Field transformation troubleshooting auxiliary method and device, equipment, medium and product

Abstract

The invention discloses a field transformation troubleshooting auxiliary method and device, equipment, a medium and a product, and relates to the technical field of financial science and technology. The field transformation troubleshooting auxiliary method comprises the following steps: determining a parameter value standard proportion of a target transformation field, and counting a parameter value contrast proportion of each data change field; according to the parameter value standard proportion and the parameter value contrast proportion of each data change field, performing similarity level evaluation to obtain a similarity level evaluation result, and calculating a proportion difference set corresponding to the similarity level evaluation result; and according to the similarity level evaluation result and the proportion difference value set, generating a code troubleshooting auxiliary report. According to the technical scheme provided by the embodiment of the invention, automatic auxiliary troubleshooting after field transformation of the financial service system can be realized, the labor cost is greatly reduced, and the troubleshooting efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a field modification investigation auxiliary method, device, equipment, medium and product. Background Art

[0002] When a parameter in a financial business system changes (for example, if the length of a customer account is increased by 5 digits, the field storing the account in the database needs to be expanded), multiple systems will need to be checked and rectified. Since dozens or even hundreds of systems in the financial industry interact with each other and package data with each other, and there is encryption for security reasons, it is not intuitive to see whether a lot of data is the target data, which increases the difficulty of rectification and investigation.

[0003] Currently, the process mainly relies on manual investigation and keyword search tools, which is inefficient. In addition, because a large number of systems are interdependent and coupled, a change in data may cause dozens or hundreds of systems to need to be investigated, and there are complex nested relationships between systems (for example, after system A is checked, system B is notified to check whether the data found by system A affects system B). Often, a transformation takes several months to investigate to avoid omissions. Summary of the invention

[0004] The present invention provides a field transformation and troubleshooting auxiliary method, device, equipment, medium and product to solve the problems of low troubleshooting efficiency, long troubleshooting cycle and high labor cost after field transformation in existing financial business systems.

[0005] According to one aspect of the present invention, a field transformation troubleshooting auxiliary method is provided, comprising:

[0006] Determine the standard proportion of parameter values ​​for the target transformation fields, and calculate the comparison proportion of parameter values ​​for each data change field;

[0007] Perform similarity level assessment based on the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field to obtain a similarity level assessment result, and calculate a proportion difference set corresponding to the similarity level assessment result;

[0008] Generate a code troubleshooting auxiliary report based on the similarity level assessment results and the percentage difference set.

[0009] According to another aspect of the present invention, a field modification troubleshooting auxiliary device is provided, comprising:

[0010] The parameter value ratio statistics module is used to determine the standard ratio of parameter values ​​of the target transformation field and to count the comparison ratio of parameter values ​​of each data change field;

[0011] The similarity level assessment and proportion difference calculation module is used to perform similarity level assessment according to the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field, obtain the similarity level assessment result, and calculate the proportion difference set corresponding to the similarity level assessment result;

[0012] The report generation module is used to generate a code troubleshooting auxiliary report based on the similarity level evaluation results and the percentage difference set.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the field transformation troubleshooting auxiliary method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the field transformation troubleshooting auxiliary method described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the field transformation troubleshooting auxiliary method described in any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention determines the standard proportion of the parameter value of the target transformation field, and counts the comparison proportion of the parameter value of each data change field, thereby performing a similarity level assessment based on the standard proportion of the parameter value and the comparison proportion of the parameter value of each data change field, obtaining a similarity level assessment result, and calculating the percentage difference set corresponding to the similarity level assessment result, and then generating a code troubleshooting auxiliary report based on the similarity level assessment result and the percentage difference set. Without intruding into the financial business system, this solution automatically filters out the codes for field changes on most financial business processing chains based on the code troubleshooting auxiliary report, greatly reducing the manpower cost required for troubleshooting, and solving the problems of low troubleshooting efficiency, long troubleshooting cycle, and high labor cost after field transformation in existing financial business systems. It can realize automatic auxiliary troubleshooting after field transformation in financial business systems, greatly reducing labor costs, and effectively improving troubleshooting efficiency.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flowchart of a field modification troubleshooting auxiliary method provided in Embodiment 1 of the present invention;

[0023] Figure 2 A flowchart of a field transformation troubleshooting auxiliary method provided in Embodiment 2 of the present invention;

[0024] Figure 3 A schematic diagram of the structure of a field modification troubleshooting auxiliary device provided in Embodiment 3 of the present invention;

[0025] Figure 4 A schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 This is a flowchart of a field modification troubleshooting auxiliary method provided in the first embodiment of the present invention. This embodiment can be applied to the situation of efficiently troubleshooting the modification fields of the financial business system. The method can be executed by a field modification troubleshooting auxiliary device. The field modification troubleshooting auxiliary device can be implemented in the form of hardware and / or software. The field modification troubleshooting auxiliary device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0030] Step 110: determine the standard proportion of the parameter value of the target transformation field, and count the comparison proportion of the parameter value of each data change field.

[0031] Among them, the target transformation field can be a field that needs to be transformed in the financial business system. The target transformation field can include but is not limited to the data field that the financial business system needs to send and receive on the same day. The parameter value standard ratio can be used to describe the ratio of each data value of the target transformation field. The data change field can be other fields in the financial business system except the target transformation field. The parameter value comparison ratio can be used to describe the ratio of each data value of the data change field.

