Visualized acquiring and payment business process orchestration method and system

Abstract

The present invention relates to the field of financial technology, and is a visualization-based acquiring payment business process orchestration method and system, comprising: obtaining a business unit set identified with business functions, obtaining an initial business function set based on the business unit set and an initial sorting unit, obtaining scene recognition text, extracting a scene text sequence from the scene recognition text, obtaining a matching business unit sequence using the scene text sequence and the initial business function set, driving the matching business unit sequence, and if the matching business unit sequence is successfully driven, generating a first matching report using the matching business unit sequence; otherwise, obtaining multiple evaluation business unit sequences using the scene text sequence and the initial business function set, identifying a target business unit sequence based on the multiple evaluation business unit sequences, and generating a second matching report using the target business unit sequence. The present invention can improve the intelligence level of payment process orchestration.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a visualization-based acquiring and payment business process orchestration method and system. Background Art

[0002] With the penetration of mobile payment in multiple scenarios, various differentiated acquiring and payment needs have emerged. However, the traditional acquiring and payment business architecture cannot meet the flexible adaptation requirements of scenario-based businesses. Correspondingly, how to intelligently orchestrate the payment process has become an urgent problem that needs to be solved.

[0003] Currently, traditional payment process orchestration mostly uses manual coding to achieve the arrangement of payment processes.

[0004] Although the above methods can achieve the orchestration of payment processes, they suffer from low orchestration efficiency and poor operation and maintenance capabilities. Therefore, accurate and intelligent orchestration of payment processes has become an urgent problem to be solved. Summary of the Invention

[0005] The present invention provides a visualization-based acquiring payment business process arrangement method and a computer-readable storage medium, the main purpose of which is to improve the intelligence level of payment process arrangement.

[0006] To achieve the above objectives, the present invention provides a visualization-based acquiring payment business process orchestration method, comprising:

[0007] receiving a process orchestration instruction, and determining a process orchestration system based on the process orchestration instruction, wherein the process orchestration system includes a scenario recognition unit, an initial sorting unit, and a result feedback unit;

[0008] Obtaining a business unit set identified with a business function, wherein the business unit set includes multiple business units, and each of the multiple business units is identified with a business function, and obtaining an initial business function set based on the business unit set and the initial sorting unit, wherein the initial business function set includes multiple initial business function sequences, and the initial business function sequences correspond to the business functions in a one-to-one manner;

[0009] confirming receipt of a scene recognition instruction from a scene recognition unit, parsing the scene recognition instruction to obtain a scene recognition text, and extracting a scene text sequence from the scene recognition text, wherein the scene text sequence includes a plurality of scene keywords;

[0010] Obtaining a matching business unit sequence using the scene text sequence and the initial business function set, driving the matching business unit sequence, and if the matching business unit sequence is successfully driven, generating a first matching report using the matching business unit sequence;

[0011] Otherwise, using the scenario text sequence and the initial business function set to obtain multiple evaluation business unit sequences, determining a target business unit sequence based on the multiple evaluation business unit sequences, and generating a second matching report using the target business unit sequence;

[0012] The result feedback unit is used to send the first matching report or the second matching report to the initiator of the process arrangement instruction, thereby realizing the process arrangement of the payment service.

[0013] Optionally, the acquiring an initial service function set based on the service unit set and the initial sorting unit includes:

[0014] confirming receipt of an initial sorting instruction from an initial sorting unit, parsing the initial sorting instruction, and obtaining a process text set, wherein the process text set includes a plurality of process texts;

[0015] Perform the following operations on each process text in the process text set:

[0016] Obtaining a process keyword sequence using a pre-built language processing model, a pre-built corpus, and a process text, wherein the process keyword sequence includes a plurality of process keywords;

[0017] Summarizing the process keyword sequences to obtain multiple process keyword sequences, and using the corpus to merge the multiple process keyword sequences to obtain multiple initial function texts;

[0018] An initial business function set is obtained using multiple initial function texts and business unit sets.

[0019] Optionally, the obtaining of the initial business function set by using the multiple initial function texts and business unit sets includes:

[0020] For each of the multiple initial function texts, perform the following steps:

[0021] Count the number of initial function texts in multiple process keyword sequences to obtain statistical quantities, summarize the statistical quantities to obtain a statistical quantity set, and use the statistical quantity set to calculate a text statistical proportion set. The calculation formula is as follows:

[0022]

[0023] in, Indicates the percentage of text statistics. Text statistics ratio, Indicates the number of statistical concentrations A statistical number, Indicates that the total number of statistics is concentrated A statistical number, Indicates the number of statistical concentrations Statistical number;

[0024] A target statistical ratio set is extracted from the text statistical ratio set using a preset statistical ratio threshold, wherein the target statistical ratio set includes multiple target statistical ratios, and the target statistical ratio is greater than or equal to the statistical ratio threshold, and the following operation is performed on each of the multiple target statistical ratios:

[0025] updating the business functions corresponding to the business units in the business unit set according to the initial function text corresponding to the target statistical ratio to obtain an updated business unit set, wherein the updated business unit set includes a plurality of business units identified with updated business functions;

[0026] The business units in the updated business unit set are respectively aggregated according to the updated business functions to obtain a plurality of classified business unit sets, and the initial business function set is obtained using the plurality of classified business unit sets.

