a second weight based on the historical data set of equipment operation;

[0075] Specifically, the equipment historical operation data set here includes the operation data of the equipment within a preset time period before the current moment, and the equipment historical operation data set includes several data, each of which corresponds to a type of data. For power equipment, it may include current size, voltage size, operating temperature, ambient temperature, ambient humidity, etc., and each data corresponds to a group of data of the same type.

[0076] Wherein, the step S1 comprises:

[0077] S11, constructing a data evaluation matrix based on the historical operation data set of the device, and determining a first weight based on the data evaluation matrix :

[0078] ;

[0079] In the formula, Represents the first Line The elements of the column, represents the number of row vectors in the data evaluation matrix, represents the gap regulation factor;

[0080] Specifically, the data evaluation matrix here can be used to reflect the important relationship between each data in the equipment historical operation data set. Assuming that the equipment historical operation data set includes data, the corresponding data evaluation matrix is , and the diagonal elements in the matrix are 0.5, which means that the important relationship between a certain data and itself is normal. Correspondingly, if the important relationship between one data and another data is important, the value in the matrix is ​​greater than 0.5. Conversely, if the important relationship between one data and another data is not important, the value in the matrix is ​​less than 0.5. The data evaluation matrix can be determined by expert scoring or by changing the variables of each data and then observing the changes in the remaining data. At the same time, in this embodiment, .

[0081] S12, determining the first The data value of the data :

[0082] .

[0083] S13, based on The data value of the data Calculate the second weight :

[0084] .

[0085] Specifically, the first weight can be used to balance the influence of subjective factors on the equipment evaluation, and the second weight can be used to adjust the discreteness of each data.

[0086] S2. Adjusting the first weight and the second weight to obtain a first adjusted weight and a second adjusted weight, and determining a comprehensive weight based on the first adjusted weight and the second adjusted weight;

[0087] Wherein, the step S2 comprises:

[0088] S21, determining a safe operating range for each data in the device historical operating data set, and adjusting the first weight based on the safe operating range to obtain a first adjusted weight :

[0089] ;

[0090] In the formula, is the first weight, is the first adjustment factor, Indicates the number of the historical operation data set of the device data, , Respectively represent the upper and lower limits of the safe operating range;

[0091] Specifically, in this embodiment, the first adjustment factor , and adjusting the first weight can correct the subjective error information implicit in the first weight to avoid affecting the evaluation process of subsequent equipment.

[0092] S22: Adjust the second weight to obtain a second adjusted weight. :

[0093] ;

[0094] In the formula, Represents the first Line The elements of the column, is the second weight, represents the second adjustment factor;

[0095] Specifically, in this embodiment, the second adjustment factor The second weight is 0.75, and the adjustment of the second weight improves the balance of the data and the accuracy of subsequent equipment evaluation.

[0096] S23: Determine a comprehensive weight based on the first adjustment weight and the second adjustment weight. :

[0097] ;

[0098] Specifically, by combining the first adjustment weight and the second adjustment weight, the influence of the device status reflected between the data can be fully utilized to avoid missing important data.

[0099] S3, establishing a device evaluation interval and state mapping table, determining a feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table, judging whether a feature mapping in the feature mapping set meets the untrustworthy condition, and if a feature mapping in the feature mapping set meets the untrustworthy condition, updating the corresponding feature mapping in the feature mapping set to obtain an updated feature mapping set;

[0100] Specifically, the equipment evaluation interval and status mapping table specifically includes several status intervals and corresponding equipment evaluation statuses. In the present application, several status intervals take values ​​of 0-1. In the present application, there are five status intervals, and the intervals are 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8, and 0.8-1, respectively. The corresponding equipment evaluation statuses are healthy, good, attention, abnormal, and faulty. If it is in a healthy or good state, the number of equipment maintenance times can be reduced. If it is in an attention state, regular maintenance is required to avoid equipment failure. If it is in a faulty state, the number of maintenance times needs to be increased. If it is in a faulty state, the equipment needs to be stopped immediately for inspection and repair.

[0101] The step of determining the feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table includes:

[0102] S311, identifying the device evaluation interval and the number of state intervals in the state mapping table , and according to the number of state intervals Determine the feature map matrix :

[0103] ;

[0104] ;

[0105] In the formula, Represents the feature map matrix Middle Line The elements of the column, Indicates The upper and lower bounds of the state interval, Indicates the number of the historical operation data set of the device The data is mapped to the equipment evaluation interval and the state in the table. The membership degree of each state interval;

[0106] Specifically, the feature mapping matrix reflects the influence mapping relationship between each data and the device state and the influence relationship between the data.

