CN116992291A - Workflow management method and device based on width learning, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a workflow management method and device based on width learning, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring flow data corresponding to the workflow and generating a test flow set and a training feature set according to the flow data; generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set; comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow; determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set; if not, training a width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set; if yes, generating a flow branch of the workflow according to the training flow set and the working system code. And automatically generating flow branches meeting the expectations of users through width learning.
Description
Technical Field
The present invention relates to the field of flow management technologies, and in particular, to a workflow management method and apparatus based on width learning, an electronic device, and a storage medium.
Background
The existing server configuration verification system is generally characterized in that workflow management is generally finished according to set established steps, other workflow management systems are generally used for carrying out workflow task priority sorting and scheduling in the same system, when a cross-system workflow is met, especially when the existing workflow does not meet requirements, and new flow branches are required to be established, original code logic of the system is required to be manually modified or the system is required to be redeveloped so as to meet the requirements, time is greatly wasted, and inter-system joint debugging is also required. The existing server configuration verification system, workflow management covers nodes such as pre-sale requirements, research and development, testing, production, after-sale and the like, and the workflow management basically performs flow torsion according to set personnel nodes whether the workflow management is interacted in a single system or in multiple systems.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a workflow management method, apparatus, electronic device, and storage medium based on width learning, which can automatically generate a flow branch that meets the expectations of users.
In a first aspect, a workflow management method based on width learning is provided, which is characterized in that the method includes:
acquiring flow data corresponding to a workflow and generating a test flow set and a training feature set according to the flow data;
generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow;
determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
if not, training the width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set;
if yes, generating flow branches of the workflow according to the training flow set and the working system codes.
In one embodiment, the obtaining the flow data corresponding to the workflow and generating the test flow set and the training feature set according to the flow data includes:
The flow data comprises flow node information and flow type information;
integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
In one embodiment, the generating a width learning model according to the training feature set and generating a training process set according to the width learning model and the training feature set includes:
generating a plurality of data sets according to the training feature set;
generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
In one embodiment, the comparing the test flows in the test flow set with the training flows in the training flow set and generating the bias rate set according to the test flows and the training flows includes:
determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
And calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
In one embodiment, the determining, according to the deviation rate set, whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user includes:
and weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
In one embodiment, the generating the flow branches of the workflow according to the training flow set and the working system code includes:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
generating the system back-end code and the table structure according to the training process and the working system code;
and generating the front-end page code according to the flow data type of the training flow.
In one embodiment, the generating the flow branches of the workflow according to the training flow set and the working system code includes:
Sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
and in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
In another aspect, a workflow management apparatus based on width learning is provided, which includes:
the first set generating module is used for acquiring flow data corresponding to the workflow and generating a test flow set and a training feature set according to the flow data;
the second set generation module is used for generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
the third set generating module is used for comparing the test flow in the test flow set with the training flow in the training flow set and generating a deviation rate set according to the test flow and the training flow;
the determining module is used for determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
The training module is used for training the width learning model according to the flow deviation rate and regenerating a training flow set according to the width learning model and the training feature set if not;
and the flow branch generating module is used for generating the flow branch of the workflow according to the training flow set and the working system code if the flow branch is generated.
In one embodiment, the first set generating module obtains flow data corresponding to a workflow and generates a test flow set and a training feature set according to the flow data, including:
the flow data comprises flow node information and flow type information;
integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
In one embodiment, the second set generating module generates a width learning model according to the training feature set and generates a training process set according to the width learning model and the training feature set, including:
generating a plurality of data sets according to the training feature set;
Generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
In one embodiment, the third set generating module compares a test flow in the test flow set and a training flow in the training flow set and generates the deviation rate set according to the test flow and the training flow includes:
determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
and calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
In one embodiment, the determining module determines, according to the deviation rate set, whether a flow deviation rate corresponding to the workflow is less than a deviation rate threshold set by a user includes:
And weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
In one embodiment, the process branch generating module generates the process branch of the workflow according to the training process set and the working system code includes:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
generating the system back-end code and the table structure according to the training process and the working system code;
and generating the front-end page code according to the flow data type of the training flow.