[0027] Optionally, the acquiring an initial service function set by using the multiple classified service unit sets includes:

[0028] Perform the following operations on each of the multiple classified business units:

[0029] Obtaining an average response time of the classified service units, calculating a unit evaluation value based on the average response time and a pre-constructed unit evaluation relational expression, summarizing the unit evaluation values ​​to obtain a unit evaluation value set, and sorting the unit evaluation values ​​in the unit evaluation value set in descending order to obtain a unit evaluation value sequence;

[0030] According to the classified business units, the bit order in the unit evaluation value sequence maps out the initial business function sequence.

[0031] Optionally, the unit evaluates the relation as follows:

[0032]

[0033] in, Represents the unit evaluation value, are all preset coefficients. Indicates the throughput of the classified service unit, represents the expected throughput of the classified service unit, Indicates the maximum response time of the classified business unit. Indicates the average response time of the classified business unit, Indicates the successful request rate of the classified business unit, Indicates the average fault diagnosis time of the classified business unit, Indicates the expanded score of the classified business unit.

[0034] Optionally, the acquiring a matching business unit sequence using the scene text sequence and the initial business function set includes:

[0035] Using the multiple initial function texts, the scene keywords in the scene text sequence are updated to obtain an updated scene keyword sequence;

[0036] Using the update scenario keyword in the update scenario keyword sequence, searching the initial business function set to obtain a retrieval business function set, wherein the retrieval business function set includes a plurality of retrieval business function sequences;

[0037] The following operations are performed on each of the multiple retrieval service function sequences:

[0038] The first retrieval business unit in the retrieval business function sequence is extracted, the first retrieval business unit is aggregated to obtain a retrieval business unit set, and a matching business unit sequence is obtained according to the update scenario keyword sequence and the retrieval business unit set.

[0039] Optionally, the acquiring of multiple evaluation business unit sequences using the scene text sequence and the initial business function set includes:

[0040] The following operations are performed for each retrieval business function sequence in the business function set:

[0041] Using a preset extraction value, extracting a matching business function sequence from the search business function sequence, and summarizing the matching business function sequences to obtain a plurality of matching business function sequences;

[0042] The number of each scene text in the scene text sequence is counted to obtain a matching number group, where the matching number group is as follows:

[0043]

[0044] in, Indicates the number of matching groups, Respectively represent the first matching quantity in the matching quantity group and the second matching quantity in the matching quantity group. Indicates the total number of matching groups Number of matches;

[0045] Perform the following operations on the matching quantities in the matching quantity group:

[0046] In a combined form, a matching business function group set is obtained using the matching quantity group and the matching business function sequence corresponding to the matching quantity, and the matching business function group sets are summarized to obtain multiple matching business function group sets. In an arranged form, multiple matching business function group sets and scene text sequences are used to obtain multiple evaluation business unit sequences.

[0047] Optionally, determining a target business unit sequence based on a plurality of evaluated business unit sequences includes:

[0048] The following operations are performed on each of the multiple evaluation business unit sequences:

[0049] Obtain the evaluation factor set for the evaluation business unit sequence and use the evaluation factor set to calculate the evaluation reference value. The calculation formula is as follows:

[0050]

[0051] in, Indicates the evaluation reference value, Indicates the preset coefficients, Indicates the first evaluation factors, Indicates that the evaluation factor set has evaluation factors;

[0052] The evaluation reference values ​​are aggregated to obtain an evaluation reference value set, and the target business unit sequence is obtained using the evaluation reference value set.

[0053] Optionally, the acquiring a target business unit sequence by using the evaluation reference value set includes:

[0054] Sorting the evaluation reference values ​​in the evaluation reference value set in descending order to obtain an evaluation reference value sequence, and using a preset evaluation extraction value to identify an extraction reference value sequence in the evaluation reference value sequence;

[0055] Acquire multiple reference alignment sequences based on the extracted reference value sequence, wherein the reference alignment sequences correspond to the evaluation factors one-to-one, and the position order of the reference alignment values ​​in the reference alignment sequence is the same as the position order of the evaluation reference values ​​in the extracted reference value sequence;

[0056] If the first reference comparison value in each of the multiple reference comparison sequences is the maximum value, the evaluation business unit sequence corresponding to the first extracted reference value in the extracted reference value sequence is taken as the target business unit sequence.

[0057] To achieve the above objectives, the present invention further provides a visualization-based acquiring and payment business process orchestration system, comprising:

[0058] A business unit acquisition module, configured to receive a process orchestration instruction and identify a process orchestration system based on the process orchestration instruction, wherein the process orchestration system includes a scenario recognition unit, an initial sorting unit, and a result feedback unit;

[0059] Obtaining a business unit set identified with a business function, wherein the business unit set includes multiple business units, and each of the multiple business units is identified with a business function, and obtaining an initial business function set based on the business unit set and the initial sorting unit, wherein the initial business function set includes multiple initial business function sequences, and the initial business function sequences correspond to the business functions in a one-to-one manner;

[0060] A business scene recognition module is used to confirm receipt of a scene recognition instruction from the scene recognition unit, parse the scene recognition instruction to obtain a scene recognition text, and extract a scene text sequence from the scene recognition text, wherein the scene text sequence includes a plurality of scene keywords;

[0061] a business unit arrangement module, configured to obtain a matching business unit sequence using the scenario text sequence and the initial business function set, drive the matching business unit sequence, and generate a first matching report using the matching business unit sequence if the matching business unit sequence is successfully driven;

[0062] Otherwise, using the scenario text sequence and the initial business function set to obtain multiple evaluation business unit sequences, determining a target business unit sequence based on the multiple evaluation business unit sequences, and generating a second matching report using the target business unit sequence;

[0063] The orchestration result feedback module is used to use the result feedback unit to send the first matching report or the second matching report to the initiator of the process orchestration instruction to realize the process orchestration of the payment service.

