CN110851292B - Method, apparatus and computer readable storage medium for intelligent prevention of operation errors - Google Patents
Method, apparatus and computer readable storage medium for intelligent prevention of operation errors Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a method for intelligently preventing operation errors, which comprises the following steps: the method comprises the steps of obtaining an original operation error data set and a solution data set, preprocessing the original operation error data set to obtain an operation error data set, carrying out word vector conversion on the operation error data set and the solution data set to obtain a word vector operation error data set and a word vector solution data set, inputting the word vector operation error data set into a feature extraction model to obtain an operation feature data set, carrying out clustering operation on the operation feature data set and the word vector solution data set to obtain an error-prone data set, receiving operation of a user, and intelligently reminding the user according to the error-prone data set. The invention also provides a device for intelligently preventing operation errors and a computer-readable storage medium. The invention can realize the intelligent prevention function of the high-efficiency operation errors.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method, an apparatus, and a computer readable storage medium for performing intelligent prevention based on user operation.
Background
At present, a large amount of information needs to be recorded in various web pages or APP operation processes, if the user does not pay attention to the information, errors can be caused when the user records the information, for example, the user can apply failure if the displacement is not modified in the new energy vehicle application process, and the user can apply unsuccessful if the business insurance is inconsistent with the start and stop dates of the traffic insurance. At present, based on the problems, whether the whole operation input information is coincident with a preset standard or not is adopted mostly, if the operation input information is not coincident with the preset standard, text reminding is carried out on the related non-coincident input information, and a user backtracks and revises the information according to the text reminding, so that the method needs the user to repeatedly revise the input information in most cases, and is tedious and delays time.
Disclosure of Invention
The invention provides a method and a device for intelligently preventing operation errors and a computer readable storage medium, and mainly aims to intelligently prevent links which are easy to cause errors in user operation.
In order to achieve the above object, the present invention provides a method for intelligently preventing operation errors, including:
acquiring an original operation error data set and a solution data set;
filling missing data and re-verifying the original operation error data set to obtain an operation error data set;
performing word vector conversion on the operation error data set and the solution data set to respectively obtain a word vector operation error data set and a word vector solution data set;
inputting the word vectorization operation error data set into a feature extraction model to obtain an operation feature data set;
clustering the operation characteristic data set and the word vectorization solution data set to obtain an error-prone data set;
and receiving the operation of the user, and carrying out intelligent preventive reminding on the operation of the user according to the error-prone data set.
Optionally, the word vector conversion includes:
presetting a weight relation among the operation error data set, the vectorization operation error data set, the solution data set and the word vectorization solution data set;
and calculating weights between the operation error data set and the solution data set based on the weight relation, and completing the word vector conversion process according to the weights.
Alternatively, the process may be carried out in a single-stage,
d＝{(t 1 ,w 1 ),(t 2 ,w 2 ),……,(t i ,w i ),……，(t n ,w n )}
wherein d is the operational error data set or the solution data set, t 1 、t 2 、……、t n For each piece of text information of the operational error data set or the solution data set, i.e. each piece of data of the operational error data set or the solution data set, w 1 、w 2 、……、w n The weight corresponding to each piece of text information is given; and
The calculating weights between the operational error dataset and the solution dataset comprises:
wherein f 1 Representing the number of occurrences of the text information in the operational error data set or the solution data set, N being the total number of data of the operational error data set or the solution data set, N j Representing the total number of text messages in the operational error data set or the solution data set, N i Representing the number of occurrences of text information i in said operational error data set or in said solution data set, F m Is a weighting factor.
Optionally, the clustering operation includes:
calculating a distance between the operational feature dataset and the word vectorization solution dataset;
optimizing the function of the cluster center and the distance to obtain an optimal value set;
and obtaining the data set which is most prone to error in the operation process according to the optimal value set.
Alternatively, the process may be carried out in a single-stage,
the distance is as follows:
wherein D is the word vectorization solution data set, D is the total data amount of the word vectorization solution data set, C is the operation feature data set, C is the total data amount of the operation feature data set, and x i,d Word vector, x representing data i of said word vectorization solution dataset j,c Characteristic data representing data j of the operational characteristic data set.