[0107] S312, based on the feature mapping matrix With the comprehensive weight Determine the feature map set :

[0108] ;

[0109] In the formula, Represents the feature map matrix Middle row vectors, Represents a feature map set Middle feature maps;

[0110] Specifically, the feature maps in the feature map set can be used to reflect the direct impact of each data on the device state. In the subsequent steps, the evaluation state of the device is directly reflected through all the data by determining the state evaluation value.

[0111] The step of determining whether a feature map in the feature map set meets the untrustworthy condition, and if the feature map in the feature map set meets the untrustworthy condition, updating the corresponding feature map in the feature map set to obtain an updated feature map set includes:

[0112] S321. Constructing mapping function , and determine the feature map set Each feature map in the feature value after being mapped by the mapping function ,like , then the corresponding feature map is retained. If , then the corresponding feature map is removed from the feature map set Eliminate from the , to obtain the eliminated feature map set ,in, To remove feature map set The number of feature maps in .

[0113] S322, removing the feature mapping set The feature maps in the total extraction are processed according to different quantities times, to get Value Mapping Set ,in, Indicates The first feature maps, For the The number of feature maps in the sampled map, .

[0114] S323, calculate each of the extraction value mapping sets The default value , and calculate all the eliminated feature map sets The default value The average :

[0115] ;

[0116] ;

[0117] Specifically, the default value is used to reflect the interference relationship between each extraction value mapping set and the other extraction value mapping sets. The default value Less than average , it means that the extracted mapping set will affect the fusion of the other extracted mapping sets, which will lead to a paradox that affects the accuracy of subsequent evaluation states, so it needs to be updated.

[0118] S324, judging whether the extracted mapping set meets the untrustworthy condition based on the untrustworthy value of the extracted mapping set, and if the extracted mapping set meets the untrustworthy condition, determining the mapping set to be updated and updating the mapping set to be updated to obtain an updated feature mapping set;

[0119] Wherein, the step S324 includes:

[0120] S3241, determine whether the default value of the sampling value mapping set is greater than the average value , if the default value of the sampling mapping set is less than the average value , then the corresponding extracted value mapping set does not meet the default condition and the corresponding extracted value mapping set is used as the mapping set to be updated ;

[0121] S3242: Calculate the trust coefficient of the mapping set to be updated :

[0122] ;

[0123] In the formula, Indicates of the mappings to be updated feature maps, Indicates The trust factor of the mapping set to be updated.

[0124] S3243, based on the trust coefficient Performing an update process on the mapping set to be updated to obtain an updated mapping set :

[0125] .

[0126] S3244, by updating the mapping set Replace the mapping set to be updated in the sampling mapping set to obtain an updated feature mapping set.

[0127] S4, fusing the updated feature mapping set to obtain a state evaluation value, determining an interval mapping relationship between the state evaluation value in the device evaluation interval and the state mapping table, and outputting a corresponding device evaluation state;

[0128] Wherein, the step S4 comprises:

[0129] S41, fusing the updated feature mapping set to obtain a state evaluation value :

[0130] ;

[0131] In the formula, Indicates A set of updated feature maps;

[0132] S42, determining the upper and lower bounds of each state interval in the device evaluation interval and state mapping table, and based on the state evaluation value The specific value of determines the state evaluation value The corresponding status interval.

[0133] S43, the state evaluation value The corresponding state interval is used as the final state interval, and the device evaluation state corresponding to the final state interval is output.

[0134] The device state evaluation method based on artificial intelligence provided in the first embodiment of the present invention first obtains the device history operation data set before the current moment, determines the first weight and the second weight based on the device history operation data set; then adjusts the first weight and the second weight to obtain the first adjustment weight and the second adjustment weight, and determines the comprehensive weight based on the first adjustment weight and the second adjustment weight; then establishes a device evaluation interval and a state mapping table, determines a feature mapping set based on the comprehensive weight and the device evaluation interval and the state mapping table, judges whether the feature mapping in the feature mapping set meets the untrustworthy condition, and if the feature mapping in the feature mapping set meets the untrustworthy condition, updates the corresponding feature mapping in the feature mapping set to obtain an updated feature mapping set; finally, fuses the updated feature mapping set to obtain a state evaluation value, determines the interval mapping relationship of the state evaluation value in the device evaluation interval and the state mapping table, and outputs the corresponding device evaluation state. The present invention can improve the rationality of data and the expression ability of the feature mapping on the device state by adjusting and fusing the weights, and improves the stability of the device state evaluation. At the same time, the present invention can timely and effectively evaluate the operation state of the device, and the evaluation efficiency is high and the accuracy is high.