[0064] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0065] A memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned visualization-based acquiring payment business process orchestration method.

[0066] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned visualization-based acquiring payment business process orchestration method.

[0067] The present invention is to solve the problem described in the background technology. The present invention obtains a business unit set marked with business functions, wherein the business unit set includes multiple business units, and each of the multiple business units is marked with a business function, and an initial business function set is obtained based on the business unit set and the initial sorting unit, wherein the initial business function set includes multiple initial business function sequences, and the initial business function sequences correspond to the business functions one by one. It can be seen that the present invention identifies the business units in combination with the text used in actual situations, so as to facilitate the division and classification of business units, and screens the business units, screens out frequently used business units, and evaluates the frequently used business units, so as to clarify the theoretical usage order of different business units under the same function, laying the foundation for the subsequent arrangement of payment services, confirming the reception of the scene recognition instruction from the scene recognition unit, parsing the scene recognition instruction, obtaining the scene recognition text, and extracting the scene text sequence from the scene recognition text, wherein the scene text sequence includes multiple scene keywords, which can be The present invention combines specific usage scenarios. By analyzing the usage scenarios, a representation text for arranging different business units is obtained. The scenario text sequence and the initial business function set are used to obtain a matching business unit sequence, and the matching business unit sequence is driven. If the matching business unit sequence is successfully driven, a first matching report is generated using the matching business unit sequence. Otherwise, multiple evaluation business unit sequences are obtained using the scenario text sequence and the initial business function set. A target business unit sequence is identified based on the multiple evaluation business unit sequences, and a second matching report is generated using the target business unit sequence. It can be seen that the present invention takes into account that in theory, the arrangement of business units may not meet the compatibility requirements. Therefore, business units with different functions are screened and multiple evaluation business unit sequences are obtained in a combined form. The evaluation business unit sequences are evaluated. After confirming that the optimal evaluation business unit sequence is optimal in all dimensions, a target business unit sequence is obtained. The business units used are clearly identified in the form of a generated report, thereby improving the intelligence of the present invention. Therefore, the present invention can improve the intelligence of payment process arrangement. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flow chart of a visualization-based acquiring and payment business process orchestration method provided in one embodiment of the present invention;

[0069] Figure 2 A functional module diagram of a visualization-based acquiring and payment business process orchestration system provided in one embodiment of the present invention;

[0070] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0071] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0072] The embodiments of the present application provide a visualization-based acquiring and payment business process orchestration method. The execution entity of the visualization-based acquiring and payment business process orchestration method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the visualization-based acquiring and payment business process orchestration method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0073] Reference Figure 1 FIG2 is a flow chart of a visualization-based acquiring payment business process orchestration method provided by an embodiment of the present invention. In this embodiment, the visualization-based acquiring payment business process orchestration method includes:

[0074] S1. Receive a process orchestration instruction, and identify a process orchestration system based on the process orchestration instruction, wherein the process orchestration system includes a scenario recognition unit, an initial sorting unit, and a result feedback unit.

[0075] It should be explained that the process orchestration instructions are instructions issued by software testers or software developers to implement testing of software in different application scenarios. In the embodiment of the present invention, the software refers to software composed of units with different business functions. The process orchestration system refers to a small program or APP that can match different software according to different application scenarios, and the process orchestration system includes a scenario recognition unit, an initial sorting unit and a result feedback unit. For the specific application of the units, please refer to the subsequent embodiments.

[0076] For example, in order to develop a software for application in a specific scenario, the software developer issues the process orchestration instruction and confirms the process orchestration system.

[0077] S2. Obtain a business unit set identified with business functions, wherein the business unit set includes multiple business units, and each of the multiple business units is identified with a business function. Based on the business unit set and the initial sorting unit, obtain an initial business function set, wherein the initial business function set includes multiple initial business function sequences, and the initial business function sequences correspond to the business functions one-to-one.

[0078] It should be explained that a business unit is a unit or node that can perform a specific function. A business function refers to the function that a business unit can perform. For example, payment and feedback. For example, if a code is compiled to generate a random number between 1 and 10, then this code identifies the business unit that generates the random number.

[0079] It is understandable that the obtaining of the initial service function set based on the service unit set and the initial sorting unit includes:

[0080] confirming receipt of an initial sorting instruction from an initial sorting unit, parsing the initial sorting instruction, and obtaining a process text set, wherein the process text set includes a plurality of process texts;

[0081] Perform the following operations on each process text in the process text set:

[0082] Obtaining a process keyword sequence using a pre-built language processing model, a pre-built corpus, and a process text, wherein the process keyword sequence includes a plurality of process keywords;

[0083] Summarizing the process keyword sequences to obtain multiple process keyword sequences, and using the corpus to merge the multiple process keyword sequences to obtain multiple initial function texts;

[0084] An initial business function set is obtained using multiple initial function texts and business unit sets.

[0085] It should be understood that the process text refers to the text used to describe the implementation process of a certain business when implementing a certain business. The process text set can be obtained in an artificially set manner. The language processing model refers to a model that can screen or eliminate texts. Optionally, a natural language processing model is used as the language processing model. The same effect can be achieved by using other technologies, which will not be described here. The corpus refers to a database that stores texts used to describe different business functions. Optionally, the corpus is obtained in an artificially formulated manner. The same effect can be achieved by using other technologies, which will not be described here. The process keyword sequence refers to the sequence of texts extracted according to the process text for implementing different functions. Process keywords are keywords used to describe different business functions.

