Method and device for determining operation process and electronic equipment

Abstract

According to the operation process determination method and device and the electronic equipment provided by the invention, the data decision processing is performed according to the obtained real-time operation data, so that the multiple pieces of first recommendation data are obtained; according to the real-time operation data and a preset operation database, performing data marking processing from the multiple pieces of first recommendation data to obtain first target data; performing data decision processing according to the obtained second operation data to obtain multiple pieces of second recommendation data; and according to the second operation data and a preset operation database, performing data marking processing from the multiple pieces of second recommendation data to obtain second target data until the operation completion instruction is responded to end determination of the operation process. Compared with the prior art, the embodiment of the invention has the advantages that the current processing step can be quickly identified and the optimal next operation step and operation sequence can be determined when the system process is processed, so that the system operation process in an enterprise is more flexible, and the efficiency and the accuracy of processing a relatively complex operation system process are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for determining an operation flow, and an electronic device. Background Art

[0002] As the scale of enterprises continues to expand, the system operation process within the enterprise is becoming increasingly complex, and operators need to master a large number of operation steps and sequences to ensure the smooth operation of the business. The system operation process within the enterprise mainly includes the following points:

[0003] Process design: Use process design tools, such as business process management (BPM) systems, to define and configure system operation processes. Process design is usually customized based on business rules and operator experience. Process execution: Operators perform tasks on the operating system according to pre-designed process steps. When performing tasks, operators must strictly follow the pre-designed process steps to complete the work. Process monitoring: Use process monitoring tools to track process execution in real time to ensure that the business is carried out according to the established process, and at the same time facilitate management to evaluate process efficiency.

[0004] However, the existing system operation processes within enterprises mainly rely on preset process steps, which leads to relatively fixed system operation processes within enterprises. When dealing with more complex operating system processes, the accuracy and efficiency are low. Summary of the invention

[0005] The present disclosure provides a method and device for determining an operation process, and an electronic device. The main purpose is to solve the problem that the existing internal operation system process of an enterprise mainly relies on preset process steps, resulting in a relatively fixed internal operation system process of the enterprise, and the accuracy and efficiency are low when processing more complex operation system processes.

[0006] According to a first aspect of the present disclosure, a method for determining an operation process is provided, which includes:

[0007] Performing data decision processing according to the acquired real-time operation data to obtain a plurality of first recommended data; wherein the first recommended data is the next operation data adjacent to the real-time operation data in the operation path including the real-time operation data;

[0008] According to the real-time operation data and the preset operation database, data labeling processing is performed on the plurality of first recommended data to obtain first target data; wherein the preset operation database includes a plurality of first operation paths, the first operation paths are target operation paths corresponding to different operation scenarios obtained through reinforcement learning, and the first target data is determined by the first operation paths;

[0009] Performing data decision processing according to the acquired second operation data to obtain a plurality of second recommended data; wherein the second operation data is the next operation data acquired after the real-time operation data is acquired, and the second recommended data is the next operation data adjacent to the second operation data in the operation path containing the second operation data;

[0010] According to the second operation data and the preset operation database, data marking processing is performed from multiple second recommended data to obtain second target data until the operation completion instruction is responded to to end the determination of the operation process, wherein the second target data is determined by the first operation path.

[0011] Optionally, before performing data decision processing according to the acquired real-time operation data to obtain a plurality of first recommendation data, the method further includes:

[0012] Acquire historical operation data and successful operation data corresponding to each of the operation scenarios; wherein the successful operation data is operation data capable of successfully executing the corresponding operation scenario;

[0013] Performing path recognition processing according to the historical operation data and the operation success data through a reinforcement learning algorithm to obtain the first operation path corresponding to each of the operation scenarios, and performing data set construction processing according to the first operation path to obtain a path data set;

[0014] Acquire first process data corresponding to each of the operation scenarios, and perform flowchart construction processing according to the first process data to obtain a flowchart data set; wherein the first process data includes flowcharts of all the operation paths corresponding to each of the operation scenarios;

[0015] A database construction process is performed according to the preset operation rules, the path data set and the flowchart data set to obtain the preset operation database.

[0016] Optionally, performing data labeling processing from a plurality of first recommendation data according to the real-time operation data and a preset operation database to obtain first target data includes:

[0017] Performing matching processing in the preset operation database according to the real-time operation data to obtain a first matching result;

[0018] In a case where it is determined according to the first matching result that there is a first operation path containing the real-time operation data in the preset operation database, determining the first operation path containing the real-time operation data as a first target path;

[0019] According to the first target path, data matching processing is performed from the plurality of the first recommended data to obtain first data to be marked; wherein the first data to be marked is the first recommended data in the first target path;

[0020] The flowchart data corresponding to the first data to be marked is marked according to the preset operation database to obtain the first target data.

[0021] Optionally, after performing matching processing in the preset operation database according to the real-time operation data to obtain a first matching result, the method further includes:

[0022] When it is determined according to the first matching result that there is no first operation path containing the real-time operation data in the preset operation database, data labeling processing is performed on a plurality of the first recommended data according to the preset operation rule to obtain the first target data.

[0023] Optionally, before performing data decision processing according to the acquired real-time operation data to obtain a plurality of first recommendation data, the method further includes:

[0024] Determining whether process information of the operation process in which the real-time operation data is located is obtained;

[0025] When it is determined that the process information has not been acquired, data decision processing is performed according to the acquired real-time operation data to obtain a plurality of the first recommendation data.

[0026] Optionally, after determining whether process information of the operation process in which the real-time operation data is located is obtained, the method further includes:

[0027] In the case where it is determined that the process information is obtained, path selection processing is performed in the preset operation database according to the process information and the real-time operation data to obtain a second operation path; wherein the second operation path is the first operation path including the real-time operation data;

[0028] According to the second operation path, flowchart data matching processing is performed in the preset operation database to obtain target flowchart data corresponding to the second operation path; wherein the preset flowchart data set includes flowchart data corresponding to all operation paths;

[0029] A target operation process is determined according to the target flow chart data, wherein the target operation process at least includes target operation data, and the target operation data is the next operation data adjacent to the real-time operation data in the second operation path.

