Disclosure of Invention
The invention provides a visual order-receiving payment business process arrangement method and a computer-readable storage medium, which mainly aim at improving the intelligent degree of payment process arrangement.
In order to achieve the above purpose, the present invention provides a method for arranging a pay-per-view business process based on visualization, which comprises:
Receiving a flow programming instruction, and confirming a flow programming system based on the flow programming instruction, wherein the flow programming system comprises a scene recognition unit, an initial ordering unit and a result feedback unit;
Acquiring a service unit set with service functions, wherein the service unit set comprises a plurality of service units, each service unit in the plurality of service units is marked with a service function, and acquiring an initial service function set based on the service unit set and an initial sequencing unit, wherein the initial service function set comprises a plurality of initial service function sequences, and the initial service function sequences are in one-to-one correspondence with the service functions;
Confirming receiving a scene recognition instruction from a scene recognition unit, analyzing the scene recognition instruction to obtain a scene recognition text, and extracting a scene Jing Wenben sequence from the scene recognition text, wherein the scene text sequence comprises a plurality of scene keywords;
Acquiring a matching service unit sequence by using the scene text sequence and the initial service function set, driving the matching service unit sequence, and generating a first matching report by using the matching service unit sequence if the matching service unit sequence is successfully driven;
Otherwise, acquiring a plurality of evaluation service unit sequences by using the scene text sequence and the initial service function set, confirming a target service unit sequence according to the plurality of evaluation service unit sequences, and generating a second matching report by using the target service unit sequence;
and sending the first matching report or the second matching report to an initiating terminal of the flow arrangement instruction by using the result feedback unit, so as to realize the flow arrangement of the payment service.
Optionally, the acquiring the initial service function set based on the service unit set and the initial sorting unit includes:
Confirming receiving an initial ordering instruction from an initial ordering unit, and analyzing the initial ordering instruction to obtain a flow text set, wherein the flow text set comprises a plurality of flow texts;
the following operations are performed for each process text in the set of process texts:
Obtaining a flow keyword sequence by utilizing a pre-constructed language processing model, a pre-constructed corpus and a flow text, wherein the flow keyword sequence comprises a plurality of flow keywords;
summarizing the flow keyword sequences to obtain a plurality of flow keyword sequences, and merging the plurality of flow keyword sequences by utilizing the corpus to obtain a plurality of initial functional texts;
and acquiring an initial service function set by utilizing the plurality of initial function texts and the service unit set.
Optionally, the acquiring the initial service function set by using a plurality of initial function texts and service unit sets includes:
the following operations are executed for each of the plurality of initial function texts:
counting the number of the initial function texts in a plurality of flow keyword sequences to obtain the counted number, summarizing the counted number to obtain a counted number set, and calculating a text counted proportion set by using the counted number set, wherein a calculation formula is shown as follows:
Wherein, theRepresenting text statistics scale setThe proportion of the individual texts is counted,Representing the statistics collectionThe number of the statistics is counted and the number of the statistics is counted,Representing statistics sharing in a collectionThe number of the statistics is counted and the number of the statistics is counted,Representing the statistics collectionCounting the number;
Extracting a target statistical proportion set from a text statistical proportion set by using a preset statistical proportion threshold, wherein the target statistical proportion set comprises a plurality of target statistical proportions, the target statistical proportion is larger than or equal to the statistical proportion threshold, and executing the following operation on each target statistical proportion in the plurality of target statistical proportions:
Updating the service functions corresponding to the service units in the service unit set according to the initial function text corresponding to the target statistical proportion to obtain an updated service unit set, wherein the updated service unit set comprises a plurality of service units marked with the updated service functions;
And respectively summarizing service units in the updated service unit set according to the updated service functions to obtain a plurality of classified service unit sets, and acquiring an initial service function set by using the classified service unit sets.