[0086] For example, the process text is: when accumulating points, you need to make a payment first. After the amount paid is converted into corresponding points, the points are accumulated on the original points. Using the language processing model and corpus, the three process keywords: payment, points conversion, and points accumulation can be extracted from the process text. After sorting the process keywords in the order of their appearance in the process text from first to last, a process keyword sequence is obtained, where the process keyword sequence is: payment, points conversion, and points accumulation.

[0087] Furthermore, different process texts may contain different keywords for describing functions. Therefore, multiple process keyword sequences can be obtained by processing the process texts in the process text set. Using a corpus to merge multiple process keyword sequences means updating the process keywords in the process keyword sequence to words with the same meaning. For example, the accumulation of points can be expressed as statistics of points, totals of points, etc. Here, accumulation, statistics, and totals are merged and can be merged into: accumulation, that is, statistics and totals can be expressed as accumulation. Using multiple initial function texts to update multiple process keyword sequences means updating process keywords with the same meaning to the same process keyword.

[0088] It should be explained that the use of multiple initial function texts and business unit sets to obtain the initial business function set includes:

[0089] For each of the multiple initial function texts, perform the following steps:

[0090] Count the number of initial function texts in multiple process keyword sequences to obtain statistical quantities, summarize the statistical quantities to obtain a statistical quantity set, and use the statistical quantity set to calculate a text statistical proportion set. The calculation formula is as follows:

[0091]

[0092] in, Indicates the percentage of text statistics. Text statistics ratio, Indicates the number of statistical concentrations A statistical number, Indicates that the total number of statistics is concentrated A statistical number, Indicates the number of statistical concentrations Statistical number;

[0093] A target statistical ratio set is extracted from the text statistical ratio set using a preset statistical ratio threshold, wherein the target statistical ratio set includes multiple target statistical ratios, and the target statistical ratio is greater than or equal to the statistical ratio threshold, and the following operation is performed on each of the multiple target statistical ratios:

[0094] updating the business functions corresponding to the business units in the business unit set according to the initial function text corresponding to the target statistical ratio to obtain an updated business unit set, wherein the updated business unit set includes a plurality of business units identified with updated business functions;

[0095] The business units in the updated business unit set are respectively aggregated according to the updated business functions to obtain a plurality of classified business unit sets, and the initial business function set is obtained using the plurality of classified business unit sets.

[0096] It can be understood that the updating of the business functions corresponding to the business units in the business unit set according to the initial function text corresponding to the target statistical ratio refers to using the language processing model and the initial function text to identify business functions that are identical in meaning to the initial function text among multiple business functions, and updating the business functions to the initial function text. Here, the initial function text used for updating is the updated business function.

[0097] It should be explained that the step of obtaining the initial service function set by using the multiple classified service unit sets includes:

[0098] Perform the following operations on each of the multiple classified business units:

[0099] Obtaining an average response time of the classified service units, calculating a unit evaluation value based on the average response time and a pre-constructed unit evaluation relational expression, summarizing the unit evaluation values ​​to obtain a unit evaluation value set, and sorting the unit evaluation values ​​in the unit evaluation value set in descending order to obtain a unit evaluation value sequence;

[0100] According to the classified business units, the bit order in the unit evaluation value sequence maps out the initial business function sequence.

[0101] It is understandable that the average response time refers to the average time required for the business classification unit to process a request. Generally speaking, there is a one-to-one correspondence between the classification business unit and the unit evaluation value. Therefore, the initial business function sequence can be obtained according to the position of the unit evaluation value corresponding to the classification business unit in the unit evaluation value sequence. For example, there are 3 classification business units, and the unit evaluation values ​​corresponding to the 3 classification business units are 3, 5, and 2, that is, the unit evaluation value corresponding to the first classification business unit is 3, the unit evaluation value corresponding to the second classification business unit is 5, and the unit evaluation value corresponding to the third classification business unit is 2. The unit evaluation value sequence obtained using the unit evaluation values ​​corresponding to the classification business units is 5, 3, and 2. The initial business function sequence obtained according to the unit evaluation value sequence is: the second classification business unit, the first classification business unit, and the third classification business unit.

[0102] Furthermore, the unit evaluates the relationship as follows:

[0103]

[0104] in, Represents the unit evaluation value, are all preset coefficients. Indicates the throughput of the classified service unit, represents the expected throughput of the classified service unit, Indicates the maximum response time of the classified business unit. Indicates the average response time of the classified business unit, Indicates the successful request rate of the classified business unit, Indicates the average fault diagnosis time of the classified business unit, Indicates the expanded score of the classified business unit.

[0105] It should be explained that the expected throughput refers to the throughput of the classified business unit under theoretical circumstances, the maximum response time refers to the maximum time that is pre-set for the classified business unit to respond, the successful request rate refers to the probability that the classified business unit successfully processes a request, and the average fault diagnosis time refers to the average time required to diagnose the classified business unit when a fault occurs in the classified business unit. The extension score is a score used to evaluate whether the classified business unit supports expansion. Optionally, the extension score shown is obtained in an artificially set form. For example, when the classified business unit cannot be expanded, the extension score is set to 0. When the classified business unit only supports manual expansion, the extension score is set to 0.5. When the classified business unit supports dynamic expansion, the extension score is set to 1. Optionally, the hierarchical analysis method is used to evaluate the classified business unit to obtain the coefficients in the unit evaluation relationship. The same effect can be achieved by using other technologies, which will not be repeated here.