[0030] Optionally, before performing data decision processing according to the acquired second operation data to obtain a plurality of second recommendation data, the method further includes:

[0031] Performing data detection processing on the second operation data to obtain a data detection result;

[0032] When it is determined that the second operation data is a non-compliant operation according to the data detection result, a pop-up window displays preset warning information and preset suggestion information.

[0033] Optionally, performing data detection processing on the real-time operation data to obtain a data detection result includes:

[0034] Perform matching processing in the preset path library according to the second operation data to obtain a second matching result; wherein the preset path library includes all operation paths;

[0035] When it is determined according to the second matching result that there is no operation path containing the second operation data in the preset path library, determining that the second operation data is a non-compliant operation; and / or,

[0036] An operation frequency of the second operation data is obtained, and when the operation frequency is less than a preset frequency threshold, the second operation data is determined to be a non-compliant operation, wherein the operation frequency is the number of times the second operation data appears in all the operation paths.

[0037] According to a second aspect of the present disclosure, a device for determining an operation process is provided, comprising:

[0038] A first decision unit is used to perform data decision processing according to the acquired real-time operation data to obtain a plurality of first recommended data; wherein the first recommended data is the next operation data adjacent to the real-time operation data in the operation path containing the real-time operation data;

[0039] a first labeling unit, configured to perform data labeling processing from a plurality of the first recommended data according to the real-time operation data and a preset operation database to obtain first target data; wherein the preset operation database includes a plurality of first operation paths, the first operation paths are target operation paths corresponding to different operation scenarios obtained through reinforcement learning, and the first target data is determined by the first operation paths;

[0040] a second decision unit, configured to perform data decision processing according to the acquired second operation data to obtain a plurality of second recommended data; wherein the second operation data is the next operation data acquired after the real-time operation data is acquired, and the second recommended data is the next operation data adjacent to the second operation data in the operation path including the second operation data;

[0041] A second marking unit is used to perform data marking processing from multiple second recommended data according to the second operation data and the preset operation database to obtain second target data until responding to the operation completion instruction to end the determination of the operation process, wherein the second target data is determined by the first operation path.

[0042] Optionally, the device further comprises:

[0043] An acquisition unit, used to acquire the historical operation data and operation success data corresponding to each of the operation scenarios; wherein the operation success data is the operation data capable of successfully executing the corresponding operation scenario;

[0044] an identification unit, configured to perform path identification processing by using a reinforcement learning algorithm according to the historical operation data and the operation success data, so as to obtain the first operation path corresponding to each of the operation scenarios;

[0045] A construction unit, configured to perform data set construction processing according to the first operation path to obtain a path data set;

[0046] The acquisition unit is further used to acquire first process data corresponding to each of the operation scenarios, and perform flowchart construction processing according to the first process data to obtain a flowchart data set; wherein the first process data includes flowcharts of all the operation paths corresponding to each of the operation scenarios;

[0047] The construction unit is further used to perform database construction processing according to preset operation rules, the path data set and the flowchart data set to obtain the preset operation database.

[0048] Optionally, the first marking unit includes:

[0049] A first matching module, configured to perform matching processing in the preset operation database according to the real-time operation data to obtain a first matching result;

[0050] A determination module, configured to determine the first operation path containing the real-time operation data as a first target path when it is determined according to the first matching result that there is a first operation path containing the real-time operation data in the preset operation database;

[0051] A second matching module, configured to perform data matching processing from the plurality of the first recommended data according to the first target path to obtain first data to be marked; wherein the first data to be marked is the first recommended data in the first target path;

[0052] The marking module is used to mark the flowchart data corresponding to the first data to be marked according to the preset operation database to obtain the first target data.

[0053] Optionally, the marking module in the first marking unit is also used to, when it is determined based on the first matching result that there is no first operation path containing the real-time operation data in the preset operation database, perform data marking processing from multiple first recommended data according to the preset operation rules to obtain the first target data.

[0054] Optionally, the device further comprises:

[0055] A first determining unit, configured to determine whether process information of the operation process in which the real-time operation data is located is obtained;

[0056] The first decision unit is further configured to, when it is determined that the process information has not been acquired, perform data decision processing according to the acquired real-time operation data to obtain a plurality of the first recommendation data.

[0057] Optionally, the device further comprises:

[0058] A selection unit is used to, when it is determined that the process information is obtained, perform path selection processing in the preset operation database according to the process information and the real-time operation data to obtain a second operation path; wherein the second operation path is the first operation path including the real-time operation data;

[0059] a matching unit, configured to perform flowchart data matching processing in the preset operation database according to the second operation path, and obtain target flowchart data corresponding to the second operation path; wherein the preset flowchart data set includes flowchart data corresponding to all operation paths;

[0060] The second determining unit is used to determine a target operation process according to the target flowchart data, wherein the target operation process at least includes target operation data, and the target operation data is the next operation data adjacent to the real-time operation data in the second operation path.

[0061] Optionally, the device further comprises:

[0062] a detection unit, configured to perform data detection processing on the second operation data to obtain a data detection result;

[0063] A display unit is used to display preset warning information and preset suggestion information in a pop-up window when it is determined that the second operation data is a non-compliant operation according to the data detection result.

[0064] Optionally, the detection unit includes:

[0065] A matching module, configured to perform matching processing in the preset path library according to the second operation data to obtain a second matching result; wherein the preset path library includes all operation paths;

[0066] a determination module, configured to determine that the second operation data is a non-compliant operation when it is determined according to the second matching result that there is no operation path containing the second operation data in the preset path library; and / or,

[0067] The determination module is also used to obtain the operation frequency of the second operation data, and determine that the second operation data is a non-compliant operation when the operation frequency is less than a preset frequency threshold, wherein the operation frequency is the number of times the second operation data appears in all the operation paths.