In one embodiment, the process branch generating module generates the process branch of the workflow according to the training process set and the working system code, and then includes:
sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
and in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
In yet another aspect, an electronic device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring flow data corresponding to a workflow and generating a test flow set and a training feature set according to the flow data;
generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow;
determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
if not, training the width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set;
if yes, generating flow branches of the workflow according to the training flow set and the working system codes.
In one embodiment, the processor, when executing the computer program, performs the steps of:
The step of obtaining the flow data corresponding to the workflow and generating the test flow set and the training feature set according to the flow data comprises the following steps:
the flow data comprises flow node information and flow type information;
integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
In one embodiment, the processor, when executing the computer program, performs the steps of:
the generating a width learning model according to the training feature set and generating a training process set according to the width learning model and the training feature set includes:
generating a plurality of data sets according to the training feature set;
generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
In one embodiment, the processor, when executing the computer program, performs the steps of:
The comparing the test flow in the test flow set with the training flow in the training flow set and generating the deviation rate set according to the test flow and the training flow comprises:
determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
and calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
In one embodiment, the processor, when executing the computer program, performs the steps of:
the step of determining whether the flow deviation rate corresponding to the workflow is smaller than the deviation rate threshold set by the user according to the deviation rate set comprises the following steps:
and weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
In one embodiment, the processor, when executing the computer program, performs the steps of:
The generating the flow branch of the workflow according to the training flow set and the working system code comprises the following steps:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
generating the system back-end code and the table structure according to the training process and the working system code;
and generating the front-end page code according to the flow data type of the training flow.
In one embodiment, the processor, when executing the computer program, performs the steps of:
the step of generating the flow branches of the workflow according to the training flow set and the working system code comprises the following steps:
sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
and in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
In yet another aspect, a computer readable storage medium is provided, having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring flow data corresponding to a workflow and generating a test flow set and a training feature set according to the flow data;
generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow;
determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
if not, training the width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set;
if yes, generating flow branches of the workflow according to the training flow set and the working system codes.
In one embodiment, the computer program when executed by a processor performs the steps of:
the step of obtaining the flow data corresponding to the workflow and generating the test flow set and the training feature set according to the flow data comprises the following steps:
the flow data comprises flow node information and flow type information;
Integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
In one embodiment, the computer program when executed by a processor performs the steps of:
the generating a width learning model according to the training feature set and generating a training process set according to the width learning model and the training feature set includes:
generating a plurality of data sets according to the training feature set;
generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
In one embodiment, the computer program when executed by a processor performs the steps of:
the comparing the test flow in the test flow set with the training flow in the training flow set and generating the deviation rate set according to the test flow and the training flow comprises:
Determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
and calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
In one embodiment, the computer program when executed by a processor performs the steps of:
the step of determining whether the flow deviation rate corresponding to the workflow is smaller than the deviation rate threshold set by the user according to the deviation rate set comprises the following steps:
and weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
In one embodiment, the computer program when executed by a processor performs the steps of:
the generating the flow branch of the workflow according to the training flow set and the working system code comprises the following steps:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
Generating the system back-end code and the table structure according to the training process and the working system code;
and generating the front-end page code according to the flow data type of the training flow.
In one embodiment, the computer program when executed by a processor performs the steps of:
the step of generating the flow branches of the workflow according to the training flow set and the working system code comprises the following steps:
sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
and in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
According to the workflow management method, the workflow management device, the electronic equipment and the storage medium based on the width learning, the flow data corresponding to the workflow are obtained, and the test flow set and the training feature set are generated according to the flow data; then generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set; comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow; then determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set; if not, training a width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set; if yes, generating a flow branch of the workflow according to the training flow set and the working system code. By integrating and planning the workflow data and automatically generating new flow branches, the working pressure of a user is reduced, and the working efficiency of a background system is improved; and introducing deviation parameters and finally generating flow branches of the workflow which meet the expectations of the user by using a width learning mode.