[0106] S3. Confirm receipt of the scene recognition instruction from the scene recognition unit, parse the scene recognition instruction to obtain a scene recognition text, and extract a scene text sequence from the scene recognition text, wherein the scene text sequence includes a plurality of scene keywords.

[0107] It should be noted that the scene recognition text is used to describe the desired software functionality. Optionally, the scene recognition text is input by the software developer into the process orchestration system. The method for extracting scene text sequences from the scene recognition text is the same as the method for obtaining process keywords from process text, and will not be further elaborated here.

[0108] S4. Obtain a matching business unit sequence using the scene text sequence and the initial business function set, drive the matching business unit sequence, and if the matching business unit sequence is successfully driven, generate a first matching report using the matching business unit sequence.

[0109] It is understandable that the method of obtaining a matching business unit sequence using the scene text sequence and the initial business function set includes:

[0110] Using the multiple initial function texts, the scene keywords in the scene text sequence are updated to obtain an updated scene keyword sequence;

[0111] Using the update scenario keyword in the update scenario keyword sequence, searching the initial business function set to obtain a retrieval business function set, wherein the retrieval business function set includes a plurality of retrieval business function sequences;

[0112] The following operations are performed on each of the multiple retrieval service function sequences:

[0113] The first retrieval business unit in the retrieval business function sequence is extracted, the first retrieval business unit is aggregated to obtain a retrieval business unit set, and a matching business unit sequence is obtained according to the update scenario keyword sequence and the retrieval business unit set.

[0114] Furthermore, the method of updating the scenario keywords in the scenario text sequence using multiple initial function texts is the same as the method of updating the business functions corresponding to the business units in the business unit set according to the initial function text corresponding to the target statistical ratio, and will not be repeated here. The purpose of obtaining the updated scenario keyword sequence is to unify the names of the business functions corresponding to the business units. The initial function text corresponding to the retrieval business function sequence is the same as the updated scenario keyword, that is, using the updated scenario keyword in the updated scenario keyword sequence, retrieving the retrieval business function set in the initial business function set means retrieving the initial business function sequence that is the same as the updated scenario keyword in the initial business function set. The method of obtaining the matching business unit sequence based on the updated scenario keyword sequence and the retrieval business unit set is the same as the method of mapping the initial business function sequence based on the position sequence in the unit evaluation value sequence according to the classified business units, and will not be repeated here. Generally speaking, the obtained matching business unit sequence brings together the best business units among different business functions. Therefore, the obtained matching business unit sequence can be considered to be the unit for executing the scenario described by the scenario recognition text under ideal conditions.

[0115] It should be noted that the matching business units in the matching business unit sequence obtained using the optimal business unit may not be compatible during execution, and therefore, the matching business unit sequence may fail to execute successfully. When the matching business unit sequence can be successfully executed, the positional order of the matching business units in the matching business unit sequence in different initial business function sequences is extracted, and the positional order of the matching business units in the corresponding initial business function sequences is identified in the matching business unit sequence to obtain a first matching report.

[0116] S5. Otherwise, use the scenario text sequence and the initial business function set to obtain multiple evaluation business unit sequences, confirm a target business unit sequence based on the multiple evaluation business unit sequences, and generate a second matching report using the target business unit sequence.

[0117] It should be explained that the method of obtaining multiple evaluation business unit sequences using the scene text sequence and the initial business function set includes:

[0118] The following operations are performed for each retrieval business function sequence in the business function set:

[0119] Using a preset extraction value, extracting a matching business function sequence from the search business function sequence, and summarizing the matching business function sequences to obtain a plurality of matching business function sequences;

[0120] The number of each scene text in the scene text sequence is counted to obtain a matching number group, where the matching number group is as follows:

[0121]

[0122] in, Indicates the number of matching groups, Respectively represent the first matching quantity in the matching quantity group and the second matching quantity in the matching quantity group. Indicates the total number of matching groups Number of matches;

[0123] Perform the following operations on the matching quantities in the matching quantity group:

[0124] In a combined form, a matching business function group set is obtained using the matching quantity group and the matching business function sequence corresponding to the matching quantity, and the matching business function group sets are summarized to obtain multiple matching business function group sets. In an arranged form, multiple matching business function group sets and scene text sequences are used to obtain multiple evaluation business unit sequences.

[0125] Furthermore, matching the service function sequence refers to sequentially extracting the search service function units that have the same extraction value from the search service function sequence. For example, if the search service function sequence includes service unit A, service unit B, service unit C, and service unit D, and the preset extraction value is 3, then the matching service function sequence to be extracted from the search service function sequence using the extraction value is: service unit A, service unit B, and service unit C.

[0126] It can be understood that the matching number represents the number of business units of the same type required to meet the scenario expressed by the scenario recognition text. After first extracting matching business function groups with the same matching number from different matching business function sequences, considering the different compatibilities that different matching business function units can adapt to, the different positions of matching business function units of the same function during implementation are obtained through permutation. After confirming that the sorted matching business function units are executable, the evaluation business unit sequence is obtained.

[0127] It should be understood that the identification of the target business unit sequence based on multiple evaluation business unit sequences includes:

[0128] The following operations are performed on each of the multiple evaluation business unit sequences:

[0129] Obtain the evaluation factor set for the evaluation business unit sequence and use the evaluation factor set to calculate the evaluation reference value. The calculation formula is as follows:

[0130]

[0131] in, Indicates the evaluation reference value, Indicates the preset coefficients, Indicates the first evaluation factors, Indicates that the evaluation factor set has evaluation factors;

[0132] The evaluation reference values ​​are aggregated to obtain an evaluation reference value set, and the target business unit sequence is obtained using the evaluation reference value set.