[0068] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0069] at least one processor; and

[0070] a memory communicatively connected to the at least one processor; wherein,

[0071] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0072] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0073] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0074] The present disclosure provides a method and device for determining an operation flow, and an electronic device. The method performs data decision processing according to the acquired real-time operation data to obtain a plurality of first recommended data; wherein the first recommended data is the next operation data adjacent to the real-time operation data in the operation path containing the real-time operation data; data labeling processing is performed from the plurality of first recommended data according to the real-time operation data and a preset operation database to obtain first target data; wherein the preset operation database includes a plurality of first operation paths, the first operation paths are target operation paths corresponding to different operation scenarios obtained by reinforcement learning, and the first target data is determined by the first operation paths; data decision processing is performed according to the acquired second operation data to obtain a plurality of second recommended data; wherein the second operation data is the next operation data acquired after the real-time operation data is acquired, and the second recommended data is the next operation data adjacent to the second operation data in the operation path containing the second operation data; data labeling processing is performed from the plurality of second recommended data according to the second operation data and the preset operation database to obtain second target data, until responding to an operation completion instruction to end the determination of the operation flow, wherein the second target data is determined by the first operation path. Compared with the related art, the embodiment of the present disclosure determines the corresponding multiple next operation data, i.e., multiple first recommended data, based on the real-time operation data, and marks the more suitable next operation data, i.e., the first target data, from the multiple first recommended data, and then determines the more suitable next operation data, i.e., the second target data, based on the next operation of the real-time operation data, i.e., the second operation data, until the operation is completed. During the system process processing, the current processing step can be quickly identified, and the best next operation step and operation sequence, i.e., the operation step and operation sequence obtained through reinforcement learning, can be determined, thereby making the system operation process within the enterprise more flexible and improving the efficiency and accuracy in processing more complex operating system processes.

[0075] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0077] Figure 1 A flowchart of a method for determining an operation process provided by an embodiment of the present disclosure;

[0078] Figure 2 A schematic diagram of a construction process of a preset operation database provided in an embodiment of the present disclosure;

[0079] Figure 3 A flowchart of another method for determining an operation flow provided by an embodiment of the present disclosure;

[0080] Figure 4 A schematic diagram of the structure of a device for determining an operation flow provided by an embodiment of the present disclosure;

[0081] Figure 5 A schematic diagram of the structure of another device for determining an operation flow provided by an embodiment of the present disclosure;

[0082] Figure 6 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0083] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0084] The following describes a method and device for determining an operation flow and an electronic device according to an embodiment of the present disclosure with reference to the accompanying drawings.

[0085] Figure 1 A flowchart of a method for determining an operation process provided in an embodiment of the present disclosure.

[0086] like Figure 1 As shown, the method comprises the following steps:

[0087] Step 101, performing data decision processing according to the acquired real-time operation data to obtain a plurality of first recommended data; wherein the first recommended data is the next operation data adjacent to the real-time operation data in the operation path including the real-time operation data.

[0088] In the embodiments of the present disclosure, the real-time operation data is the operation data generated by the real-time operation steps in the corresponding operation process, such as: clicking, inputting, selecting, etc. According to the real-time operation data, the current operation step, i.e., the real-time operation step, can be located. Specifically, the embodiments of the present disclosure do not limit the content of the real-time operation data.

[0089] Among them, the multiple first recommended data at least include the next operation data adjacent to the real-time operation data in all operation paths containing real-time operation data, that is, the multiple first recommended data refer to the operation data corresponding to all possible operation steps after the real-time operation step. All operation steps after the real-time operation step can be determined through the multiple first recommended data.

[0090] By making decisions on the operation data corresponding to the real-time operation steps and determining all the operation steps after the real-time operation steps, it is convenient for operators to determine the operations to be performed later, thereby improving efficiency.

[0091] Step 102: Perform data labeling processing on the plurality of first recommended data according to the real-time operation data and the preset operation database to obtain first target data; wherein the preset operation database includes a plurality of first operation paths, the first operation paths are target operation paths corresponding to different operation scenarios obtained through reinforcement learning, and the first target data is determined by the first operation paths.

[0092] In the embodiment of the present disclosure, the preset operation database is a pre-constructed database including the best operation paths (i.e., multiple first operation paths) under different operation scenarios, and a visualized operation flowchart corresponding to each operation path. The best operation path is the first operation path, which is obtained by reinforcement learning through historical operation data and successful operation data under the current scenario. Different operation scenarios can be different system operation processes, such as: account creation process, account cancellation process, etc. Specifically, the embodiment of the present disclosure does not limit the content of the preset operation database and the content of the operation scenario.

[0093] The first target data is the next best operation data of the real-time operation data, that is, the next operation data adjacent to the real-time operation data in the first operation path.

[0094] Among them, the process of data marking according to the preset operation database is the process of determining the next best operation data corresponding to the real-time operation data. The preset operation database needs to be constructed in advance, for example: using machine learning algorithms (such as decision trees, reinforcement learning, etc.) to analyze the operator's historical operation data and success cases in advance, identify the best operation paths under different scenarios, and construct a path data set; and based on standardized process automation technology, model the standardized and fixed operation steps within the enterprise to form a visual operation flowchart, obtain a flowchart data set, and jointly construct the preset operation database through the path data set and the flowchart data set.

[0095] Step 103, performing data decision processing based on the acquired second operation data to obtain multiple second recommended data; wherein, the second operation data is the next operation data acquired after acquiring the real-time operation data, and the second recommended data is the next operation data adjacent to the second operation data in the operation path containing the second operation data.

[0096] In the disclosed embodiment, the second operation data is the operation data corresponding to the next operation step executed by the operator after the operator executes the operation step corresponding to the real-time operation data. At the same time, the operator does not necessarily follow the recommended steps when performing process operations, that is, after the operator executes the operation step corresponding to the real-time operation data, the operator does not necessarily continue to execute the operation step corresponding to the first target data, but may execute other operation steps, namely the second operation step (the operation step corresponding to the second operation data). At this time, the operation data corresponding to all possible operation steps after the second operation step, namely multiple second recommended data, will be determined based on the second operation data. All operation steps after the second operation step can be determined through the multiple second recommended data.