Drawings
FIG. 1 is a flow diagram of a workflow management method based on width learning;
FIG. 2 is a schematic diagram of steps of a workflow management method based on width learning;
FIG. 3 is a flow example diagram of a workflow management method based on width learning;
FIG. 4 is an example diagram of a set of test flows in a workflow management method based on width learning;
FIG. 5 is an example diagram of a training feature set in a workflow management method based on width learning;
FIG. 6 is an example diagram of a training flow set in a workflow management method based on width learning;
FIG. 7 is a schematic diagram of a workflow management system based on width learning;
fig. 8 is an internal structural diagram of a computer device in an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. 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 application.
The application provides a system topology diagram of a workflow management method based on width learning as shown in fig. 1, wherein the width learning is a high-efficiency incremental learning mode of transversely expanding a network by taking a random vector function linked neural network as a mapping characteristic and directly connecting the mapping characteristic and an enhancement node to an output end through a neural enhancement node based on a single hidden layer neural network, and the width learning is applicable to a system with few data characteristics and higher requirement on prediction instantaneity. To achieve automation, intelligence, and integration of work process management, a workflow may be viewed as a set of multiple basic tasks, with precedence and data transfer dependencies between certain task nodes. Generating a test flow set and a training feature set through the collected flow data; generating a plurality of data sets according to the training feature set, generating a weight matrix by taking deviation parameters input by a user as mapping features through a width learning model, generating initial flow node data according to the weight matrix and the plurality of data sets, and generating a training flow set by integrating the initial flow node data and the data sets; comparing the test flow and the training flow to determine the enhancement node of each flow type and generating a deviation rate set according to the enhancement node and the training flow set; calculating whether the deviation rate of each flow is smaller than a deviation rate threshold value set by a user according to the deviation rate set; if not, training a width learning model according to the node deviation rate corresponding to the flow deviation rate, and regenerating a training flow corresponding to the flow deviation rate through the width learning model; if yes, generating flow branches of the workflow according to the training flow and the working system code, wherein the flow deviation rate is smaller than a deviation rate threshold, and the workflow has a plurality of corresponding training flows and a plurality of corresponding flow branches.
In one embodiment, as shown in fig. 2, the present invention provides a workflow management method based on width learning, which is characterized in that the method includes:
s201, acquiring flow data corresponding to a workflow and generating a test flow set and a training feature set according to the flow data;
s202, generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
s203, comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow;
s204, determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
s205, if not, training the width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set;
s206, if yes, generating a flow branch of the workflow according to the training flow set and the working system code.
Specifically, a basic width learning model is generated according to a training feature set, an adjustable deviation parameter is set in the width learning model by introducing the concept of deviation parameter, training flows corresponding to each flow type are generated according to the width learning model and the training feature set, node deviation rates are obtained through comparison of the training flows and the testing flows, the flow deviation rate corresponding to each flow type is calculated after weighting treatment is carried out on the node deviation rates, whether the flow deviation rate is smaller than a deviation rate threshold value is compared, and if the flow deviation rate is smaller than the deviation rate threshold value, the width learning model is retrained according to the corresponding node deviation rate, and a new training flow is generated; if the flow is larger than the training flow, generating a corresponding flow branch according to the training flow. As shown in fig. 3, a certain amount of flow data is first collected, and the collected data is classified and integrated into a preset database. For example, existing flow data includes: node post personnel, flow type data (component FW verification flow, HDD verification flow, overall compatibility verification flow, etc.), the data types in the flow are as follows: text, string, bootean, etc.; and then carrying out static feature extraction on each flow data to construct a workflow feature library, wherein the workflow feature library is divided into a training set and a testing set. Constructing a width learning basic model according to the data of the training set, comparing the training set with the training set of the width learning network after the training is completed, obtaining a certain amount of difference data through comparison, and obtaining a deviation rate y (one record of each flow type); then collecting the deviation rate y as a new feature vector set, and continuously performing incremental learning on the width learning basic model to optimize the basic model to obtain a final width learning model until the deviation rate y is smaller than a certain threshold value such as 0.001%; and then generating a flow branch of the initial edition according to the width learning model generated by the data learning module: and automatically generating a system back-end code and a table structure according to the node post personnel, the flow type data and the data type in the flow by combining the existing system codes, and generating a front-end page code according to the data type in the flow. After the new flow branch is generated, the mail reminds a system administrator to check, if the new flow branch meets the requirement, the deviation rate of the flow type is corrected to be 0, if the new flow branch does not meet the requirement, the deviation rate is corrected to be 100%, a data learning instruction is initiated, and the learning is performed again.