[0135] Embodiment 2

[0136] like Figure 2 As shown, in the second embodiment of the present invention, a device status assessment system based on artificial intelligence is provided, and the system includes:

[0137] A weight determination module 1 is used to obtain a data set of historical operation of the device before the current moment, and determine a first weight and a second weight based on the data set of historical operation of the device;

[0138] A weight adjustment module 2, configured to adjust the first weight and the second weight to obtain a first adjustment weight and a second adjustment weight, and determine a comprehensive weight based on the first adjustment weight and the second adjustment weight;

[0139] An updating module 3 is used to establish a device evaluation interval and state mapping table, determine a feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table, judge whether a feature mapping in the feature mapping set meets the untrustworthy condition, and if a feature mapping in the feature mapping set meets the untrustworthy condition, update the corresponding feature mapping in the feature mapping set to obtain an updated feature mapping set;

[0140] The output module 4 is used to fuse the updated feature mapping set to obtain a state evaluation value, determine the interval mapping relationship between the state evaluation value in the device evaluation interval and the state mapping table, and output the corresponding device evaluation state.

[0141] The weight determination module 1 comprises:

[0142] A first weight determination submodule is used to construct a data evaluation matrix based on the equipment historical operation data set, and determine a first weight based on the data evaluation matrix :

[0143] ;

[0144] In the formula, Represents the first Line The elements of the column, represents the number of row vectors in the data evaluation matrix, represents the gap regulation factor;

[0145] A data value determination submodule is used to determine the first The data value of the data :

[0146] ;

[0147] The second weight determination submodule is used to determine the weight of the The data value of the data Calculate the second weight :

[0148] .

[0149] The weight adjustment module 2 includes:

[0150] A first adjustment submodule is used to determine the safe operation range of each data in the device historical operation data set, and adjust the first weight based on the safe operation range to obtain a first adjustment weight :

[0151] ;

[0152] In the formula, is the first weight, is the first adjustment factor, Indicates the number of the historical operation data set of the device data, , Respectively represent the upper and lower limits of the safe operating range;

[0153] A second adjustment submodule, configured to adjust the second weight to obtain a second adjusted weight :

[0154] ;

[0155] In the formula, Represents the first Line The elements of the column, is the second weight, represents the second adjustment factor;

[0156] A comprehensive weight submodule, configured to determine a comprehensive weight based on the first adjustment weight and the second adjustment weight :

[0157] .

[0158] The updating module 3 comprises:

[0159] The feature mapping matrix determination submodule is used to identify the device evaluation interval and the number of state intervals in the state mapping table. , and according to the number of state intervals Determine the feature map matrix :

[0160] ;

[0161] ;

[0162] In the formula, Represents the feature map matrix Middle Line The elements of the column, Indicates The upper and lower bounds of the state interval, Indicates the number of the historical operation data set of the device The data is mapped to the equipment evaluation interval and the state in the table. The membership degree of each state interval;

[0163] A feature mapping set determination submodule is used to determine the feature mapping matrix based on the feature mapping matrix. With the comprehensive weight Determine the feature map set :

[0164] ;

[0165] In the formula, Represents the feature map matrix Middle row vectors, Represents a feature map set Middle feature maps.

[0166] The updating module 3 further includes:

[0167] Build submodules for building mapping functions , and determine the feature map set Each feature map in the feature value after being mapped by the mapping function ,like , then the corresponding feature map is retained. If , then the corresponding feature map is removed from the feature map set Eliminate from the , to obtain the eliminated feature map set ,in, To remove feature map set The number of feature maps in ;

[0168] The extraction submodule is used to remove the feature map set The feature maps in the total extraction are processed according to different quantities times, to get Value Mapping Set ,in, Indicates The first feature maps, For the The number of feature maps in the sampled map, ;

[0169] The dishonesty value calculation submodule is used to calculate each of the extraction value mapping sets The default value , and calculate all the eliminated feature map sets The default value The average :

[0170] ;

[0171] ;

[0172] The judgment submodule is used to judge whether the extracted value mapping set meets the untrustworthy condition based on the untrustworthy value of the extracted value mapping set. If the extracted value mapping set meets the untrustworthy condition, the mapping set to be updated is determined and updated to obtain an updated feature mapping set.