[0097] For details, please refer to the description in the above step 101, so it will not be described here one by one.

[0098] Step 104, performing data marking processing from multiple second recommended data according to the second operation data and the preset operation database to obtain second target data, until responding to the operation completion instruction to end the determination of the operation process, wherein the second target data is determined by the first operation path.

[0099] In the embodiment of the present disclosure, after all the operation steps after the second operation step can be determined through multiple second recommended data, the best operation step after the second operation step, that is, the operation step corresponding to the second target data, will be determined, and so on. Each time after the operator performs an operation, the best step after the current step will be determined according to the operation data until the operation is completed.

[0100] The operation completion instruction is an instruction automatically generated after the operator completes the operation process. Through the operation completion instruction, it can be determined that the operation process can be directly terminated without performing subsequent operations.

[0101] Specifically, regarding the embodiment of the present disclosure, reference may be made to the description in the above step 102, so detailed description will not be given here.

[0102] Furthermore, the first target data and the second target data are equivalent to automatically deciding and recommending an optimal operating procedure based on the real-time operating data and the second operating data. Subsequent operators can perform corresponding operations according to the optimal operating procedures, which can improve the efficiency and accuracy of operators in handling various operations, while ensuring that the operating process meets the standard requirements and enhances the compliance and fluency of the work.

[0103] The present disclosure provides a method for determining an operation flow, which performs data decision processing according to acquired real-time operation data to obtain a plurality of first recommended data; wherein the first recommended data is the next operation data adjacent to the real-time operation data in the operation path containing the real-time operation data; performs data labeling processing from the plurality of first recommended data according to the real-time operation data and a preset operation database to obtain first target data; wherein the preset operation database includes a plurality of first operation paths, the first operation paths are target operation paths corresponding to different operation scenarios obtained by reinforcement learning, and the first target data is determined by the first operation paths; performs data decision processing according to acquired second operation data to obtain a plurality of second recommended data; wherein the second operation data is the next operation data acquired after the real-time operation data is acquired, and the second recommended data is the next operation data adjacent to the second operation data in the operation path containing the second operation data; performs data labeling processing from the plurality of second recommended data according to the second operation data and the preset operation database to obtain second target data, until responding to an operation completion instruction to end the determination of the operation flow, wherein the second target data is determined by the first operation path. Compared with the related art, the embodiment of the present disclosure determines the corresponding multiple next operation data, i.e., multiple first recommended data, based on the real-time operation data, and marks the more suitable next operation data, i.e., the first target data, from the multiple first recommended data, and then determines the more suitable next operation data, i.e., the second target data, based on the next operation of the real-time operation data, i.e., the second operation data, until the operation is completed. During the system process processing, the current processing step can be quickly identified, and the best next operation step and operation sequence, i.e., the operation step and operation sequence obtained through reinforcement learning, can be determined, thereby making the system operation process within the enterprise more flexible and improving the efficiency and accuracy in processing more complex operating system processes.

[0104] In one implementation of the embodiment of the present disclosure, before performing the path selection process, it is necessary to first construct a preset operation database so that automatic decision making and recommendation of the next best operation step can be made. Regarding the construction of the preset operation database, the embodiment of the present disclosure provides a schematic diagram of the construction process of the preset operation database, such as Figure 2As shown, including:

[0105] Step 201, obtaining the historical operation data and operation success data corresponding to each of the operation scenarios; wherein the operation success data is the operation data capable of successfully executing the corresponding operation scenario.

[0106] In the embodiment of the present disclosure, the historical operation data includes the historical operation data corresponding to all system operation processes (i.e., operation scenarios). At the same time, since the historical operation data refers to the record of all operations in the system operation process in the past period of time, including but not limited to: the time of the operation, the operator, the operation steps, the operation results (whether successful or not) and any other information related to the operation, the historical operation data includes operation error data, and the operation error data is the operation data that cannot successfully execute the corresponding system operation process.

[0107] It should be noted that the historical operation data also includes the operation success data, that is, the historical operation data provides a comprehensive record of the operation, and the operation success data is a subset of the historical operation data, focusing on the records of successful completion and achievement of the expected results.

[0108] Since the historical operation data includes successful operation data, error operation data and other operation data, it is necessary to extract the successful operation data (i.e., obtain the historical operation data and the successful operation data in parallel) for data verification to ensure that the first operation path obtained subsequently is constructed on the basis of the successful operation data.

[0109] Step 202: Perform path recognition processing based on the historical operation data and the operation success data through a reinforcement learning algorithm to obtain the first operation path corresponding to each of the operation scenarios, and perform data set construction processing based on the first operation path to obtain a path data set.

[0110] In the embodiment of the present disclosure, when performing path recognition processing, a reinforcement learning algorithm may be used. At the same time, other recognition algorithms may also be used, such as a decision tree.

[0111] By using machine learning algorithms (preset recognition algorithms such as decision trees, reinforcement learning, etc.) to analyze operators' historical operation data and success cases, the best operation paths in different scenarios can be identified. Through path recognition processing, it can help optimize the system operation process, reduce errors and improve efficiency. For example: if reinforcement learning can determine that a specific step combination has the advantages of high efficiency and high operation frequency on the basis of successfully completing the task, then this step combination can be recommended as the first operation path.

[0112] When constructing the data set, the first operation data may be processed in a set to obtain the path data set.

[0113] Step 203, obtaining the first process data corresponding to each of the operation scenarios, and performing flowchart construction processing based on the first process data to obtain a flowchart data set; wherein the first process data includes flowcharts of all the operation paths corresponding to each of the operation scenarios.

[0114] In the embodiment of the present disclosure, the first process data is all possible operation paths involved in each system operation process (operation scenario) and the corresponding flowchart data, including but not limited to: all standardized and fixed operation steps within the enterprise. Specifically, the embodiment of the present disclosure does not limit the first process data.