Optionally, the acquiring an initial service function set by using the multiple classified service unit sets includes:
The following operations are performed for each classified service unit in the plurality of classified service unit sets:
Acquiring average response time of classified service units, calculating unit evaluation values based on the average response time and a pre-constructed unit evaluation relation, summarizing the unit evaluation values to obtain a unit evaluation value set, and sequencing the unit evaluation values in the unit evaluation value set according to the sequence from large to small to obtain a unit evaluation value sequence;
and mapping an initial service function sequence according to the classified service units in the bit sequence in the unit evaluation value sequence.
Optionally, the unit evaluates the relationship as follows:
Wherein, theThe unit evaluation value is represented by a unit,Are all the coefficients of the preset value,Indicating the throughput of the classified service units,Indicating the expected throughput of the classified traffic units,Indicating the maximum response time of the classified service units,Representing the average response time of the classified service units,Indicating the successful request rate of the classified service units,Represents the average fault diagnosis time of the classified service units,Representing the expansion score of the classified business units.
Optionally, the obtaining the matching service unit sequence by using the scene text sequence and the initial service function set includes:
updating scene keywords in the scene text sequence by utilizing a plurality of initial function texts to obtain an updated scene keyword sequence;
Searching a search service function set in the initial service function set by using the update scene keywords in the update scene keyword sequence, wherein the search service function set comprises a plurality of search service function sequences;
The following operations are performed for each of the plurality of search service function sequences:
Extracting a first search service unit in the search service function sequence, summarizing the first search service unit to obtain a search service unit set, and acquiring a matched service unit sequence according to the updated scene keyword sequence and the search service unit set.
Optionally, the acquiring a plurality of evaluation service unit sequences by using the scene text sequence and the initial service function set includes:
the following operations are performed for each search service function sequence in the service function set:
extracting a matching service function sequence from the retrieval service function sequence by using a preset extraction value, and summarizing the matching service function sequence to obtain a plurality of matching service function sequences;
counting the number of each scene text in the scene text sequence to obtain a matching number group, wherein the matching number group is as follows:
Wherein, theRepresenting a set of matching numbers,Respectively representing a first number of matches in the set of matches and a second number of matches in the set of matches,Representing sharing among matched quantity setsA number of matches;
The following operations are performed on the matching numbers in the matching number group:
And in a combined form, acquiring a matched service function group set by utilizing the matched quantity groups and matched service function sequences corresponding to the matched quantity, summarizing the matched service function group set to acquire a plurality of matched service function group sets, and acquiring a plurality of evaluation service unit sequences by utilizing the plurality of matched service function group sets and scene text sequences in an arranged form.
Optionally, the identifying the target service unit sequence according to the multiple evaluation service unit sequences includes:
the following operations are performed for each of the plurality of evaluation business unit sequences:
acquiring an evaluation factor set of the evaluation service unit sequence, calculating an evaluation reference value by using the evaluation factor set, and calculating the following formula:
Wherein, theThe evaluation reference value is represented by a reference value,Representing a preset firstThe number of coefficients is set to be the number of coefficients,Representing the first of the set of assessment factorsThe number of the evaluation factors is a function of the number of the evaluation factors,Representing sharing among a set of evaluation factorsEach evaluation factor;
Summarizing the evaluation reference values to obtain an evaluation reference value set, and acquiring a target service unit sequence by using the evaluation reference value set.
Optionally, the acquiring the target service unit sequence by using the evaluation reference value set includes:
Sequencing the evaluation reference values in the evaluation reference value set according to the sequence from large to small to obtain an evaluation reference value sequence, and confirming an extraction reference value sequence in the evaluation reference value sequence by utilizing a preset evaluation extraction value;
Acquiring a plurality of reference comparison sequences based on the extracted reference value sequences, wherein the reference comparison sequences correspond to the evaluation factors one by one, and the bit sequence of the reference comparison values in the reference comparison sequences is identical to the bit sequence of the evaluation reference values in the extracted reference value sequences;
And if the first reference comparison value in each of the plurality of reference comparison sequences is the maximum value, taking the evaluation service unit sequence corresponding to the first extraction reference value in the extraction reference value sequence as a target service unit sequence.