In addition, to achieve the above object, the present invention provides an apparatus for intelligent prevention of operation errors, the apparatus comprising a memory and a processor, wherein the memory stores an intelligent prevention program for operation errors that can be executed on the processor, and the intelligent prevention program for operation errors, when executed by the processor, performs the steps of:
acquiring an original operation error data set and a solution data set;
filling missing data and re-verifying the original operation error data set to obtain an operation error data set;
performing word vector conversion on the operation error data set and the solution data set to respectively obtain a word vector operation error data set and a word vector solution data set;
inputting the word vectorization operation error data set into a feature extraction model to obtain an operation feature data set;
clustering the operation characteristic data set and the word vectorization solution data set to obtain an error-prone data set;
and receiving the operation of the user, and carrying out intelligent preventive reminding on the operation of the user according to the error-prone data set.
Optionally, the word vector conversion includes:
presetting a weight relation among the operation error data set, the vectorization operation error data set, the solution data set and the word vectorization solution data set;
and calculating weights between the operation error data set and the solution data set based on the weight relation, and completing the word vector conversion process according to the weights.
Alternatively, the process may be carried out in a single-stage,
d＝{(t 1 ,w 1 ),(t 2 ,w 2 ),……,(t i ,w i ),……，(t n ,w n )}
wherein d is the operational error data set or the solution data set, t 1 、t 2 、……、t n For each piece of text information of the operational error data set or the solution data set, the text information is theOperating on each piece of data of the error dataset or the solution dataset, w 1 、w 2 、……、w n The weight corresponding to each piece of text information is given; and
The calculating weights between the operational error dataset and the solution dataset comprises:
wherein f i Representing the number of occurrences of the text information in the operational error data set or the solution data set, N being the total number of data of the operational error data set or the solution data set, N j Representing the total number of text messages in the operational error data set or the solution data set, N i Representing the number of occurrences of text information i in said operational error data set or in said solution data set, F m Is a weighting factor.
Optionally, the clustering operation includes:
calculating a distance between the operational feature dataset and the word vectorization solution dataset;
optimizing the function of the cluster center and the distance to obtain an optimal value set;
and obtaining the data set which is most prone to error in the operation process according to the optimal value set.
In addition, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon an operation error intelligent prevention program executable by one or more processors to implement the steps of the operation error intelligent prevention method as described above.
According to the invention, the original operation error data set and the solution data set are obtained, the missing data and the re-verification operation are carried out on the original operation error data set, so that the data purity is ensured, and furthermore, in order to better utilize the data, the original operation error data set and the solution data set are subjected to word vector conversion and feature extraction, so that better utilization of the data is facilitated, meanwhile, the operation data which is most prone to error is found out based on clustering operation, and intelligent reminding is given to a user. Therefore, the intelligent prevention method and device for the operation errors and the computer readable storage medium can realize efficient operation error prevention.
Drawings
FIG. 1 is a flow chart of a method for intelligent prevention of operation errors according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating an internal structure of an apparatus for intelligent prevention of operation errors according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a process for intelligent operation error prevention in an apparatus for intelligent operation error prevention according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides a method for intelligently preventing operation errors. Referring to fig. 1, a flow chart of a method for intelligent prevention of operation errors according to an embodiment of the invention is shown. The method may be performed by an apparatus, which may be implemented in software and/or hardware.
In this embodiment, the method for intelligently preventing operation errors includes:
s1, acquiring an original error data set and a solution data set which comprise error operation and error reasons.
Preferably, the original error data set refers to a data set which is operated by a user in error and expressed in text form when the user operates on a web page, an app or the like. If the license plate number of the new car is provided with a city prefix, such as Guangdong B, in the process of the insurance operation of the car, if the prefix of the local city needs to be removed when the license plate number of the local city is located at the outer place, the insurance error operation can be generated, such as the license plate number of the lake south area in the Shenzhen area, and the Guangdong B needs to be removed; if the new energy vehicle is not modified, tax payment in the process of insurance application cannot be carried out; if the date of the business insurance is inconsistent with the date of the traffic insurance, the bill can be made to fail when the insurance application is finished; some types of insurance require that the applicant and the insured person are the same person, and if not the same person, the applicant fails to report the policy after all the information is recorded, so that the page or app needs to be returned for re-modification.