[0115] When constructing a flowchart, a preset construction algorithm may be used, such as Business Process Automation (BPA). Business Process Automation can use software and technology to perform repetitive business tasks, with the goal of reducing human intervention, improving efficiency, reducing error rates, and optimizing the entire business process. Specifically, regarding the preset construction algorithm during the flowchart construction process, the disclosed embodiment does not limit it.

[0116] Based on the standardized process automation technology, the standardized and fixed operation steps within the enterprise are modeled to form a visual operation flow chart (preset flow chart data). The system can automatically generate and recommend operation steps based on the real-time operation status.

[0117] Step 204 , performing database construction processing according to the preset operation rules, the path data set and the flowchart data set to obtain the preset operation database.

[0118] In the embodiment of the present disclosure, the preset operation rule is a custom-set rule used to handle some specific system operation processes, i.e., operation scenarios. For example, the only system operation processes required within an enterprise are registration and logout, and now an additional stored system operation process needs to be added. At this time, a stored process can be customized and added to the preset operation database. If the real-time data is unique to the stored process, it can be directly recommended.

[0119] The database construction process is a process of designing and filling a database, which can organize the preset operation rules, the path data set and the flowchart data set into a structured database to facilitate query, management and optimization of the system operation process.

[0120] By building a preset operation database, the optimal operation path in each system operation process can be solidified, and quick access and efficient management can be provided in the form of a database, thereby improving the efficiency of the entire system operation process and user experience.

[0121] In one implementable manner of the embodiment of the present disclosure, as a refinement of the above-mentioned step 102, when performing data labeling processing, the following manner may also be adopted but is not limited to: matching processing is performed in the preset operation database according to the real-time operation data to obtain a first matching result; when it is determined according to the first matching result that there is a first operation path containing the real-time operation data in the preset operation database, the first operation path containing the real-time operation data is determined as the first target path; data matching processing is performed from multiple first recommended data according to the first target path to obtain first data to be labeled; wherein the first data to be labeled is the first recommended data in the first target path; and the flowchart data corresponding to the first data to be labeled is labeled according to the preset operation database to obtain the first target data.

[0122] In the embodiment of the present disclosure, since the first operation path is the optimal operation path under different operation scenarios, two situations may occur when the real-time data is matched. Situation 1: there is a first operation path containing the real-time operation data; Situation 2: there is no first operation path containing the real-time operation data. When situation 1 occurs, the best operation data after the real-time operation data, i.e., the first data to be marked, can be determined based on the matched first operation path, i.e., the first target path.

[0123] Among them, the first data to be marked is marked in the flowchart, which is the first target data. The difference between the first data to be marked and the first target data is only whether it is marked in the visual flowchart. The marking method includes but is not limited to: highlighting the flowchart data corresponding to the target operation path, color marking the flowchart data corresponding to the target operation path (or other marking methods such as text marking). Specifically, the embodiment of the present disclosure does not limit the marking method.

[0124] The above describes the implementation method for situation one. Next, when situation two occurs, it can be implemented in but not limited to the following manner: when it is determined according to the first matching result that there is no first operation path containing the real-time operation data in the preset operation database, data marking processing is performed from multiple first recommended data according to the preset operation rules to obtain the first target data.

[0125] In the embodiment of the present disclosure, the preset operation rule is a custom set rule used to process some specific system operation processes, i.e., operation scenarios. Specifically, regarding the preset operation rule, please refer to the description in the above step 204, so it will not be repeated here.

[0126] When there is no first operation path containing the real-time operation data, it means that the real-time operation data is data corresponding to a unique operation step in the preset operation rule. At this time, the operation step in the preset operation rule (that is, the first recommended data in the preset operation rule) can be directly marked.

[0127] In one possible implementation of the embodiment of the present disclosure, when the operator performs the system operation flow, two situations may also occur. Situation 1: the operator forgets to predetermine the system operation flow and directly performs the operation. Situation 2: the operator predetermines the operation flow. When Situation 1 occurs, it is necessary to decide all the operation data after the real-time operation data, and determine the best operation steps among all the operation data after the real-time operation data. The best operation steps determined at this time are the operation steps obtained through reinforcement learning, which do not necessarily only include the best operation steps in the system operation flow that the operator needs to perform. That is, when Situation 1 occurs, it is necessary to Figure 1 The method described above is used to handle the situation. When situation 2 occurs, it is only necessary to recommend the best steps under this process based on the process information of the determined system operation process.

[0128] Therefore, when determining the operation process, it is necessary to first determine whether the process information of the predetermined system operation process has been obtained, and then determine the operation process based on possible situations. Specifically, when judging the situation, it can also be implemented by but not limited to the following methods: determine whether the process information of the operation process in which the real-time operation data is located has been obtained; if it is determined that the process information has not been obtained, perform data decision processing based on the obtained real-time operation data to obtain multiple first recommended data.

[0129] In the embodiment of the present disclosure, when it is determined that the process information is not obtained, that is, when situation 1 occurs, Figure 1 Processed as described.

[0130] When situation 2 occurs, that is, when it is determined that the process information is obtained, the embodiment of the present disclosure further provides a flowchart of another method for determining the operation process, such as Figure 3 As shown, including:

[0131] Step 301, when it is determined that the process information is obtained, path selection processing is performed in the preset operation database according to the process information and the real-time operation data to obtain a second operation path; wherein the second operation path is the first operation path including the real-time operation data.

[0132] In the embodiments of the present disclosure, the real-time operation data is the operation data generated by the real-time operation steps in the corresponding system operation process, such as: clicking, inputting, selecting, etc. According to the real-time operation data, the current operation step, i.e., the real-time operation step, can be located. The preset operation database is a pre-constructed database including the best operation paths under different scenarios (i.e., the first operation paths corresponding to each of the multiple system operation processes). The best operation path is the first operation path, which represents the operation path with the highest operation frequency under the current scenario. Different scenarios are different system operation processes, such as: account creation process, account cancellation process, etc. Specifically, the embodiments of the present disclosure do not limit the content of the real-time operation data, the content of the preset operation database, and the content of the system operation process.