In one embodiment, the obtaining the flow data corresponding to the workflow and generating the test flow set and the training feature set according to the flow data includes:
the flow data comprises flow node information and flow type information;
integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
Specifically, as shown in fig. 4, the present application may be used in a business process, which includes a component FW verification process, an HDD verification process, an overall compatibility verification process, and other process types; the method can also be used for business processes in the company, and the like, wherein the business processes comprise a leave-asking process, an office supplies application process, a vehicle pass application process and the like. The flow node information corresponding to the component FW verification flow comprises an SE node, a TE node, a TL node and a VM node, and the data types of the node data in the nodes comprise text, string, bootean and the like. And integrating the node data and the flow types through the relation among all the nodes in the acquired flow data to generate a test flow, and finally generating a test flow set. As shown in fig. 5, the process data is subjected to static feature extraction to generate a training feature set, for example, SIV/HWE/PIV nodes are subjected to feature extraction to generate SIV nodes, HWE nodes and PIV nodes, and each process node information and each process type information are contained in the training feature set.
In one embodiment, the generating a width learning model according to the training feature set and generating a training process set according to the width learning model and the training feature set includes:
generating a plurality of data sets according to the training feature set;
generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
Specifically, a data set [ X, Y ] is generated from a training feature set]E.g. component FW verification procedure, SE node][ HDD authentication procedure, TE node]Etc., then introducing the concept of deviation rate into the existing width learning model, i.e. the user sets an adjustable deviation parameter d in the existing width learning model j Different flow types are allocated with different deviation parameters, the deviation parameters of each data set are determined by the flow types in the data set, then a weight matrix is generated through the deviation parameters of all the data sets, but because the number of the data sets generated each time is uncertain, the number of elements in the weight matrix generated each time is also different, and then initial flow node data FF= { W is generated according to the data sets and the weight matrix mu 1 ,W 2 ……W N N is the dataset [ X, Y ]]As shown in FIG. 6, the initial flow node data and data sets [ X, Y ]]Integrating to obtain a training flow set H: h= [ H ] 1 ,H 2 ,H 3 ,H 4 ……H m ]。
In one embodiment, the comparing the test flows in the test flow set with the training flows in the training flow set and generating the bias rate set according to the test flows and the training flows includes:
determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
and calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
Specifically, comparing the training process set H with the test set, and generating an enhanced node z= [ Z ] corresponding to each process type for each process type 1 ,Z 2 ……Zn]The method comprises the steps of carrying out a first treatment on the surface of the Combining training process set with enhanced node group to obtainInactivation data S: s=
[Z n |H m ]Then according to the solving equation Y= [ H ] m ,Z n ]d j Obtaining a deviation rate set Y= [ Y ] 1 ,Y 2 ,Y 3 ,Y 4 ……]The four node deviation rates are included in each flow type, wherein the node deviation rates include node quantity deviation rate, node sequence deviation rate, node post personnel deviation rate and node personnel role deviation rate, and if three training flows are shared in the training flow set, 12 node deviation rates are needed in the finally generated deviation rate set.