[0133] It should be explained that evaluation factors refer to indicators used to evaluate business unit sequences, such as energy consumption, response time, throughput, concurrency, network bandwidth, and encryption strength.

[0134] Furthermore, the obtaining of a target business unit sequence using the evaluation reference value set includes:

[0135] Sorting the evaluation reference values ​​in the evaluation reference value set in descending order to obtain an evaluation reference value sequence, and using a preset evaluation extraction value to identify an extraction reference value sequence in the evaluation reference value sequence;

[0136] Acquire multiple reference alignment sequences based on the extracted reference value sequence, wherein the reference alignment sequences correspond to the evaluation factors one-to-one, and the position order of the reference alignment values ​​in the reference alignment sequence is the same as the position order of the evaluation reference values ​​in the extracted reference value sequence;

[0137] If the first reference comparison value in each of the multiple reference comparison sequences is the maximum value, the evaluation business unit sequence corresponding to the first extracted reference value in the extracted reference value sequence is taken as the target business unit sequence.

[0138] It should be explained that the method of obtaining the reference value sequence is the same as the method of obtaining the matching business function sequence, which will not be repeated here. Generally speaking, each evaluation business unit sequence corresponds to an evaluation reference value, and each evaluation reference value corresponds to an evaluation factor set. Therefore, different reference comparison sequences can be obtained using different evaluation factors. For example, there are three evaluation factor sets, of which the first evaluation factor set is: 3, 4, 5, the second evaluation factor set is 4, 7, 1, and the third evaluation factor set is: 1, 6, 3, and the evaluation reference value corresponding to the first evaluation factor set is greater than the evaluation reference value corresponding to the second evaluation factor set, which is greater than the evaluation reference value corresponding to the third evaluation factor set. Therefore, using these three evaluation sets, three reference comparison sequences can be obtained, and the three reference comparison sequences are {3, 4, 1}, {4, 7, 6} and {5, 1, 3} respectively. When the first reference comparison value in each reference comparison sequence in multiple reference comparison sequences is the maximum value, it means that the first evaluation business unit sequence is superior to other evaluation business unit sequences in all dimensions. The method for obtaining the second matching report is the same as the method for obtaining the first matching report, and will not be repeated here.

[0139] S6. Utilize the result feedback unit to send the first matching report or the second matching report to the initiator of the process orchestration instruction, thereby implementing process orchestration for the payment service.

[0140] The present invention is to solve the problem described in the background technology. The present invention obtains a business unit set marked with business functions, wherein the business unit set includes multiple business units, and each of the multiple business units is marked with a business function, and an initial business function set is obtained based on the business unit set and the initial sorting unit, wherein the initial business function set includes multiple initial business function sequences, and the initial business function sequences correspond to the business functions one by one. It can be seen that the present invention identifies the business units in combination with the text used in actual situations, so as to facilitate the division and classification of business units, and screens the business units, screens out frequently used business units, and evaluates the frequently used business units, so as to clarify the theoretical usage order of different business units under the same function, laying the foundation for the subsequent arrangement of payment services, confirming the reception of the scene recognition instruction from the scene recognition unit, parsing the scene recognition instruction, obtaining the scene recognition text, and extracting the scene text sequence from the scene recognition text, wherein the scene text sequence includes multiple scene keywords, which can be The present invention combines specific usage scenarios. By analyzing the usage scenarios, a representation text for arranging different business units is obtained. The scenario text sequence and the initial business function set are used to obtain a matching business unit sequence, and the matching business unit sequence is driven. If the matching business unit sequence is successfully driven, a first matching report is generated using the matching business unit sequence. Otherwise, multiple evaluation business unit sequences are obtained using the scenario text sequence and the initial business function set. A target business unit sequence is identified based on the multiple evaluation business unit sequences, and a second matching report is generated using the target business unit sequence. It can be seen that the present invention takes into account that in theory, the arrangement of business units may not meet the compatibility requirements. Therefore, business units with different functions are screened and multiple evaluation business unit sequences are obtained in a combined form. The evaluation business unit sequences are evaluated. After confirming that the optimal evaluation business unit sequence is optimal in all dimensions, a target business unit sequence is obtained. The business units used are clearly identified in the form of a generated report, thereby improving the intelligence of the present invention. Therefore, the present invention can improve the intelligence of payment process arrangement.

[0141] like Figure 2 , which is a functional module diagram of a visualization-based acquiring and payment business process orchestration system provided by an embodiment of the present invention.

[0142] The visualization-based acquiring and payment business process orchestration system 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the visualization-based acquiring and payment business process orchestration system 100 may include a business unit acquisition module 101, a business scenario recognition module 102, a business unit orchestration module 103, and an orchestration result feedback module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0143] The business unit acquisition module 101 is configured to receive a process orchestration instruction and identify a process orchestration system based on the process orchestration instruction, wherein the process orchestration system includes a scenario recognition unit, an initial sorting unit, and a result feedback unit;

[0144] Obtaining a business unit set identified with a business function, wherein the business unit set includes multiple business units, and each of the multiple business units is identified with a business function, and obtaining an initial business function set based on the business unit set and the initial sorting unit, wherein the initial business function set includes multiple initial business function sequences, and the initial business function sequences correspond to the business functions in a one-to-one manner;

[0145] The business scenario recognition module 102 is configured to confirm receipt of a scenario recognition instruction from the scenario recognition unit, parse the scenario recognition instruction to obtain a scenario recognition text, and extract a scenario text sequence from the scenario recognition text, wherein the scenario text sequence includes a plurality of scenario keywords;

[0146] The business unit arrangement module 103 is configured to obtain a matching business unit sequence using the scene text sequence and the initial business function set, drive the matching business unit sequence, and generate a first matching report using the matching business unit sequence if the matching business unit sequence is successfully driven;

[0147] Otherwise, using the scenario text sequence and the initial business function set to obtain multiple evaluation business unit sequences, determining a target business unit sequence based on the multiple evaluation business unit sequences, and generating a second matching report using the target business unit sequence;

[0148] The orchestration result feedback module 104 is configured to utilize the result feedback unit to send the first matching report or the second matching report to the initiator of the process orchestration instruction, thereby implementing process orchestration for the payment service.