[0133] The second operation path is the optimal operation path in the system operation process corresponding to the real-time operation data. Through the second operation path, the next optimal operation data of the real-time operation data can be determined, that is, the next operation data adjacent to the real-time operation data in the second operation path.

[0134] The process of performing path selection in the preset operation database is the process of determining the best operation path corresponding to the real-time operation data.

[0135] Step 302: Perform flowchart data matching processing in the preset operation database according to the second operation path to obtain target flowchart data corresponding to the second operation path; wherein the preset flowchart data set includes flowchart data corresponding to all operation paths.

[0136] In an embodiment of the present disclosure, the preset operation database includes a flowchart data set, which is a pre-constructed data set of flowcharts containing all operation paths within the enterprise. The corresponding flowchart, i.e., the target flowchart data, can be found from the flowchart data set through the second operation path.

[0137] Among them, the flowchart data set is a visualized flowchart data set, therefore, the target flowchart data can be regarded as data obtained by visualizing the second operation path in a flowchart manner, and the operation steps corresponding to each operation data in the second operation path can be accurately determined. After obtaining the target flowchart data, the next best operation step of the operation step corresponding to the real-time operation data can be determined according to the target flowchart data. Since the second operation path is the best operation step in the system operation process corresponding to the real-time operation data, the next best operation step is the operation step corresponding to the next operation data adjacent to the real-time operation data in the second operation path.

[0138] Step 303: determine a target operation process according to the target flowchart data, wherein the target operation process at least includes target operation data, and the target operation data is the next operation data adjacent to the real-time operation data in the second operation path.

[0139] In the embodiment of the present disclosure, the target operation process is the optimal overall operation steps of the system operation process corresponding to the real-time operation data. By operating according to the steps corresponding to the target operation process, it can be ensured that the system operation process corresponding to the real-time operation data is completed accurately and efficiently.

[0140] It should be noted that the target operation process not only includes the target operation data, but also includes the operation data in the entire second operation path. Therefore, the target operation process is the data obtained after marking the target flowchart data in the flowchart data set. For example: in the flowchart data set, the target flowchart data is highlighted to obtain the target operation process.

[0141] Furthermore, the target operation process is equivalent to automatically deciding and recommending an optimal operation procedure based on the real-time operation data. Subsequent operators can perform corresponding operations according to the target operation process, which can improve the efficiency and accuracy of operators in handling various operations, while ensuring that the operation process processing meets the standard requirements and enhances the compliance and fluency of the work.

[0142] In one implementable method of the embodiment of the present disclosure, it is also necessary to monitor the operation steps to ensure the accuracy of the operator's operation. Specifically, regarding the monitoring of the operation steps, the following methods may also be used but are not limited to: performing data detection processing on the second operation data to obtain a data detection result; when it is determined that the second operation data is a non-compliant operation based on the data detection result, a pop-up window displays a preset warning message and preset suggestion information.

[0143] In the embodiment of the present disclosure, the second operation data is the operation data corresponding to the next operation step executed by the operator after the operator executes the operation step corresponding to the real-time operation data. Since the operator may not operate in accordance with the target operation process when performing the process operation, the second operation data needs to be detected to determine whether the operator's process operation is compliant.

[0144] The preset warning information and preset suggestion information are custom set information, for example: the preset warning information is that the operation step may be wrong, please check! The preset suggestion information is a suggestion to confirm whether the second operation data is correct. Specifically, the preset warning information and preset suggestion information are not limited in the embodiment of the present disclosure.

[0145] In one implementable manner of the embodiment of the present disclosure, when performing data detection processing on the second operation data, it can be implemented in but not limited to the following manner: performing matching processing in the preset path library according to the second operation data to obtain a second matching result; wherein the preset path library contains all operation paths; when it is determined according to the second matching result that there is no operation path containing the second operation data in the preset path library, determining that the second operation data is a non-compliant operation; and / or obtaining the operation frequency of the second operation data, and when the operation frequency is less than a preset frequency threshold, determining that the second operation data is a non-compliant operation, wherein the operation frequency is the number of times the second operation data appears in all the operation paths.

[0146] In the embodiment of the present disclosure, the operation frequency is the execution frequency of the second operation data in all the operation paths, and can be used to indicate the usage frequency of real-time operation data.

[0147] The preset frequency threshold is a custom-set value, which is used to determine whether the second operation data is compliant. The preset frequency threshold can be set according to business needs, security requirements or operation specifications, for example: 10 times, 5 times, etc. Specifically, the preset frequency threshold is not limited in the embodiment of the present disclosure.

[0148] In summary, the embodiments of the present disclosure can achieve the following technical effects:

[0149] The disclosed embodiment determines the corresponding multiple next operation data, i.e., multiple first recommended data, based on the real-time operation data, and marks the more suitable next operation data, i.e., the first target data, from the multiple first recommended data, and then determines the more suitable next operation data, i.e., the second target data, for the second operation data based on the next operation of the real-time operation data, i.e., the second operation data, until the operation is completed. When performing system process processing, the current processing step can be quickly identified, and the best next operation step and operation sequence, i.e., the operation step and operation sequence obtained through reinforcement learning, can be determined, thereby making the system operation process within the enterprise more flexible and improving the efficiency and accuracy in processing more complex operating system processes.

[0150] Corresponding to the above-mentioned method for determining the operation flow, the present invention also provides a device for determining the operation flow. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0151] Figure 4 A schematic diagram of a structure of a device for determining an operation flow provided by an embodiment of the present disclosure, such as Figure 4 As shown, including:

[0152] The first decision unit 401 is used to perform data decision processing according to the acquired real-time operation data to obtain a plurality of first recommended data; wherein the first recommended data is the next operation data adjacent to the real-time operation data in the operation path containing the real-time operation data;

[0153] A first labeling unit 402 is configured to perform data labeling processing from a plurality of first recommended data according to the real-time operation data and a preset operation database to obtain first target data; wherein the preset operation database includes a plurality of first operation paths, each of which is a target operation path corresponding to each of different operation scenarios obtained through reinforcement learning, and the first target data is determined by the first operation path;

[0154] The second decision unit 403 is used to perform data decision processing according to the acquired second operation data to obtain multiple second recommended data; wherein the second operation data is the next operation data acquired after the real-time operation data is acquired, and the second recommended data is the next operation data adjacent to the second operation data in the operation path containing the second operation data;

[0155] The second marking unit 404 is used to perform data marking processing from multiple second recommended data according to the second operation data and the preset operation database to obtain second target data until responding to the operation completion instruction to end the determination of the operation process, wherein the second target data is determined by the first operation path.