[0173] The judgment submodule includes:

[0174] The judgment unit is used to judge whether the default value of the sampling value mapping set is greater than the average value. , if the default value of the sampling mapping set is less than the average value , then the corresponding extracted value mapping set does not meet the default condition and the corresponding extracted value mapping set is used as the mapping set to be updated ;

[0175] A calculation unit, used to calculate the trust coefficient of the mapping set to be updated :

[0176] ;

[0177] In the formula, Indicates of the mappings to be updated feature maps, Indicates The trust factor of the mapping set to be updated;

[0178] An updating unit for updating a trust factor based on the trust factor Performing an update process on the mapping set to be updated to obtain an updated mapping set :

[0179] ;

[0180] Replacement unit for updating the mapping set by the Replace the mapping set to be updated in the sampling mapping set to obtain an updated feature mapping set.

[0181] The output module 4 comprises:

[0182] A fusion submodule is used to fuse the updated feature map set to obtain a state evaluation value :

[0183] ;

[0184] In the formula, Indicates A set of updated feature maps;

[0185] The interval determination submodule is used to determine the upper and lower bounds of each state interval in the device evaluation interval and state mapping table, and based on the state evaluation value The specific value of determines the state evaluation value The corresponding state interval;

[0186] The output submodule is used to output the state evaluation value The corresponding state interval is used as the final state interval, and the device evaluation state corresponding to the final state interval is output.

[0187] In some other embodiments of the present invention, the embodiments of the present invention provide the following technical solutions: a computer, comprising a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101, wherein the processor 101 implements the above-mentioned artificial intelligence-based equipment status assessment method when executing the computer program.

[0188] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0189] Among them, the memory 102 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 102 may be inside or outside the data processing device. In a specific embodiment, the memory 102 is a non-volatile memory. In a specific embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0190] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0191] The processor 101 implements the above-mentioned artificial intelligence-based device status assessment method by reading and executing computer program instructions stored in the memory 102.

[0192] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 3 As shown, the processor 101, the memory 102, and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0193] The communication interface 103 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present invention. The communication interface 103 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0194] The bus 100 includes hardware, software or both, and couples the components of the computer device to each other. The bus 100 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 100 may include one or more buses. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0195] The computer can execute the artificial intelligence-based equipment state evaluation method of the present invention based on the acquisition of the artificial intelligence-based equipment state evaluation system, thereby realizing artificial intelligence-based equipment state evaluation.

[0196] In some further embodiments of the present invention, in combination with the above-mentioned artificial intelligence-based device status assessment method, the embodiments of the present invention provide the following technical solutions: a storage medium having a computer program stored thereon, and the computer program implements the above-mentioned artificial intelligence-based device status assessment method when executed by a processor.

[0197] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0198] More specific examples of readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0199] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0200] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0201] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. An artificial intelligence-based equipment status assessment method, characterized in that: include: Acquire a historical operation data set of the device before the current moment, and determine a first weight and a second weight based on the historical operation data set of the device; Adjusting the first weight and the second weight to obtain a first adjusted weight and a second adjusted weight, and determining a comprehensive weight based on the first adjusted weight and the second adjusted weight; Establishing a device evaluation interval and state mapping table, determining a feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table, judging whether a feature mapping in the feature mapping set meets a dishonesty condition, and if a feature mapping in the feature mapping set meets the dishonesty condition, updating the corresponding feature mapping in the feature mapping set to obtain an updated feature mapping set; The updated feature mapping set is subjected to fusion processing to obtain a state evaluation value, and an interval mapping relationship between the state evaluation value in the device evaluation interval and the state mapping table is determined to output a corresponding device evaluation state; The step of determining the first weight and the second weight based on the equipment historical operation data set comprises: A data evaluation matrix is ​​constructed based on the historical operation data set of the equipment, and a first weight is determined based on the data evaluation matrix : ； In the formula, Represents the first Line The elements of the column, represents the number of row vectors in the data evaluation matrix, represents the gap regulation factor; Determine the first The data value of the data : ； Based on The data value of the data Calculate the second weight : ； The step of adjusting the first weight and the second weight to obtain a first adjusted weight and a second adjusted weight, and determining a comprehensive weight based on the first adjusted weight and the second adjusted weight comprises: Determine a safe operating range for each data in the device historical operating data set, and adjust the first weight based on the safe operating range to obtain a first adjusted weight : ； In the formula, is the first weight, is the first adjustment factor, Indicates the number of the historical operation data set of the device data, , Respectively represent the upper and lower limits of the safe operating range; The second weight is adjusted to obtain a second adjusted weight : ； In the formula, Represents the first Line The elements of the column, is the second weight, represents the second adjustment factor; Determine a comprehensive weight based on the first adjustment weight and the second adjustment weight : 。 2. The device status assessment method based on artificial intelligence according to claim 1 is characterized in that: The step of determining a feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table comprises: Identify the device evaluation interval and the number of state intervals in the state mapping table , and according to the number of state intervals Determine the feature map matrix : ； ； In the formula, Represents the feature map matrix Middle Line The elements of the column, Indicates The upper and lower bounds of the state interval, Indicates the number of the historical operation data set of the device The data is mapped to the equipment evaluation interval and the state in the table. The membership degree of each state interval; Based on the feature mapping matrix With the comprehensive weight Determine the feature map set : ； In the formula, Represents the feature map matrix Middle row vectors, Represents a feature map set Middle feature maps.