[0149] In detail, the modules in the visualization-based acquiring payment business process orchestration system 100 in the embodiment of the present invention are used in the same manner as above. Figure 1The same technical means are used as the visualization-based acquiring payment business process orchestration method described in , and can produce the same technical effects, so I will not go into details here.

[0150] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A visualization-based acquiring payment business process arrangement method, characterized in that: The method comprises: receiving a process orchestration instruction, and determining a process orchestration system based on the process orchestration instruction, wherein the process orchestration system includes a scenario recognition unit, an initial sorting unit, and a result feedback unit; Acquire a business unit set identified with a business function, wherein the business unit set includes multiple business units, and each of the multiple business units is identified with a business function; confirming receipt of an initial sorting instruction from an initial sorting unit, parsing the initial sorting instruction, and obtaining a process text set, wherein the process text set includes a plurality of process texts; Acquire multiple process keyword sequences using a pre-built language processing model, a pre-built corpus, and multiple process texts, wherein the process keyword sequences include multiple process keywords; Using the corpus, a plurality of process keyword sequences are merged to obtain a plurality of initial function texts; Counting the number of each of the multiple initial function texts in the multiple process keyword sequences to obtain a statistical quantity set, and using the statistical quantity set to calculate a text statistical proportion set; A target statistical ratio set is extracted from the text statistical ratio set using a preset statistical ratio threshold, wherein the target statistical ratio set includes multiple target statistical ratios, and the target statistical ratio is greater than or equal to the statistical ratio threshold, and the following operation is performed on each of the multiple target statistical ratios: updating the business functions corresponding to the business units in the business unit set according to the initial function text corresponding to the target statistical ratio to obtain an updated business unit set, wherein the updated business unit set includes a plurality of business units identified with updated business functions; The business units in the updated business unit set are aggregated according to the updated business functions to obtain a plurality of classified business unit sets, and an initial business function set is obtained using the plurality of classified business unit sets, wherein the initial business function set includes a plurality of initial business function sequences, and the initial business function sequences correspond to the business functions in a one-to-one manner; confirming receipt of a scene recognition instruction from a scene recognition unit, parsing the scene recognition instruction to obtain a scene recognition text, and extracting a scene text sequence from the scene recognition text, wherein the scene text sequence includes a plurality of scene keywords; Obtaining a matching business unit sequence using the scene text sequence and the initial business function set, driving the matching business unit sequence, and if the matching business unit sequence is successfully driven, generating a first matching report using the matching business unit sequence; Otherwise, using the scenario text sequence and the initial business function set to obtain multiple evaluation business unit sequences, determining a target business unit sequence based on the multiple evaluation business unit sequences, and generating a second matching report using the target business unit sequence; The result feedback unit is used to send the first matching report or the second matching report to the initiator of the process arrangement instruction, thereby realizing the process arrangement of the payment service.

2. The visualization-based acquiring payment business process arrangement method according to claim 1, characterized in that: The statistical quantity set is used to calculate the text statistical proportion set, and the calculation formula is as follows: in, Indicates the percentage of text statistics. Text statistics ratio, Indicates the number of statistical concentrations A statistical number, Indicates that the total number of statistics is concentrated A statistical number, Indicates the number of statistical concentrations A statistical number.

3. The visualization-based acquiring payment business process arrangement method according to claim 2, characterized in that: The obtaining of an initial service function set by using the multiple classified service unit sets includes: Perform the following operations on each of the multiple classified business units: Obtaining an average response time of the classified service units, calculating a unit evaluation value based on the average response time and a pre-constructed unit evaluation relational expression, summarizing the unit evaluation values ​​to obtain a unit evaluation value set, and sorting the unit evaluation values ​​in the unit evaluation value set in descending order to obtain a unit evaluation value sequence; According to the classified business units, the bit order in the unit evaluation value sequence maps out the initial business function sequence.

4. The visualization-based acquiring payment business process arrangement method according to claim 3, characterized in that: The unit evaluates the relation as follows: in, Represents the unit evaluation value, are all preset coefficients. Indicates the throughput of the classified service unit, represents the expected throughput of the classified service unit, Indicates the maximum response time of the classified business unit. Indicates the average response time of the classified business unit, Indicates the successful request rate of the classified business unit, Indicates the average fault diagnosis time of the classified business unit, Indicates the expanded score of the classified business unit.

5. The visualization-based acquiring payment business process arrangement method according to claim 4, characterized in that: The obtaining of a matching business unit sequence by using the scene text sequence and the initial business function set includes: Using the multiple initial function texts, the scene keywords in the scene text sequence are updated to obtain an updated scene keyword sequence; Using the update scenario keyword in the update scenario keyword sequence, searching the initial business function set to obtain a retrieval business function set, wherein the retrieval business function set includes a plurality of retrieval business function sequences; The following operations are performed on each of the multiple retrieval service function sequences: The first retrieval service unit in the retrieval service function sequence is extracted, the first retrieval service unit is aggregated to obtain a retrieval service unit set, and a matching service unit sequence is obtained according to the update scenario keyword sequence and the retrieval service unit set.