[0156] The present disclosure provides a device for determining an operation process, which performs path selection processing in a preset operation database according to acquired real-time operation data to obtain a target operation path; wherein the preset operation database includes first operation paths corresponding to each of a plurality of system operation processes, and the first operation path is an operation path with the highest operation frequency in the corresponding system operation process; data matching processing is performed in a preset flowchart data set according to the target operation path to obtain target flowchart data corresponding to the target operation path; wherein the preset flowchart data set includes flowchart data corresponding to all the operation paths; and a target operation process is determined according to the target flowchart data, wherein the target operation process at least includes target operation data, and the target operation data is the next operation data adjacent to the real-time operation data in the target operation path. Compared with the related art, the embodiment of the present disclosure determines the corresponding target operation path according to the real-time operation data, determines the corresponding target flow chart according to the target operation path, and determines the target operation process of the next operation step (target operation data) containing the real-time operation data according to the indication of the target flow chart. When performing system process processing, the current processing step can be quickly identified, and the next operation step and operation sequence with a higher operation frequency can be determined, thereby making the system operation process within the enterprise more flexible and improving the efficiency and accuracy when processing more complex operating system processes.

[0157] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0158] The acquisition unit 405 is used to acquire the historical operation data and operation success data corresponding to each of the operation scenarios; wherein the operation success data is the operation data that can successfully execute the corresponding operation scenario;

[0159] An identification unit 406 is configured to perform path identification processing by using a reinforcement learning algorithm according to the historical operation data and the operation success data, so as to obtain the first operation path corresponding to each of the operation scenarios;

[0160] A construction unit 407, configured to perform a data set construction process according to the first operation path to obtain a path data set;

[0161] The acquisition unit 405 is further used to acquire first process data corresponding to each of the operation scenarios, and perform flowchart construction processing according to the first process data to obtain a flowchart data set; wherein the first process data includes flowcharts of all the operation paths corresponding to each of the operation scenarios;

[0162] The construction unit 407 is further configured to perform database construction processing according to preset operation rules, the path data set, and the flowchart data set to obtain the preset operation database.

[0163] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the first marking unit 402 includes:

[0164] A first matching module 4021, configured to perform matching processing in the preset operation database according to the real-time operation data to obtain a first matching result;

[0165] A determination module 4022 is configured to determine, when it is determined according to the first matching result that there is a first operation path containing the real-time operation data in the preset operation database, the first operation path containing the real-time operation data as a first target path;

[0166] A second matching module 4023 is used to perform data matching processing from the plurality of first recommended data according to the first target path to obtain first data to be marked; wherein the first data to be marked is the first recommended data in the first target path;

[0167] The marking module 4024 is used to mark the flowchart data corresponding to the first data to be marked according to the preset operation database to obtain the first target data.

[0168] Furthermore, in a possible implementation of the embodiment of the present disclosure, the marking module 4024 in the first marking unit 402 is also used to, when it is determined based on the first matching result that there is no first operation path containing the real-time operation data in the preset operation database, perform data marking processing from multiple first recommended data according to the preset operation rules to obtain the first target data.

[0169] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0170] A first determining unit 408 is used to determine whether the process information of the operation process in which the real-time operation data is located is obtained;

[0171] The first decision unit 408 is further configured to, when it is determined that the process information has not been acquired, perform data decision processing according to the acquired real-time operation data to obtain a plurality of the first recommendation data.

[0172] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0173] A selection unit 409 is configured to, when it is determined that the process information is obtained, perform path selection processing in the preset operation database according to the process information and the real-time operation data to obtain a second operation path; wherein the second operation path is the first operation path including the real-time operation data;

[0174] A matching unit 410 is used to perform flowchart data matching processing in the preset operation database according to the second operation path to obtain target flowchart data corresponding to the second operation path; wherein the preset flowchart data set includes flowchart data corresponding to all operation paths;

[0175] The second determining unit 411 is used to determine a target operation process according to the target flowchart data, wherein the target operation process at least includes target operation data, and the target operation data is the next operation data adjacent to the real-time operation data in the second operation path.

[0176] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0177] A detection unit 412, configured to perform data detection processing on the second operation data to obtain a data detection result;

[0178] The display unit 413 is used to display preset warning information and preset suggestion information in a pop-up window when it is determined that the second operation data is a non-compliant operation according to the data detection result.

[0179] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the detection unit 412 includes:

[0180] A matching module 4121 is used to perform matching processing in the preset path library according to the second operation data to obtain a second matching result; wherein the preset path library contains all operation paths;

[0181] A determination module 4122 is configured to determine that the second operation data is a non-compliant operation when it is determined according to the second matching result that there is no operation path containing the second operation data in the preset path library; and / or,

[0182] The determination module 4122 is also used to obtain the operation frequency of the second operation data, and when the operation frequency is less than a preset frequency threshold, determine that the second operation data is a non-compliant operation, wherein the operation frequency is the number of times the second operation data appears in all the operation paths.

[0183] It should be noted that the above explanation of the method embodiment is also applicable to the device of the embodiment of the present disclosure, and the principle is the same, which is no longer limited in the embodiment of the present disclosure.

[0184] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0185] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0186] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded from a storage unit 608 to a RAM (Random Access Memory) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I / O (Input / Output) interface 605 is also connected to the bus 604.

[0187] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0188] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a method for determining an operation flow. For example, in some embodiments, the method for determining an operation flow may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the determination method of the aforementioned operation flow in any other appropriate manner (for example, by means of firmware).