In one embodiment, the determining, according to the deviation rate set, whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user includes:
and weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
Specifically, the deviation rate parameters corresponding to each flow type are weighted, the average value of the deviation rates of the four nodes is obtained after the weighting, namely the flow deviation rate corresponding to the flow type, if the flow deviation rate is smaller than the deviation rate threshold value set by a user, for example, 0.001%, the generated training flow is proved to meet the requirement, otherwise, the generated training flow does not meet the requirement, for example, the flow deviation rate of the component FW verification flow does not meet the requirement, a new component FW verification flow needs to be regenerated, the HDD verification flow and the overall compatibility verification flow meet the requirement, and a new flow branch is generated according to the training flow of the HDD verification flow and the training flow of the overall compatibility verification flow. The step of specifically generating a new training process set containing component FW verification processes includes: taking the node deviation rate y of the flow type as a new feature vector set, and performing incremental learning on the width learning model through the feature vector set to optimize the width learning model; and generating a new data set corresponding to the process type according to the training feature set, generating a new component FW verification process through the training of the completed width learning model and the new data set, and continuously determining whether the new process deviation rate is smaller than a deviation rate threshold value until the new process deviation rate is smaller than 0.001%.
In one embodiment, the generating the flow branches of the workflow according to the training flow set and the working system code includes:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
generating the system back-end code and the table structure according to the training process and the working system code;
and generating the front-end page code according to the flow data type of the training flow.
Specifically, a new training process is generated according to the width learning model: and automatically generating a system back-end code and a list structure according to node data and flow type data in the training flow and combining the existing system code, and generating a front-end page code according to the data type corresponding to the flow data, wherein the system back-end code, the list structure and the front-end page code are generated well to represent that the flow branch generation is completed.
In one embodiment, the generating the flow branches of the workflow according to the training flow set and the working system code includes:
sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
And in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
Specifically, after the new flow branch is generated, a system administrator can be reminded to check in a mail mode or the like, if the new flow branch meets the requirement, the node deviation rate of the flow type is modified to 0, the flow branch is issued on line, if the new flow branch does not meet the requirement, the node deviation rate is modified to 1 and returned to the width learning model, the width learning model is retrained through the node deviation rates, and then a new training flow is regenerated according to the width learning model and the training feature set.
The scheme of the application has the following beneficial effects:
1) By integrating and planning the workflow data and automatically generating new flow branches, the working pressure of a user is reduced, and the working efficiency of a background system is improved;
2) And introducing deviation parameters and finally generating flow branches of the workflow which meet the expectations of the user by using a width learning mode.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
In one embodiment, as shown in fig. 7, a workflow management apparatus based on width learning, wherein the apparatus includes:
a first set generating module 701, configured to obtain flow data corresponding to a workflow and generate a test flow set and a training feature set according to the flow data;
a second set generating module 702, configured to generate a width learning model according to the training feature set and generate a training process set according to the width learning model and the training feature set;
a third set generating module 703, configured to compare a test procedure in the test procedure set and a training procedure in the training procedure set, and generate a deviation rate set according to the test procedure and the training procedure;
a determining module 704, configured to determine, according to the deviation rate set, whether a flow deviation rate corresponding to the workflow is less than a deviation rate threshold set by a user;
the training module 705 is configured to train the width learning model according to the process deviation rate and regenerate a training process set according to the width learning model and the training feature set if not;
and the flow branch generating module 706 is configured to generate a flow branch of the workflow according to the training flow set and the working system code if the flow branch is yes.
In one embodiment, the first set generating module obtains flow data corresponding to a workflow and generates a test flow set and a training feature set according to the flow data, including:
the flow data comprises flow node information and flow type information;
integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
In one embodiment, the second set generating module generates a width learning model according to the training feature set and generates a training process set according to the width learning model and the training feature set, including:
generating a plurality of data sets according to the training feature set;
generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
In one embodiment, the third set generating module compares a test flow in the test flow set and a training flow in the training flow set and generates the deviation rate set according to the test flow and the training flow includes:
Determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
and calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
In one embodiment, the determining module determines, according to the deviation rate set, whether a flow deviation rate corresponding to the workflow is less than a deviation rate threshold set by a user includes:
and weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
In one embodiment, the process branch generating module generates the process branch of the workflow according to the training process set and the working system code includes:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
generating the system back-end code and the table structure according to the training process and the working system code;
And generating the front-end page code according to the flow data type of the training flow.