In order to achieve the above purpose, the present invention further provides a system for arranging the order-receiving payment business process based on visualization, which comprises:
the business unit acquisition module is used for receiving a flow programming instruction and confirming a flow programming system based on the flow programming instruction, wherein the flow programming system comprises a scene identification unit, an initial ordering unit and a result feedback unit;
Acquiring a service unit set with service functions, wherein the service unit set comprises a plurality of service units, each service unit in the plurality of service units is marked with a service function, and acquiring an initial service function set based on the service unit set and an initial sequencing unit, wherein the initial service function set comprises a plurality of initial service function sequences, and the initial service function sequences are in one-to-one correspondence with the service functions;
The business scene recognition module is used for confirming and receiving a scene recognition instruction from the scene recognition unit, analyzing the scene recognition instruction to obtain a scene recognition text, and extracting a scene Jing Wenben sequence from the scene recognition text, wherein the scene text sequence comprises a plurality of scene keywords;
The business unit arrangement module is used for acquiring a matched business unit sequence by utilizing the scene text sequence and the initial business function set, driving the matched business unit sequence, and generating a first matching report by utilizing the matched business unit sequence if the matched business unit sequence is successfully driven;
Otherwise, acquiring a plurality of evaluation service unit sequences by using the scene text sequence and the initial service function set, confirming a target service unit sequence according to the plurality of evaluation service unit sequences, and generating a second matching report by using the target service unit sequence;
and the arrangement result feedback module is used for sending the first matching report or the second matching report to an initiating terminal of the flow arrangement instruction by utilizing the result feedback unit so as to realize the flow arrangement of the payment service.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
and the processor executes the instructions stored in the memory to realize the visual order-receiving payment business process arrangement method.
In order to solve the above-mentioned problems, the present invention further provides a computer readable storage medium, where at least one instruction is stored, where the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned order-receiving payment business process arrangement method based on visualization.
The invention solves the problems in the background technology, the invention acquires a service unit set with service functions, wherein the service unit set comprises a plurality of service units, each service unit in the plurality of service units is marked with service functions, the initial service function set is acquired based on the service unit set and an initial sequencing unit, wherein the initial service function set comprises a plurality of initial service function sequences, and the initial service function sequences are in one-to-one correspondence with the service functions, the invention identifies the service units by combining texts used in actual situations so as to divide and classify the service units, screens the service units which are frequently used, evaluates the service units which are frequently used so as to determine the use sequence of different service units under the theoretical situation under the same function, lays a foundation for arranging payment services subsequently, confirms and receives scene recognition instructions from scene recognition units, obtains scene recognition texts, extracts a scene Jing Wenben sequence from the scene recognition texts, wherein the text sequence comprises a plurality of scene keywords, analyzes the scene recognition texts by combining the text, and uses the text sequence to obtain a matching sequence, otherwise, if the service units are matched with the service units, the service units are successfully obtained by using the sequence, otherwise, the service units are matched with the sequence is successfully obtained by using the sequence, the method comprises the steps of determining a target service unit sequence according to a plurality of evaluation service unit sequences, and generating a second matching report by utilizing the target service unit sequence, wherein under the theoretical condition, the service units are possibly arranged without meeting the requirement of compatibility, so that service units with different functions are screened, a plurality of evaluation service unit sequences are obtained in a combined mode, the evaluation service unit sequences are evaluated, the optimal evaluation service unit sequences are determined to be optimal in all dimensions, the target service unit sequence is obtained, and the used service units are determined by generating the report form, so that the intelligent degree of the method is improved. Therefore, the invention can improve the intelligent degree of the payment flow arrangement.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a method for arranging a receipt payment business process based on visualization. The execution main body of the visual order-receiving payment business process arrangement method comprises at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the visual order-based payment business process arrangement method can be executed by software or hardware installed in a terminal device or a server device, wherein the software can be a blockchain platform. The server side comprises, but is not limited to, a single server, a server cluster, a cloud server or a cloud server cluster and the like.