Further, each data of the original error data set includes an error operation and an error cause. Such as when performing an application operation on a web page, app, or the like, the erroneous operation includes performing an erroneous operation resulting from filling in a insured person, filling in a license plate number, filling in a displacement, or the like. The error causes are causes of the error operations, such as that the filling insured person is not the same person as the insured person, the filling displacement is not satisfied with the specified displacement interval requirement, the filling license plate number is a foreign license plate, and the local city prefix is not removed. It is therefore preferable that the erroneous operation and the cause of the error are in one-to-one correspondence.
The solution data set is a data set giving a relevant solution to the error operation generated in the original operation error data set, typically expressed in text data information. The solution data sets are recorded as the modification applicant and the insured person are the same person in sequence, and because the insured person is a foreign license plate, the prefix of the local city needs to be removed from the license plate, and the displacement is modified again, so that the displacement is within a specified displacement interval [ a, b ], and the like.
S2, filling missing data and re-verifying the original error data set to obtain an error data set.
In the preferred embodiment of the present invention, since the original error data has a small missing amount of the error operation and the error cause, i.e. when one or both of the error operation and the error cause are empty, there is a missing value. Preferably, for example, when the error operation A is missing, traversing the error cause A corresponding to the error operation A 1 According to the error cause A 1 The error operation A can be obtained, and the same is obtained when the error causes B 1 Traversing and the error cause B when missing 1 A corresponding error operation B, according to which the error cause B can be obtained 1 And when the error operation and the error cause are both absent, directly eliminating.
The re-verification operation is to traverse each error operation and error reason in the original operation error data in sequence, open a corresponding application webpage or app to perform verification operation, and if the verified error operation or error reason does not correspond to the error operation and error reason in the original operation error data, reject the original error operation or error reason of the original operation error data, and replace the original error operation or error reason after the re-verification operation. If the error operation is filling displacement, the corresponding error is that the displacement does not meet the specified displacement interval [0.8L,4L ] requirement, but the specified displacement interval is found to be [0.9L,4.5L ] when the re-verification operation is performed, so that corresponding modification is performed.
Preferably, the re-verification operation can use pre-written scripts based on programming languages such as JAVA, C++, and the like to automatically traverse the error operation and the error cause for verification operation.
And S3, carrying out word vector conversion on the error data set and the solution data set to respectively obtain a word vector error data set and a word vector solution data set.
Because the error data set and the solution data set are both data sets in a text form, the text form data set is unfavorable for subsequent feature extraction, so that the error data set and the solution data set need to be converted into a vector form, the conversion process is word vector conversion, and the conversion is completed as a word vector conversion model.
Further, the operation error data set, the solution data set are respectively converted into a word vectorization operation error data set and a word vectorization solution data set, comprising: and respectively supposing a weight relation between the operation error data set and the vectorization operation error data set, the solution data set and the word vectorization solution data set, calculating weights among the data sets based on the weight relation, and completing the word vector conversion process according to the weights.
Specifically, the weight relationship is:
d＝{(t 1 ,w 1 ),(t 2 ,w 2 ),……,(t i ,w i ),……，(t n ,w n )}
wherein d is the operational error data set or the solution data set, t 1 、t 2 、……、t n For each piece of text information of the operation error data set or the solution data set (the text information is each piece of data of the operation error data set or the solution data set), as described above [ insured person ]][ number of filled license plate ]][ filling displacement ]]Etc., w 1 、w 2 、……、w n And the weight corresponding to each piece of text information is given.
Further, the weight calculating method comprises the following steps:
wherein f i Representing the number of occurrences of the text information in the operational error data set or the solution data set, N being the total number of data of the operational error data set or the solution data set, N j Representing the total number of text messages in the operational error data set or the solution data set, N i Representing the number of occurrences of text information i in said operational error data set or in said solution data set, F m The weighting factor is typically less than 1.
Preferably, the weight corresponding to each text information of the operation error data set or the solution data set is calculated, the weight corresponding to each text information is used for replacing each text information, and the word vector conversion is completed to obtain a word vector operation error data set and a word vector solution data set.
S4, inputting the word vectorization operation error data set into a feature extraction model to obtain an operation feature data set.
Preferably, the feature extraction model includes an encoding process, and an operating feature output process.