In one embodiment, the process branch generating module generates the process branch of the workflow according to the training process set and the working system code, and then includes:
sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
and in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
For specific limitations on the workflow management apparatus based on the width learning, reference may be made to the above limitation on the workflow management method based on the width learning, and no further description is given here. The various modules in the workflow management apparatus based on width learning described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements an alert information processing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 8 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, an electronic device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of:
acquiring flow data corresponding to a workflow and generating a test flow set and a training feature set according to the flow data;
generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow;
determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
If not, training the width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set;
if yes, generating flow branches of the workflow according to the training flow set and the working system codes.
In one embodiment, the processor, when executing the computer program, performs the steps of:
the step of obtaining the flow data corresponding to the workflow and generating the test flow set and the training feature set according to the flow data comprises the following steps:
the flow data comprises flow node information and flow type information;
integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
In one embodiment, the processor, when executing the computer program, performs the steps of:
the generating a width learning model according to the training feature set and generating a training process set according to the width learning model and the training feature set includes:
Generating a plurality of data sets according to the training feature set;
generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
In one embodiment, the processor, when executing the computer program, performs the steps of:
the comparing the test flow in the test flow set with the training flow in the training flow set and generating the deviation rate set according to the test flow and the training flow comprises:
determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
and calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
In one embodiment, the processor, when executing the computer program, performs the steps of:
The step of determining whether the flow deviation rate corresponding to the workflow is smaller than the deviation rate threshold set by the user according to the deviation rate set comprises the following steps:
and weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
In one embodiment, the processor, when executing the computer program, performs the steps of:
the generating the flow branch of the workflow according to the training flow set and the working system code comprises the following steps:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
generating the system back-end code and the table structure according to the training process and the working system code;
and generating the front-end page code according to the flow data type of the training flow.
In one embodiment, the processor, when executing the computer program, performs the steps of:
the step of generating the flow branches of the workflow according to the training flow set and the working system code comprises the following steps:
sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
And in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
In one embodiment, a computer readable storage medium is provided having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring flow data corresponding to a workflow and generating a test flow set and a training feature set according to the flow data;
generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow;
determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
if not, training the width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set;
If yes, generating flow branches of the workflow according to the training flow set and the working system codes.
In one embodiment, the computer program when executed by a processor performs the steps of:
the step of obtaining the flow data corresponding to the workflow and generating the test flow set and the training feature set according to the flow data comprises the following steps:
the flow data comprises flow node information and flow type information;
integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
In one embodiment, the computer program when executed by a processor performs the steps of:
the generating a width learning model according to the training feature set and generating a training process set according to the width learning model and the training feature set includes:
generating a plurality of data sets according to the training feature set;
generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
In one embodiment, the computer program when executed by a processor performs the steps of:
the comparing the test flow in the test flow set with the training flow in the training flow set and generating the deviation rate set according to the test flow and the training flow comprises:
determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
and calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
In one embodiment, the computer program when executed by a processor performs the steps of:
the step of determining whether the flow deviation rate corresponding to the workflow is smaller than the deviation rate threshold set by the user according to the deviation rate set comprises the following steps:
and weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
In one embodiment, the computer program when executed by a processor performs the steps of:
the generating the flow branch of the workflow according to the training flow set and the working system code comprises the following steps:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
generating the system back-end code and the table structure according to the training process and the working system code;
and generating the front-end page code according to the flow data type of the training flow.