Referring to fig. 1, a flow diagram of a method for arranging a pay-per-view business flow based on visualization according to an embodiment of the present invention is shown. In this embodiment, the method for arranging the order-receiving payment business process based on visualization includes:
S1, receiving a flow programming instruction, and confirming a flow programming system based on the flow programming instruction, wherein the flow programming system comprises a scene recognition unit, an initial ordering unit and a result feedback unit.
It should be explained that the flow programming instruction is an instruction sent by a software tester or a software developer, and is used for testing software in different application scenarios. In the embodiment of the invention, the software refers to software composed of units with different service functions. The process scheduling system refers to an applet or APP capable of matching different software according to different application scenes, and includes a scene recognition unit, an initial ordering unit, and a result feedback unit, for specific applications of the units, please refer to the subsequent embodiments.
Illustratively, in order to develop a piece of software that is applied to a particular scenario, the software developer issues the flow programming instructions and the software developer confirms the flow programming system.
S2, acquiring a service unit set with service functions, wherein the service unit set comprises a plurality of service units, each service unit in the plurality of service units is marked with a service function, and acquiring an initial service function set based on the service unit set and an initial sequencing unit, wherein the initial service function set comprises a plurality of initial service function sequences, and the initial service function sequences correspond to the service functions one by one.
It should be explained that a service unit refers to a unit or node that is capable of performing a specific function. The service function refers to a function that can be implemented by the service unit. Such as payment, feedback. For example, a code capable of generating a random number between 1 and 10 is edited in a coded form, and then the code is a service unit identified with the generation of the random number.
It can be understood that the obtaining the initial service function set based on the service unit set and the initial sorting unit includes:
Confirming receiving an initial ordering instruction from an initial ordering unit, and analyzing the initial ordering instruction to obtain a flow text set, wherein the flow text set comprises a plurality of flow texts;
the following operations are performed for each process text in the set of process texts:
Obtaining a flow keyword sequence by utilizing a pre-constructed language processing model, a pre-constructed corpus and a flow text, wherein the flow keyword sequence comprises a plurality of flow keywords;
summarizing the flow keyword sequences to obtain a plurality of flow keyword sequences, and merging the plurality of flow keyword sequences by utilizing the corpus to obtain a plurality of initial functional texts;
and acquiring an initial service function set by utilizing the plurality of initial function texts and the service unit set.
It should be understood that the flow text refers to text for describing a process of implementing a certain service when the service is implemented. The process text set can be acquired by a manual setting mode. The language processing model is a model capable of screening or eliminating text, and optionally, a natural language processing model is adopted as the language processing model, and other technologies can achieve the same action and effects, which are not described herein. Corpus refers to a database storing text describing different business functions. Alternatively, the corpus is obtained in a manually-formulated manner, and the same effects can be achieved by adopting other technologies, which are not described in detail herein. The sequence of the flow keywords refers to a sequence of texts extracted according to the flow texts for realizing different functions. The flow keywords are keywords for describing different service functions.
The process text is that when the points are accumulated, payment is needed first, the paid amount is converted into corresponding points, the points are accumulated on the original points, the language processing model and the corpus are utilized to extract payment, point conversion and point accumulation from the process text, the three process keywords are sequenced from first to last in the process text according to the sequence of the process keywords, and a process keyword sequence is obtained, wherein the process keyword sequence is payment, point conversion and point accumulation.
Further, the keywords included in the description functions may be different in different process texts, so that a plurality of process keyword sequences can be obtained by processing the process texts in the process text set. The merging operation of the plurality of flow keyword sequences by using the corpus refers to updating the flow keywords in the flow keyword sequences into words with the same meaning. For example, the integration of the points may be expressed as statistics of the points, total of the points, or the like, and the integration operation may be performed on the integration, statistics, or total, or the integration may be expressed as the integration, that is, the statistics and total may be expressed as the integration. Updating the plurality of process keyword sequences with the plurality of initial function texts means that all process keywords having the same meaning are updated to the same process keyword.