3. The device status assessment method based on artificial intelligence according to claim 2 is characterized in that: The step of determining whether a feature map in the feature map set meets the untrustworthy condition, and if the feature map in the feature map set meets the untrustworthy condition, updating the corresponding feature map in the feature map set to obtain an updated feature map set includes: Building a mapping function , and determine the feature map set Each feature map in the feature value after being mapped by the mapping function ,like , then the corresponding feature map is retained. If , then the corresponding feature map is removed from the feature map set Eliminate from the , to obtain the eliminated feature map set ,in, To remove feature map set The number of feature maps in ; The feature map set is removed The feature maps in the total extraction are processed according to different quantities times, to get Value Mapping Set ,in, Indicates The first feature maps, For the The number of feature maps in the sampled map, ; Calculate each of the extracted value mapping sets The default value , and calculate all the eliminated feature map sets The default value The average : ； ； Based on the untrustworthy value of the extracted mapping set, it is judged whether the extracted mapping set meets the untrustworthy condition. If the extracted mapping set meets the untrustworthy condition, the mapping set to be updated is determined and updated to obtain an updated feature mapping set.

4. The device status assessment method based on artificial intelligence according to claim 3 is characterized in that: The step of judging whether the extracted value mapping set meets the untrustworthy condition based on the untrustworthy value of the extracted value mapping set, and if the extracted value mapping set meets the untrustworthy condition, determining the mapping set to be updated and updating the mapping set to be updated to obtain an updated feature mapping set comprises: Determine whether the default value of the sampling mapping set is greater than the average value , if the default value of the sampling mapping set is less than the average value , then the corresponding extracted value mapping set does not meet the default condition and the corresponding extracted value mapping set is used as the mapping set to be updated ; Calculate the trust factor of the mapping set to be updated : ； In the formula, Indicates of the mappings to be updated feature maps, Indicates The trust factor of the mapping set to be updated; Based on the trust factor Performing an update process on the mapping set to be updated to obtain an updated mapping set : ； Update the mapping set by Replace the mapping set to be updated in the sampling mapping set to obtain an updated feature mapping set.

5. The device status assessment method based on artificial intelligence according to claim 1 is characterized in that: The step of fusing the updated feature mapping set to obtain a state evaluation value, determining an interval mapping relationship between the state evaluation value in the device evaluation interval and the state mapping table to output a corresponding device evaluation state comprises: The updated feature map set is fused to obtain a state evaluation value : ； In the formula, Indicates Update feature map sets; Determine the upper and lower bounds of each state interval in the device evaluation interval and state mapping table, and based on the state evaluation value The specific value of determines the state evaluation value The corresponding state interval; The state evaluation value The corresponding state interval is used as the final state interval, and the device evaluation state corresponding to the final state interval is output.

6. An artificial intelligence-based equipment status assessment system, the system adopts the artificial intelligence-based equipment status assessment method according to claim 1, characterized in that: The system comprises: A weight determination module, used to obtain a data set of historical operation of the device before the current moment, and determine a first weight and a second weight based on the data set of historical operation of the device; a weight adjustment module, configured to adjust the first weight and the second weight to obtain a first adjusted weight and a second adjusted weight, and determine a comprehensive weight based on the first adjusted weight and the second adjusted weight; An updating module, used to establish a device evaluation interval and state mapping table, determine a feature mapping set based on the comprehensive weight and the device evaluation interval and state mapping table, judge whether a feature mapping in the feature mapping set meets the untrustworthy condition, and if the feature mapping in the feature mapping set meets the untrustworthy condition, update the corresponding feature mapping in the feature mapping set to obtain an updated feature mapping set; The output module is used to fuse the updated feature mapping set to obtain a state evaluation value, determine the interval mapping relationship between the state evaluation value in the device evaluation interval and the state mapping table, and output the corresponding device evaluation state.

7. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the artificial intelligence-based device status assessment method as described in any one of claims 1 to 5 is implemented.

8. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for evaluating the device status based on artificial intelligence as described in any one of claims 1 to 5 is implemented.

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