Preferably, the encoding process is based on a Bi-LSTM model, and the Bi-LSTM model includes an input gate, an output gate, and a forget gate, and the specific decoding process is as follows:
h t ＝o t ⊙tanh(c t )
wherein t is the data of the word vectorization operation error data set,a unit for representing data t before memorization of the Bi-LSTM model, c t A unit for representing the data t after the memorization of the Bi-LSTM model, c t-1 Representing the unit of data t-1 after the memorization of the Bi-LSTM model, wherein sigma is a sigmoid function, o t ,i t ,f t Respectively representing the updated values of the input gate, the forget gate and the output gate, wherein, the value of the element product is represented by the formula (I), the tangent is an arctangent function, the weight of the Bi-LSTM model is represented by the formula (W), the bias of the Bi-LSTM model is represented by the formula (b), and the value of the element product is represented by the formula (h) t Representing the encoded value, h, representing the data t t-1 Representing the encoded value representing data t-1.
Further, the operation feature output process includes: and solving the coding value based on the sigma (sigmoid function) to obtain an activation value, judging the sizes of the activation value and a preset threshold, discarding the activation value when the activation value is smaller than the preset threshold, and outputting the activation value as an operation characteristic when the activation value is larger than the preset threshold.
And S5, clustering the operation characteristic data set and the word vectorization solution data set to obtain the most error-prone data set.
Preferably, the clustering operation includes: and calculating the distance between the operation characteristic data set and the word vectorization solution data set, optimizing a function of a clustering center and the distance to obtain an optimal value set, and obtaining the data set most prone to error in the operation process according to the optimal value set.
The distance is
Wherein D is the word vectorization solution data set, D is the total data amount of the word vectorization solution data set, C is the operation feature data set, C is the total data amount of the operation feature data set, and x i,d Word vector, x representing data i of said word vectorization solution dataset j,c Characteristic data representing data j of the operational characteristic data set.
The function of the cluster center and the distance is:
wherein J is a function of the cluster Center and the distance, K is the cluster-like number of the clustering operation, center k X is the center of the class cluster corresponding to the class cluster K i For said distance, i.e. x i ＝dist(x i,d ,x j,c )。
Further, the function J of the cluster center and the distance is optimized to obtain an optimal value, and the optimal value set includes various operation characteristic data which is most prone to generating operation errors and corresponding word vectorization solution data.
Preferably, the most error-prone data set is obtained by means of inverse solution according to the optimal value set, wherein the optimal value set comprises operation characteristic data which is most prone to generating operation errors and corresponding word vectorization solution data, and the inverse solution comprises operations of word vectorization, coding and the like corresponding to S3-S4. The most error prone data set includes three parts, error handling, error cause and solution.
S6, receiving the operation of the user, and carrying out intelligent preventive reminding on the operation of the user according to the most error-prone data set.
If the user fills in the automobile displacement, the user can traverse whether the automobile displacement exists in the error operation in the most error-prone data set, if so, the automobile displacement (error operation) is not satisfied with the specified displacement interval requirement (error reason), and the automobile displacement is required to be fed back to the user within the displacement interval [ a, b ] (solution), so that the purpose of intelligent prevention reminding is achieved.
The invention also provides a device for intelligently preventing the operation errors. Referring to fig. 2, an internal structure diagram of an apparatus for intelligent prevention of operation errors according to an embodiment of the invention is shown.
In this embodiment, the device 1 for intelligently preventing operation errors may be a PC (personal computer), or a terminal device such as a smart phone, a tablet computer, a portable computer, or a server. The apparatus 1 for intelligent prevention of operational errors comprises at least a memory 11, a processor 12, a communication bus 13, and a network interface 14.
The memory 11 includes at least one type of readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the operation error intelligent prevention device 1, for example a hard disk of the operation error intelligent prevention device 1. The memory 11 may in other embodiments also be an external storage device of the apparatus 1 for intelligent prevention of operation errors, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which is provided on the apparatus 1 for intelligent prevention of operation errors. Further, the memory 11 may also comprise both an internal memory unit and an external memory device of the apparatus 1 for intelligent prevention of operation errors. The memory 11 may be used not only for storing application software installed in the apparatus for intelligent prevention of operation errors 1 and various types of data, for example, codes of the program for intelligent prevention of operation errors 01 and the like, but also for temporarily storing data that has been output or is to be output.