It should be explained that the obtaining the initial service function set by using the multiple initial function texts and the service unit set includes:
the following operations are executed for each of the plurality of initial function texts:
counting the number of the initial function texts in a plurality of flow keyword sequences to obtain the counted number, summarizing the counted number to obtain a counted number set, and calculating a text counted proportion set by using the counted number set, wherein a calculation formula is shown as follows:
Wherein, theRepresenting text statistics scale setThe proportion of the individual texts is counted,Representing the statistics collectionThe number of the statistics is counted and the number of the statistics is counted,Representing statistics sharing in a collectionThe number of the statistics is counted and the number of the statistics is counted,Representing the statistics collectionCounting the number;
Extracting a target statistical proportion set from a text statistical proportion set by using a preset statistical proportion threshold, wherein the target statistical proportion set comprises a plurality of target statistical proportions, the target statistical proportion is larger than or equal to the statistical proportion threshold, and executing the following operation on each target statistical proportion in the plurality of target statistical proportions:
Updating the service functions corresponding to the service units in the service unit set according to the initial function text corresponding to the target statistical proportion to obtain an updated service unit set, wherein the updated service unit set comprises a plurality of service units marked with the updated service functions;
And respectively summarizing service units in the updated service unit set according to the updated service functions to obtain a plurality of classified service unit sets, and acquiring an initial service function set by using the classified service unit sets.
It can be understood that the updating of the service function corresponding to the service unit in the service unit set according to the initial function text corresponding to the target statistical proportion means that the language processing model and the initial function text are utilized to identify the service function which is the same as the initial function text in terms of definition in the plurality of service functions, and update the service function to the initial function text, where the initial function text used for updating is the updated service function.
It should be explained that the obtaining the initial service function set by using the plurality of classified service unit sets includes:
The following operations are performed for each classified service unit in the plurality of classified service unit sets:
Acquiring average response time of classified service units, calculating unit evaluation values based on the average response time and a pre-constructed unit evaluation relation, summarizing the unit evaluation values to obtain a unit evaluation value set, and sequencing the unit evaluation values in the unit evaluation value set according to the sequence from large to small to obtain a unit evaluation value sequence;
and mapping an initial service function sequence according to the classified service units in the bit sequence in the unit evaluation value sequence.
It is understood that the average response time refers to the average time required by the traffic classification unit to process a request. Through the recognition, the classified service units and the unit evaluation values have a one-to-one correspondence, so that the initial service function sequence can be acquired in the bit sequence in the unit evaluation value sequence according to the unit evaluation value corresponding to the classified service units. For example, 3 classified service units exist, the unit evaluation values corresponding to the 3 classified service units are respectively 3, 5 and 2, namely, the unit evaluation value corresponding to the first classified service unit is 3, the unit evaluation value corresponding to the second classified service unit is 5, the unit evaluation value corresponding to the third classified service unit is 2, the unit evaluation value sequence obtained by using the unit evaluation value corresponding to the classified service unit is 5, 3 and 2, and the initial service function sequence obtained according to the unit evaluation value sequence is the second classified service unit, the first classified service unit and the third classified service unit.
Further, the unit evaluates the relationship as follows:
Wherein, theThe unit evaluation value is represented by a unit,Are all the coefficients of the preset value,Indicating the throughput of the classified service units,Indicating the expected throughput of the classified traffic units,Indicating the maximum response time of the classified service units,Representing the average response time of the classified service units,Indicating the successful request rate of the classified service units,Represents the average fault diagnosis time of the classified service units,Representing the expansion score of the classified business units.
It should be explained that the expected throughput refers to the throughput of the classified service unit in the theoretical case, the maximum response time refers to the maximum time preset to allow the classified service unit to respond, the success request rate refers to the probability of the classified service unit successfully processing the request, and the average failure diagnosis time refers to the average time required for diagnosing the classified service unit when the classified service unit fails. The expansion score is a score for evaluating whether the classified service unit supports capacity expansion, and optionally, the expansion score is obtained in a manually set form, for example, when the classified service unit cannot expand capacity, the expansion score is set to 0, when the classified service unit supports only manual capacity expansion, the expansion score is set to 0.5, and when the classified service unit supports dynamic capacity expansion, the expansion score is set to 1. Optionally, an analytic hierarchy process is adopted to evaluate the classified service units to obtain coefficients in the unit evaluation relational expression, and other technologies are adopted to achieve the same effect, which is not described herein.