[0189] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0190] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0191] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0192] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0193] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0194] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0195] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0196] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0197] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining an operation process, characterized in that: include: Performing data decision processing according to the acquired real-time operation data to obtain a plurality of first recommended data; wherein the first recommended data is the next operation data adjacent to the real-time operation data in the operation path including the real-time operation data; According to the real-time operation data and the preset operation database, data labeling processing is performed on the plurality of first recommended data to obtain first target data; wherein the preset operation database includes a plurality of first operation paths, the first operation paths are target operation paths corresponding to different operation scenarios obtained through reinforcement learning, and the first target data is determined by the first operation paths; Performing data decision processing according to the acquired second operation data to obtain a plurality of second recommended data; wherein the second operation data is the next operation data acquired after the real-time operation data is acquired, and the second recommended data is the next operation data adjacent to the second operation data in the operation path containing the second operation data; According to the second operation data and the preset operation database, data marking processing is performed from multiple second recommended data to obtain second target data until the operation completion instruction is responded to to end the determination of the operation process, wherein the second target data is determined by the first operation path.

2. The method according to claim 1, characterized in that Before performing data decision processing according to the acquired real-time operation data to obtain a plurality of first recommendation data, the method further includes: Acquire historical operation data and successful operation data corresponding to each of the operation scenarios; wherein the successful operation data is operation data capable of successfully executing the corresponding operation scenario; Performing path recognition processing according to the historical operation data and the operation success data through a reinforcement learning algorithm to obtain the first operation path corresponding to each of the operation scenarios, and performing data set construction processing according to the first operation path to obtain a path data set; Acquire first process data corresponding to each of the operation scenarios, and perform flowchart construction processing according to the first process data to obtain a flowchart data set; wherein the first process data includes flowcharts of all the operation paths corresponding to each of the operation scenarios; A database construction process is performed according to the preset operation rules, the path data set and the flowchart data set to obtain the preset operation database.

3. The method according to claim 2, characterized in that The performing data labeling processing from the plurality of first recommended data according to the real-time operation data and the preset operation database to obtain the first target data comprises: Performing matching processing in the preset operation database according to the real-time operation data to obtain a first matching result; In a case where it is determined according to the first matching result that there is a first operation path containing the real-time operation data in the preset operation database, determining the first operation path containing the real-time operation data as a first target path; According to the first target path, data matching processing is performed from the plurality of the first recommended data to obtain first data to be marked; wherein the first data to be marked is the first recommended data in the first target path; The flowchart data corresponding to the first data to be marked is marked according to the preset operation database to obtain the first target data.

4. The method according to claim 3, characterized in that After performing matching processing in the preset operation database according to the real-time operation data to obtain a first matching result, the method further includes: When it is determined according to the first matching result that there is no first operation path containing the real-time operation data in the preset operation database, data labeling processing is performed on a plurality of the first recommended data according to the preset operation rule to obtain the first target data.

5. The method according to claim 1, characterized in that Before performing data decision processing according to the acquired real-time operation data to obtain a plurality of first recommendation data, the method further includes: Determining whether process information of the operation process in which the real-time operation data is located is obtained; When it is determined that the process information has not been acquired, data decision processing is performed according to the acquired real-time operation data to obtain a plurality of the first recommendation data.

6. The method according to claim 5, characterized in that After determining whether the process information of the operation process in which the real-time operation data is located is obtained, the method further includes: In the case where it is determined that the process information is obtained, path selection processing is performed in the preset operation database according to the process information and the real-time operation data to obtain a second operation path; wherein the second operation path is the first operation path including the real-time operation data; According to the second operation path, flowchart data matching processing is performed in the preset operation database to obtain target flowchart data corresponding to the second operation path; wherein the preset flowchart data set includes flowchart data corresponding to all operation paths; A target operation process is determined according to the target flow chart data, wherein the target operation process at least includes target operation data, and the target operation data is the next operation data adjacent to the real-time operation data in the second operation path.

7. The method according to claim 1, characterized in that Before performing data decision processing according to the acquired second operation data to obtain a plurality of second recommendation data, the method further includes: Performing data detection processing on the second operation data to obtain a data detection result; When it is determined that the second operation data is a non-compliant operation according to the data detection result, a pop-up window displays preset warning information and preset suggestion information.

8. The method according to claim 7, characterized in that The performing data detection processing on the real-time operation data to obtain the data detection result comprises: Perform matching processing in the preset path library according to the second operation data to obtain a second matching result; wherein the preset path library includes all operation paths; When it is determined according to the second matching result that there is no operation path containing the second operation data in the preset path library, determining that the second operation data is a non-compliant operation; and / or, An operation frequency of the second operation data is obtained, and when the operation frequency is less than a preset frequency threshold, the second operation data is determined to be a non-compliant operation, wherein the operation frequency is the number of times the second operation data appears in all the operation paths.

9. A device for determining an operation process, characterized in that: include: A first decision unit is used to perform data decision processing according to the acquired real-time operation data to obtain a plurality of first recommended data; wherein the first recommended data is the next operation data adjacent to the real-time operation data in the operation path containing the real-time operation data; a first labeling unit, configured to perform data labeling processing from a plurality of the first recommended data according to the real-time operation data and a preset operation database to obtain first target data; wherein the preset operation database includes a plurality of first operation paths, the first operation paths are target operation paths corresponding to different operation scenarios obtained through reinforcement learning, and the first target data is determined by the first operation paths; a second decision unit, configured to perform data decision processing according to the acquired second operation data to obtain a plurality of second recommended data; wherein the second operation data is the next operation data acquired after the real-time operation data is acquired, and the second recommended data is the next operation data adjacent to the second operation data in the operation path including the second operation data; A second marking unit is used to perform data marking processing from multiple second recommended data according to the second operation data and the preset operation database to obtain second target data until responding to the operation completion instruction to end the determination of the operation process, wherein the second target data is determined by the first operation path.

10. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

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