The processor 12 may in some embodiments be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing data stored in the memory 11, e.g. executing the program 01 for intelligent operation error prevention, etc.
The communication bus 13 is used to enable connection communication between these components.
The network interface 14 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), typically used to establish a communication connection between the apparatus 1 and other electronic devices.
Optionally, the device 1 may further comprise a user interface, which may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-emitting diode) touch, or the like. The display may also be referred to as a display screen or a display unit, as appropriate, for displaying information processed in the device 1 for intelligent prevention of operating errors and for displaying a visual user interface.
Fig. 2 shows only an operation error intelligent prevention apparatus 1 having components 11-14 and an operation error intelligent prevention program 01, and it will be understood by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the operation error intelligent prevention apparatus 1, and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In the embodiment of the device 1 shown in fig. 2, a program 01 for intelligent prevention of operation errors is stored in the memory 11; the processor 12 executes the program 01 for intelligent prevention of operation errors stored in the memory 11, and performs the following steps:
step one, an original error data set and a solution data set comprising error operation and error reasons are obtained.
Preferably, the original error data set refers to a data set which is operated by a user in error and expressed in text form when the user operates on a web page, an app or the like. If the license plate number of the new car is provided with a city prefix, such as Guangdong B, in the process of the insurance operation of the car, if the prefix of the local city needs to be removed when the license plate number of the local city is located outside, the insurance error operation can be generated, such as the license plate number of the Hunan area in the Shenzhen area, and the Guangdong B needs to be removed; if the new energy vehicle is not modified, tax payment in the process of insurance application cannot be carried out; if the date of the business insurance is inconsistent with the date of the traffic insurance, the bill can be made to fail when the insurance application is finished; some types of insurance require that the applicant and the insured person are the same person, and if not the same person, the applicant fails to report the policy after all the information is recorded, so that the page or app needs to be returned for re-modification.
Further, each data of the original error data set includes an error operation and an error cause. Such as when performing an application operation on a web page, app, or the like, the erroneous operation includes performing an erroneous operation resulting from filling in a insured person, filling in a license plate number, filling in a displacement, or the like. The error causes are causes of the error operations, such as that the filling insured person is not the same person as the insured person, the filling displacement is not satisfied with the specified displacement interval requirement, the filling license plate number is a foreign license plate, and the local city prefix is not removed. It is therefore preferable that the erroneous operation and the cause of the error are in one-to-one correspondence.
The solution data set is a data set giving a relevant solution to the error operation generated in the original operation error data set, typically expressed in text data information. The solution data sets are recorded as the modification applicant and the insured person are the same person in sequence, and because the insured person is a foreign license plate, the prefix of the local city needs to be removed from the license plate, and the displacement is modified again, so that the displacement is within a specified displacement interval [ a, b ], and the like.
And step two, filling missing data and re-verifying the original operation error data set to obtain an operation error data set.
In the preferred embodiment of the present invention, since the original operation error data has a small missing amount of the error operation and the error cause, that is, when one or both of the error operation and the error cause are empty, there is a missing value. Preferably, for example, when the error operation A is missing, traversing the error cause A corresponding to the error operation A 1 According to the error cause A 1 The error operation A can be obtained, and the same is obtained when the error causes B 1 Traversing and the error cause B when missing 1 A corresponding error operation B, according to which the error cause B can be obtained 1 And when the error operation and the error cause are both absent, directly eliminating.
The re-verification operation is to traverse each error operation and error reason in the original operation error data in sequence, open a corresponding application webpage or app to perform verification operation, and if the verified error operation or error reason does not correspond to the error operation and error reason in the original operation error data, reject the original error operation or error reason of the original operation error data, and replace the original error operation or error reason after the re-verification operation. If the error operation is filling displacement, the corresponding error is that the displacement does not meet the specified displacement interval [0.8L,4L ] requirement, but the specified displacement interval is found to be [0.9L,4.5L ] when the re-verification operation is performed, so that corresponding modification is performed.
Preferably, the re-verification operation can use pre-written scripts based on programming languages such as JAVA, C++, and the like to automatically traverse the error operation and the error cause for verification operation.