S3, confirming receiving a scene recognition instruction from a scene recognition unit, analyzing the scene recognition instruction to obtain a scene recognition text, and extracting a scene Jing Wenben sequence from the scene recognition text, wherein the scene text sequence comprises a plurality of scene keywords.
It should be explained that the scene recognition text is text for describing the required generation software functions. Optionally, the scene recognition text is input by a software developer in the process scheduling system, and a method for extracting the scene text sequence from the scene recognition text is the same as a method for acquiring the process keyword by using the process text, which is not described herein.
S4, acquiring a matching service unit sequence by using the scene text sequence and the initial service function set, driving the matching service unit sequence, and if the matching service unit sequence is successfully driven, generating a first matching report by using the matching service unit sequence.
It can be understood that the obtaining the matching service unit sequence by using the scene text sequence and the initial service function set includes:
updating scene keywords in the scene text sequence by utilizing a plurality of initial function texts to obtain an updated scene keyword sequence;
Searching a search service function set in the initial service function set by using the update scene keywords in the update scene keyword sequence, wherein the search service function set comprises a plurality of search service function sequences;
The following operations are performed for each of the plurality of search service function sequences:
Extracting a first search service unit in the search service function sequence, summarizing the first search service unit to obtain a search service unit set, and acquiring a matched service unit sequence according to the updated scene keyword sequence and the search service unit set.
Further, the method for updating the scene keywords in the scene text sequence by using the plurality of initial function texts is the same as the method for updating the service functions corresponding to the service units in the service unit set by using the initial function texts corresponding to the target statistical proportion, and is not described herein. The purpose of acquiring the updated scene keyword sequence is to unify the names of the service functions corresponding to the service units. The initial function text corresponding to the search service function sequence is the same as the update scene keyword, that is, the update scene keyword in the update scene keyword sequence is utilized to search the search service function set in the initial service function set, that is, the initial service function sequence identical to the update scene keyword is searched in the initial service function set. The method for obtaining the matching service unit sequence according to the updated scene keyword sequence and the search service unit set is the same as the method for mapping the initial service function sequence according to the classified service units in the unit evaluation value sequence, and will not be described herein. Through recognition, the acquired matching service unit sequence aggregates the best service units in different service functions, so that the acquired matching service unit sequence can be considered as an ideal unit for executing the scene described by the scene recognition text.
It should be explained that, the matching service units in the matching service unit sequence acquired by using the best service unit are not necessarily compatible in the execution process, so that the matching service unit sequence cannot be successfully executed. And when the matching service unit sequence can be successfully executed, extracting the bit sequences of the matching service units in different initial service function sequences in the matching service unit sequence, and identifying the bit sequences of the matching service units in the corresponding initial service function sequences in the matching service unit sequence to obtain a first matching report.
S5, otherwise, acquiring a plurality of evaluation service unit sequences by using the scene text sequence and the initial service function set, confirming a target service unit sequence according to the plurality of evaluation service unit sequences, and generating a second matching report by using the target service unit sequence.
It should be explained that the obtaining a plurality of evaluation service unit sequences by using the scene text sequence and the initial service function set includes:
the following operations are performed for each search service function sequence in the service function set:
extracting a matching service function sequence from the retrieval service function sequence by using a preset extraction value, and summarizing the matching service function sequence to obtain a plurality of matching service function sequences;
counting the number of each scene text in the scene text sequence to obtain a matching number group, wherein the matching number group is as follows:
Wherein, theRepresenting a set of matching numbers,Respectively representing a first number of matches in the set of matches and a second number of matches in the set of matches,Representing sharing among matched quantity setsA number of matches;
The following operations are performed on the matching numbers in the matching number group:
And in a combined form, acquiring a matched service function group set by utilizing the matched quantity groups and matched service function sequences corresponding to the matched quantity, summarizing the matched service function group set to acquire a plurality of matched service function group sets, and acquiring a plurality of evaluation service unit sequences by utilizing the plurality of matched service function group sets and scene text sequences in an arranged form.