In one embodiment, the computer program when executed by a processor performs the steps of:
the step of generating the flow branches of the workflow according to the training flow set and the working system code comprises the following steps:
sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
and in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A workflow management method based on width learning, the method comprising:
acquiring flow data corresponding to a workflow and generating a test flow set and a training feature set according to the flow data;
generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
Comparing the test flow in the test flow set with the training flow in the training flow set, and generating a deviation rate set according to the test flow and the training flow;
determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
if not, training the width learning model according to the deviation rate set and regenerating a training flow set according to the width learning model and the training feature set;
if yes, generating flow branches of the workflow according to the training flow set and the working system codes.
2. The method of claim 1, wherein the obtaining flow data corresponding to a workflow and generating a test flow set and a training feature set from the flow data comprises:
the flow data comprises flow node information and flow type information;
integrating and generating the test flow set according to the flow node information and the flow type;
and extracting static characteristic data from the flow node information and generating the training characteristic set according to the static characteristic data and the flow type.
3. The method of claim 2, wherein the generating a width learning model from the training feature set and generating a training flow set from the width learning model and the training feature set comprises:
generating a plurality of data sets according to the training feature set;
generating a weight matrix according to the width learning model and the deviation parameter input by the user;
generating initial flow node data according to the data sets and the weight matrix, and integrating the initial flow node data and the data sets to generate the training flow set.
4. The method of claim 1, wherein the comparing the test flows in the set of test flows to the training flows in the set of training flows and generating the set of bias rates from the test flows and the training flows comprises:
determining an enhancement node according to the training process and the testing process and generating inactivation data according to the enhancement node and the training process set;
and calculating node deviation rate according to the inactivated data and the deviation parameter, and generating the deviation rate set according to the node deviation rate, wherein the node deviation rate comprises node quantity deviation rate, node sequence deviation rate, node personnel position deviation rate and node personnel role deviation rate.
5. The method of claim 4, wherein determining from the set of bias rates whether a flow bias rate corresponding to the workflow is less than a user-set bias rate threshold comprises:
and weighting the node deviation rate according to the deviation parameter, and calculating the flow deviation rate corresponding to the flow type according to the weighted node deviation rate.
6. The method of claim 1, wherein generating the flow branches of the workflow from the training flow set and the working system code comprises:
the flow branch comprises a system back-end code, a table structure and a front-end page code;
generating the system back-end code and the table structure according to the training process and the working system code;
and generating the front-end page code according to the flow data type of the training flow.
7. The method of claim 1, wherein generating the flow branches of the workflow from the training flow set and the working system code comprises:
sending the flow branches to the user;
responding to the user to determine that the flow branches meet requirements, modifying node deviation rates corresponding to the flow branches to be 0 and issuing the flow branches;
And in response to the user determining that the flow branch does not meet the requirement, modifying node deviation rates corresponding to the flow branch to be 1, and returning the node deviation rates to the width learning model.
8. A workflow management apparatus based on width learning, the apparatus comprising:
the first set generating module is used for acquiring flow data corresponding to the workflow and generating a test flow set and a training feature set according to the flow data;
the second set generation module is used for generating a width learning model according to the training feature set and generating a training flow set according to the width learning model and the training feature set;
the third set generating module is used for comparing the test flow in the test flow set with the training flow in the training flow set and generating a deviation rate set according to the test flow and the training flow;
the determining module is used for determining whether the flow deviation rate corresponding to the workflow is smaller than a deviation rate threshold set by a user according to the deviation rate set;
the training module is used for training the width learning model according to the flow deviation rate and regenerating a training flow set according to the width learning model and the training feature set if not;
And the flow branch generating module is used for generating the flow branch of the workflow according to the training flow set and the working system code if the flow branch is generated.
9. An electronic device, comprising:
one or more processors; and a memory associated with the one or more processors, the memory for storing program instructions that, when read for execution by the one or more processors, perform the method of any of claims 1-7.
10. A computer storage medium, characterized in that it has stored thereon a computer program, wherein the program, when executed by a processor, implements the method according to any of claims 1-7.
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