And thirdly, performing word vector conversion on the operation error data set and the solution data set to respectively obtain a word vectorization operation error data set and a word vectorization solution data set.
Because the operation error data set and the solution data set are both data sets in a text form, the text form data set is unfavorable for subsequent feature extraction, so that the operation error data set and the solution data set need to be converted into a vector form, the conversion process is word vector conversion, and the conversion is completed as a word vector conversion model.
Further, the operation error data set, the solution data set are respectively converted into a word vectorization operation error data set and a word vectorization solution data set, comprising: and respectively supposing a weight relation between the operation error data set and the vectorization operation error data set, the solution data set and the word vectorization solution data set, calculating weights among the data sets based on the weight relation, and completing the word vector conversion process according to the weights.
Specifically, the weight relationship is:
d＝{(t 1 ,w 1 ),(t 2 ,w 2 ),……,(t i ,w i ),……，(t n ,w n )}
wherein d is the operational error data set or the solution data set, t 1 、t 2 、……、t n For each piece of text information of the operation error data set or the solution data set (the text information is each piece of data of the operation error data set or the solution data set), as described above [ insured person ]][ number of filled license plate ]][ filling displacement ]]Etc., w 1 、w 2 、……、w n And the weight corresponding to each piece of text information is given.
Further, the weight calculating method comprises the following steps:
wherein f i Representing the number of occurrences of the text information in the operational error data set or the solution data set, N being the total number of data of the operational error data set or the solution data set, N j Representing the total number of text messages in the operational error data set or the solution data set, N i Representing the number of occurrences of text information i in said operational error data set or in said solution data set, F m The weighting factor is typically less than 1.
Preferably, the weight corresponding to each text information of the operation error data set or the solution data set is calculated, the weight corresponding to each text information is used for replacing each text information, and the word vector conversion is completed to obtain a word vector operation error data set and a word vector solution data set.
And step four, inputting the word vectorization operation error data set into a feature extraction model to obtain an operation feature data set.
Preferably, the feature extraction model includes an encoding process, and an operating feature output process.
Preferably, the encoding process is based on a Bi-LSTM model, and the Bi-LSTM model includes an input gate, an output gate, and a forget gate, and the specific decoding process is as follows:
h t ＝o t ⊙tanh(c t )
wherein t isData of the erroneous data set is vectorized for the word,a unit for representing data t before memorization of the Bi-LSTM model, c t A unit for representing the data t after the memorization of the Bi-LSTM model, c t-1 Representing the unit of data t-1 after the memorization of the Bi-LSTM model, wherein sigma is a sigmoid function, o t ,i t ,f t Respectively representing the updated values of the input gate, the forget gate and the output gate, wherein, the value of the element product is represented by the formula (I), the tangent is an arctangent function, the weight of the Bi-LSTM model is represented by the formula (W), the bias of the Bi-LSTM model is represented by the formula (b), and the value of the element product is represented by the formula (h) t Representing the encoded value, h, representing the data t t-1 Representing the encoded value representing data t-1.
Further, the operation feature output process includes: and solving the coding value based on the sigma (sigmoid function) to obtain an activation value, judging the sizes of the activation value and a preset threshold, discarding the activation value when the activation value is smaller than the preset threshold, and outputting the activation value as an operation characteristic when the activation value is larger than the preset threshold.
And fifthly, clustering the operation characteristic data set and the word vectorization solution data set to obtain the most error-prone data set.
Preferably, the clustering operation includes: and calculating the distance between the operation characteristic data set and the word vectorization solution data set, optimizing a function of a clustering center and the distance to obtain an optimal value set, and obtaining the data set most prone to error in the operation process according to the optimal value set.
The distance is
Wherein D is the word vectorization solution data set, D is the total data amount of the word vectorization solution data set, C is the operation feature data set, and C is the operation feature numberTotal data of data set, x i,d Word vector, x representing data i of said word vectorization solution dataset j,c Characteristic data representing data j of the operational characteristic data set.
The function of the cluster center and the distance is:
wherein J is a function of the cluster Center and the distance, K is the cluster-like number of the clustering operation, center k X is the center of the class cluster corresponding to the class cluster K i For said distance, i.e. x i ＝dist(x i,d ,x j,c )。
Further, the function J of the cluster center and the distance is optimized to obtain an optimal value, and the optimal value set includes various operation characteristic data which is most prone to generating operation errors and corresponding word vectorization solution data.