6. The visualization-based acquiring payment business process arrangement method according to claim 5, characterized in that: The method of obtaining multiple evaluation business unit sequences by utilizing the scene text sequence and the initial business function set includes: The following operations are performed for each retrieval business function sequence in the business function set: Using a preset extraction value, extracting a matching business function sequence from the search business function sequence, and summarizing the matching business function sequences to obtain a plurality of matching business function sequences; The number of each scene text in the scene text sequence is counted to obtain a matching number group, where the matching number group is as follows: in, Indicates the number of matching groups. Respectively represent the first matching quantity in the matching quantity group and the second matching quantity in the matching quantity group. Indicates the total number of matches in the group Number of matches; Perform the following operations on the matching quantities in the matching quantity group: In a combined form, a matching business function group set is obtained using the matching quantity group and the matching business function sequence corresponding to the matching quantity, and the matching business function group sets are summarized to obtain multiple matching business function group sets. In an arranged form, multiple matching business function group sets and scene text sequences are used to obtain multiple evaluation business unit sequences.

7. The visualization-based acquiring payment business process arrangement method according to claim 6, characterized in that: The step of determining a target business unit sequence based on a plurality of evaluated business unit sequences includes: The following operations are performed on each of the multiple evaluation business unit sequences: Obtain the evaluation factor set for the evaluation business unit sequence and use the evaluation factor set to calculate the evaluation reference value. The calculation formula is as follows: in, Indicates the evaluation reference value, Indicates the preset coefficients, Indicates the first evaluation factors, Indicates that the evaluation factor set has evaluation factors; The evaluation reference values ​​are aggregated to obtain an evaluation reference value set, and the target business unit sequence is obtained using the evaluation reference value set.

8. The visualization-based acquiring payment business process arrangement method according to claim 7, characterized in that: The obtaining of a target business unit sequence by using the evaluation reference value set includes: Sorting the evaluation reference values ​​in the evaluation reference value set in descending order to obtain an evaluation reference value sequence, and using a preset evaluation extraction value to identify an extraction reference value sequence in the evaluation reference value sequence; Acquire multiple reference alignment sequences based on the extracted reference value sequence, wherein the reference alignment sequences correspond to the evaluation factors one-to-one, and the position order of the reference alignment values ​​in the reference alignment sequence is the same as the position order of the evaluation reference values ​​in the extracted reference value sequence; If the first reference comparison value in each of the multiple reference comparison sequences is the maximum value, the evaluation business unit sequence corresponding to the first extracted reference value in the extracted reference value sequence is taken as the target business unit sequence.

9. A visualization-based acquiring and payment business process orchestration system, characterized by: The system comprises: A business unit acquisition module, configured to receive a process orchestration instruction and identify a process orchestration system based on the process orchestration instruction, wherein the process orchestration system includes a scenario recognition unit, an initial sorting unit, and a result feedback unit; Acquire a business unit set identified with a business function, wherein the business unit set includes multiple business units, and each of the multiple business units is identified with a business function; confirming receipt of an initial sorting instruction from an initial sorting unit, parsing the initial sorting instruction, and obtaining a process text set, wherein the process text set includes a plurality of process texts; Acquire multiple process keyword sequences using a pre-built language processing model, a pre-built corpus, and multiple process texts, wherein the process keyword sequences include multiple process keywords; Using the corpus, a plurality of process keyword sequences are merged to obtain a plurality of initial function texts; Counting the number of each of the multiple initial function texts in the multiple process keyword sequences to obtain a statistical quantity set, and using the statistical quantity set to calculate a text statistical proportion set; A target statistical ratio set is extracted from the text statistical ratio set using a preset statistical ratio threshold, wherein the target statistical ratio set includes multiple target statistical ratios, and the target statistical ratio is greater than or equal to the statistical ratio threshold, and the following operation is performed on each of the multiple target statistical ratios: updating the business functions corresponding to the business units in the business unit set according to the initial function text corresponding to the target statistical ratio to obtain an updated business unit set, wherein the updated business unit set includes a plurality of business units identified with updated business functions; The business units in the updated business unit set are aggregated according to the updated business functions to obtain a plurality of classified business unit sets, and an initial business function set is obtained using the plurality of classified business unit sets, wherein the initial business function set includes a plurality of initial business function sequences, and the initial business function sequences correspond to the business functions in a one-to-one manner; A business scene recognition module is used to confirm receipt of a scene recognition instruction from the scene recognition unit, parse the scene recognition instruction to obtain a scene recognition text, and extract a scene text sequence from the scene recognition text, wherein the scene text sequence includes a plurality of scene keywords; a business unit arrangement module, configured to obtain a matching business unit sequence using the scenario text sequence and the initial business function set, drive the matching business unit sequence, and generate a first matching report using the matching business unit sequence if the matching business unit sequence is successfully driven; Otherwise, using the scenario text sequence and the initial business function set to obtain multiple evaluation business unit sequences, determining a target business unit sequence based on the multiple evaluation business unit sequences, and generating a second matching report using the target business unit sequence; The orchestration result feedback module is used to use the result feedback unit to send the first matching report or the second matching report to the initiator of the process orchestration instruction to realize the process orchestration of the payment service.

Images