Further, the matching service function sequence refers to sequentially extracting the retrieval service function units with the same extraction value from the retrieval service function sequence. For example, the search service function sequence includes an A-service unit, an B-service unit, a C-service unit and a T-service unit, and if the preset extraction value is 3, the matching service function sequence to be extracted in the search service function sequence by using the extraction value is the A-service unit, the B-service unit and the C-service unit.
It is understood that the number of matches characterizes the number of business units that are required for the same class of business units to satisfy the scene represented by the scene recognition text. Firstly, after the matching service function groups with the same matching quantity are extracted from different matching service function sequences, the compatibility difference which can be adapted to different matching service function units is considered, so that different sequences of the matching service function units with the same function in the implementation process are acquired through an arrangement mode, and the ordered matching service function units are confirmed to be capable of being executed, and then an evaluation service unit sequence is obtained.
It should be appreciated that the identifying the target service unit sequence from the plurality of estimated service unit sequences includes:
the following operations are performed for each of the plurality of evaluation business unit sequences:
acquiring an evaluation factor set of the evaluation service unit sequence, calculating an evaluation reference value by using the evaluation factor set, and calculating the following formula:
Wherein, theThe evaluation reference value is represented by a reference value,Representing a preset firstThe number of coefficients is set to be the number of coefficients,Representing the first of the set of assessment factorsThe number of the evaluation factors is a function of the number of the evaluation factors,Representing sharing among a set of evaluation factorsEach evaluation factor;
Summarizing the evaluation reference values to obtain an evaluation reference value set, and acquiring a target service unit sequence by using the evaluation reference value set.
It should be explained that the evaluation factor refers to an index reference for evaluating the sequence of evaluation business units. Such as energy consumption, response time, throughput, concurrency, network bandwidth, encryption strength.
Further, the acquiring the target service unit sequence by using the evaluation reference value set includes:
Sequencing the evaluation reference values in the evaluation reference value set according to the sequence from large to small to obtain an evaluation reference value sequence, and confirming an extraction reference value sequence in the evaluation reference value sequence by utilizing a preset evaluation extraction value;
Acquiring a plurality of reference comparison sequences based on the extracted reference value sequences, wherein the reference comparison sequences correspond to the evaluation factors one by one, and the bit sequence of the reference comparison values in the reference comparison sequences is identical to the bit sequence of the evaluation reference values in the extracted reference value sequences;
And if the first reference comparison value in each of the plurality of reference comparison sequences is the maximum value, taking the evaluation service unit sequence corresponding to the first extraction reference value in the extraction reference value sequence as a target service unit sequence.
It should be explained that the obtaining manner of the reference value sequence is the same as that of the matching service function sequence, and will not be described herein. Through recognition, each evaluation service unit sequence corresponds to one evaluation reference value, and each evaluation reference value corresponds to one evaluation factor group, so that different reference comparison sequences can be obtained by using different evaluation factors. For example, three sets of evaluation factors exist, wherein the first set of evaluation factors is 3,4,5, the second set of evaluation factors is 4,7,1, the third set of evaluation factors is 1,6,3, and the evaluation reference value corresponding to the first set of evaluation factors is greater than the evaluation reference value corresponding to the second set of evaluation factors, and is greater than the evaluation reference value corresponding to the third set of evaluation factors, then three reference alignment sequences {3,4,1}, {4,7,6}, and {5,1,3} can be obtained by using the three sets of evaluation factors. When the first reference comparison value in each of the plurality of reference comparison sequences is the maximum value, the first evaluation service unit sequence is better than the other evaluation service unit sequences in each dimension. The second matching report is obtained in the same manner as the first matching report, and will not be described in detail herein.