Preferably, the most error-prone data set is obtained by means of inverse solution according to the optimal value set, wherein the optimal value set comprises operation characteristic data which is most prone to generating operation errors and corresponding word vectorization solution data, and the inverse solution comprises operations of word vectorization, coding and the like corresponding to S3-S4. The most error prone data set includes three parts, error handling, error cause and solution.
And step six, receiving the operation of the user, and carrying out intelligent preventive reminding on the operation of the user according to the most error-prone data set.
If the user fills in the automobile displacement, the user can traverse whether the automobile displacement exists in the error operation in the most error-prone data set, if so, the automobile displacement (error operation) is not satisfied with the specified displacement interval requirement (error reason), and the automobile displacement is required to be fed back to the user within the displacement interval [ a, b ] (solution), so that the purpose of intelligent prevention reminding is achieved.
Alternatively, in other embodiments, the program for intelligent prevention of operation errors may be further divided into one or more modules, where one or more modules are stored in the memory 11 and executed by one or more processors (the processor 12 in this embodiment) to perform the present invention, and the modules referred to herein refer to a series of instruction segments of a computer program capable of performing specific functions for describing the execution of the program for intelligent prevention of operation errors in the apparatus for intelligent prevention of operation errors.
For example, referring to fig. 3, a program module schematic diagram of a program for intelligent operation error prevention in an embodiment of an apparatus for intelligent operation error prevention according to the present invention is shown, where the program for intelligent operation error prevention may be divided into a data receiving and processing module 10, a word vector conversion module 20, a feature extraction and clustering module 30, and an intelligent operation reminder module 40, which are exemplary:
the data receiving and processing module 10 is configured to: and acquiring an original operation error data set and a solution data set, and performing filling missing data and re-verification operation on the original operation error data set to obtain an operation error data set.
The word vector conversion module 20 is configured to: and carrying out word vector conversion on the operation error data set and the solution data set to respectively obtain a word vectorization operation error data set and a word vectorization solution data set.
The feature extraction and clustering module 30 is configured to: and inputting the word vectorization operation error data set into a feature extraction model to obtain an operation feature data set, and carrying out clustering operation on the operation feature data set and the word vectorization solution data set to obtain an error-prone data set.
The operation intelligent reminding module 40 is used for: and receiving the operation of the user, and carrying out intelligent preventive reminding on the operation of the user according to the error-prone data set.
The functions or operation steps implemented when the program modules such as the data receiving and processing module 10, the word vector conversion module 20, the feature extraction and clustering module 30, the operation intelligent reminding module 40 and the like are executed are substantially the same as those of the foregoing embodiments, and are not repeated herein.
In addition, an embodiment of the present invention further proposes a computer-readable storage medium having stored thereon a program for intelligent prevention of an operation error, the program for intelligent prevention of an operation error being executable by one or more processors to implement the following operations:
and acquiring an original operation error data set and a solution data set, and performing filling missing data and re-verification operation on the original operation error data set to obtain an operation error data set.
And carrying out word vector conversion on the operation error data set and the solution data set to respectively obtain a word vectorization operation error data set and a word vectorization solution data set.
And inputting the word vectorization operation error data set into a feature extraction model to obtain an operation feature data set, and carrying out clustering operation on the operation feature data set and the word vectorization solution data set to obtain an error-prone data set.
And receiving the operation of the user, and carrying out intelligent preventive reminding on the operation of the user according to the error-prone data set.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. A method of intelligent prevention of operational errors, the method comprising:
acquiring an original operation error data set and a solution data set;
filling missing data and re-verifying the original operation error data set to obtain an operation error data set;
performing word vector conversion on the operation error data set and the solution data set to respectively obtain a word vector operation error data set and a word vector solution data set;
inputting the word vectorization operation error data set into a feature extraction model to obtain an operation feature data set;
clustering the operation characteristic data set and the word vectorization solution data set to obtain an error-prone data set;
receiving operation of a user, and carrying out intelligent preventive reminding on the operation of the user according to the error-prone data set;
wherein filling missing data into the original operational error data set comprises: determining whether the original error data in the original operation error data set lacks error operation or error reasons; if the original error data lacks error operation, traversing error reasons corresponding to the missing error operation, and obtaining corresponding error operation according to the traversed error reasons; if the original error data has the missing error reason, traversing the error operation corresponding to the missing error reason, and obtaining the corresponding error reason according to the traversed error operation;
the re-verification operation includes: traversing each error operation and error reason in the original operation error data in sequence, opening a corresponding application webpage or app to perform verification operation, and if the verified error operation or error reason does not correspond to the error operation and error reason in the original operation error data, eliminating the original error operation or error reason of the original operation error data, and replacing the original error operation or error reason with the error operation or error reason after the re-verification operation.
2. The method of intelligent prevention of operational errors of claim 1, wherein the word vector conversion comprises:
presetting a weight relation among the operation error data set, the word vectorization operation error data set, the solution data set and the word vectorization solution data set;
and calculating weights between the operation error data set and the solution data set based on the weight relation, and completing the word vector conversion process according to the weights.
3. The method of intelligent prevention of operational errors of claim 1, wherein the clustering operation comprises:
calculating a distance between the operational feature dataset and the word vectorization solution dataset;
optimizing the function of the cluster center and the distance to obtain an optimal value set;
and obtaining the data set which is most prone to error in the operation process according to the optimal value set.
4. A method of intelligent prevention of operational errors as set forth in claim 3, wherein said distance is:
wherein D is the word vectorization solution data set, D is the total data amount of the word vectorization solution data set, C is the operation feature data set, C is the total data amount of the operation feature data set, and x i,d Word vector, x representing data i of said word vectorization solution dataset j,c Characteristic data representing data j of the operational characteristic data set.
5. An apparatus for intelligent prevention of an operation error, the apparatus comprising a memory and a processor, the memory having stored thereon a program for intelligent prevention of an operation error operable on the processor, the program for intelligent prevention of an operation error, when executed by the processor, performing the steps of:
acquiring an original operation error data set and a solution data set;
filling missing data and re-verifying the original operation error data set to obtain an operation error data set;
performing word vector conversion on the operation error data set and the solution data set to respectively obtain a word vector operation error data set and a word vector solution data set;
inputting the word vectorization operation error data set into a feature extraction model to obtain an operation feature data set;
clustering the operation characteristic data set and the word vectorization solution data set to obtain an error-prone data set;
receiving operation of a user, and carrying out intelligent preventive reminding on the operation of the user according to the error-prone data set;
wherein filling missing data into the original operation error data set comprises: determining whether the original error data in the original operation error data set lacks error operation or error reasons; if the original error data lacks error operation, traversing error reasons corresponding to the missing error operation, and obtaining corresponding error operation according to the traversed error reasons; if the original error data has the missing error reason, traversing the error operation corresponding to the missing error reason, and obtaining the corresponding error reason according to the traversed error operation;
the re-verification operation includes: traversing each error operation and error reason in the original operation error data in sequence, opening a corresponding application webpage or app to perform verification operation, and if the verified error operation or error reason does not correspond to the error operation and error reason in the original operation error data, eliminating the original error operation or error reason of the original operation error data, and replacing the original error operation or error reason with the error operation or error reason after the re-verification operation.
6. The apparatus for intelligent prevention of an operation error of claim 5, wherein the word vector conversion comprises:
presetting a weight relation among the operation error data set, the word vectorization operation error data set, the solution data set and the word vectorization solution data set;
and calculating weights between the operation error data set and the solution data set based on the weight relation, and completing the word vector conversion process according to the weights.
7. The apparatus for intelligent prevention of operational errors of claim 5, wherein the clustering operation comprises:
calculating a distance between the operational feature dataset and the word vectorization solution dataset;
optimizing the function of the cluster center and the distance to obtain an optimal value set;
and obtaining the data set which is most prone to error in the operation process according to the optimal value set.
8. A computer-readable storage medium, wherein a program for intelligent prevention of an operation error is stored on the computer-readable storage medium, the program for intelligent prevention of an operation error being executable by one or more processors to implement the steps of the method for intelligent prevention of an operation error as claimed in any one of claims 1 to 4.
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| US10361802B1 (en) * | 1999-02-01 | 2019-07-23 | Blanding Hovenweep, Llc | Adaptive pattern recognition based control system and method |
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