S6, sending the first matching report or the second matching report to an initiating terminal of the flow arrangement instruction by utilizing the result feedback unit, so as to realize flow arrangement of the payment service.
The invention solves the problems in the background technology, the invention acquires a service unit set with service functions, wherein the service unit set comprises a plurality of service units, each service unit in the plurality of service units is marked with service functions, the initial service function set is acquired based on the service unit set and an initial sequencing unit, wherein the initial service function set comprises a plurality of initial service function sequences, and the initial service function sequences are in one-to-one correspondence with the service functions, the invention identifies the service units by combining texts used in actual situations so as to divide and classify the service units, screens the service units which are frequently used, evaluates the service units which are frequently used so as to determine the use sequence of different service units under the theoretical situation under the same function, lays a foundation for arranging payment services subsequently, confirms and receives scene recognition instructions from scene recognition units, obtains scene recognition texts, extracts a scene Jing Wenben sequence from the scene recognition texts, wherein the text sequence comprises a plurality of scene keywords, analyzes the scene recognition texts by combining the text, and uses the text sequence to obtain a matching sequence, otherwise, if the service units are matched with the service units, the service units are successfully obtained by using the sequence, otherwise, the service units are matched with the sequence is successfully obtained by using the sequence, the method comprises the steps of determining a target service unit sequence according to a plurality of evaluation service unit sequences, and generating a second matching report by utilizing the target service unit sequence, wherein under the theoretical condition, the service units are possibly arranged without meeting the requirement of compatibility, so that service units with different functions are screened, a plurality of evaluation service unit sequences are obtained in a combined mode, the evaluation service unit sequences are evaluated, the optimal evaluation service unit sequences are determined to be optimal in all dimensions, the target service unit sequence is obtained, and the used service units are determined by generating the report form, so that the intelligent degree of the method is improved. Therefore, the invention can improve the intelligent degree of the payment flow arrangement.
Fig. 2 is a functional block diagram of a visual order-receiving payment business process arrangement system according to an embodiment of the present invention.
The visual order-receiving payment business process arrangement system 100 can be installed in electronic equipment. Depending on the implementation function, the visualization-based order payment business process arrangement system 100 may include a business unit acquisition module 101, a business scene identification module 102, a business unit arrangement module 103, and an arrangement result feedback module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The service unit obtaining module 101 is configured to receive a flow scheduling instruction, and confirm a flow scheduling system based on the flow scheduling instruction, where the flow scheduling system includes a scene recognition unit, an initial ordering unit, and a result feedback unit;
Acquiring a service unit set with service functions, wherein the service unit set comprises a plurality of service units, each service unit in the plurality of service units is marked with a service function, and acquiring an initial service function set based on the service unit set and an initial sequencing unit, wherein the initial service function set comprises a plurality of initial service function sequences, and the initial service function sequences are in one-to-one correspondence with the service functions;
The service scene recognition module 102 is configured to confirm receiving a scene recognition instruction from a scene recognition unit, parse the scene recognition instruction to obtain a scene recognition text, and extract a scene Jing Wenben sequence from the scene recognition text, where the scene text sequence includes a plurality of scene keywords;
The service unit arrangement module 103 is configured to obtain a matching service unit sequence by using the scene text sequence and an initial service function set, drive the matching service unit sequence, and if the matching service unit sequence is successfully driven, generate a first matching report by using the matching service unit sequence;
Otherwise, acquiring a plurality of evaluation service unit sequences by using the scene text sequence and the initial service function set, confirming a target service unit sequence according to the plurality of evaluation service unit sequences, and generating a second matching report by using the target service unit sequence;
the arrangement result feedback module 104 is configured to send a first matching report or a second matching report to an initiating end of a process arrangement instruction by using the result feedback unit, so as to implement process arrangement of the payment service.
In detail, the modules in the order-receiving payment business process arrangement system 100 based on visualization in the embodiment of the present invention use the same technical means as the above-mentioned order-receiving payment business process arrangement method based on visualization in fig. 1, and can produce the same technical effects, which are not described herein.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.