[0032] In an embodiment of the present invention, each data value of the target transformation field can be obtained first, and based on the quantity ratio of each data value of the target transformation field, the standard parameter value ratio of the target transformation field is used as the parameter value standard ratio of the target transformation field, and the parameter value comparison ratio of each data change field is calculated based on the quantity ratio of each data value of each data change field.

[0033] Step 120: perform similarity level assessment based on the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field to obtain a similarity level assessment result, and calculate a proportion difference set corresponding to the similarity level assessment result.

[0034] The similarity level assessment may be an assessment operation of the similarity between the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field. The similarity level assessment result may be a similarity assessment result between the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field. The difference proportion set may be a data set describing the deviation of the similarity between the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field.

[0035] In an embodiment of the present invention, the parameter value comparison ratio of each data change field can be compared with the standard parameter value ratio in turn for similarity, and a similarity level assessment can be performed to obtain a similarity level assessment result, and then the ratio difference set corresponding to the similarity level assessment result of the target transformation field and each data change field is calculated.

[0036] Optionally, different levels of similarity evaluation rules may be pre-set, and using the different levels of similarity evaluation rules, the parameter value comparison ratio of each data change field is compared with the parameter value standard ratio for similarity to obtain a similarity level evaluation result.

[0037] Step 130: Generate a code troubleshooting auxiliary report based on the similarity level evaluation results and the percentage difference set.

[0038] Among them, the code troubleshooting auxiliary report can be a report that assists in troubleshooting the code fields of the financial business system based on the percentage difference set and the similarity level assessment results.

[0039] In an embodiment of the present invention, data related to field troubleshooting can be determined based on the data change fields and target transformation fields in the similarity level assessment results, and then the data related to field troubleshooting can be sorted according to the proportion difference set, that is, the relevant information of the fields that need to be checked is arranged in front, and a code troubleshooting auxiliary report is obtained.

[0040] The technical solution of the embodiment of the present invention determines the standard proportion of the parameter value of the target transformation field, and counts the comparison proportion of the parameter value of each data change field, thereby performing a similarity level assessment based on the standard proportion of the parameter value and the comparison proportion of the parameter value of each data change field, obtaining a similarity level assessment result, and calculating the percentage difference set corresponding to the similarity level assessment result, and then generating a code troubleshooting auxiliary report based on the similarity level assessment result and the percentage difference set. Without intruding into the financial business system, this solution automatically filters out the codes for field changes on most financial business processing chains based on the code troubleshooting auxiliary report, greatly reducing the manpower cost required for troubleshooting, and solving the problems of low troubleshooting efficiency, long troubleshooting cycle, and high labor cost after field transformation in existing financial business systems. It can realize automatic auxiliary troubleshooting after field transformation in financial business systems, greatly reducing labor costs, and effectively improving troubleshooting efficiency.

[0041] Embodiment 2

[0042] Figure 2 This is a flowchart of a field transformation investigation auxiliary method provided in the second embodiment of the present invention. This embodiment is specific based on the above embodiment and provides a specific optional implementation method for determining the standard proportion of parameter values ​​of the target transformation field. Figure 2 As shown, the method includes:

[0043] Step 210: Obtain the number of each target parameter value of the target transformation field.

[0044] Among them, the target parameter value can be the parameter value of the target transformation field.

[0045] In the embodiment of the present invention, after determining the target transformation field, the number of each parameter value of the target transformation field may be counted, that is, the number of each target parameter value may be obtained.

[0046] Step 220: Based on the number of target parameter values ​​and the total number of parameters in the target transformation field, determine the standard proportion of parameter values, and count the comparison proportion of parameter values ​​in each data change field.

[0047] The total number of parameters may be the sum of the values ​​of each target parameter of the target transformation field.

[0048] In an embodiment of the present invention, the total number of parameters of the target transformation field can be calculated based on the number of each target parameter value of the target transformation field, and the standard proportion of parameter values ​​can be determined according to the ratio of the number of each target parameter value to the total number of parameters, and based on the determination logic of the standard proportion of parameter values, the comparison proportion of parameter values ​​of each data change field can be calculated.

[0049] For example, assuming that the parameter values ​​of the target transformation field A include three categories: aa, bb and ba, the value of aa accounts for 13%, the value of bb accounts for 19%, and the value of ba accounts for 68%, then the parameter value standards account for 13%, 19% and 68%.

[0050] Step 230: perform similarity level assessment based on the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field to obtain a similarity level assessment result, and calculate a proportion difference set corresponding to the similarity level assessment result.

[0051] In an optional embodiment of the present invention, the similarity level assessment result may include a first-level similarity assessment result, and / or a second-level similarity assessment result; performing a similarity level assessment based on the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field to obtain a similarity level assessment result, which may include: when the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of the current data change field meet the evaluation condition of consistent proportion values, determining that the current data change field and the target transformation field are first-level similar, and obtaining a first-level similarity assessment result; when the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of the current data change field do not meet the evaluation condition of consistent proportion values, determining the target comparison proportion combination of the current data change field based on the standard proportion of parameter values; performing a second-level similarity level assessment based on the standard proportion of parameter values ​​and the target comparison proportion combination of the current data change field to obtain a second-level similarity assessment result.

[0052] Among them, the first-level similarity evaluation result can be used to describe whether the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of the current data change field meet the first-level similarity. The second-level similarity evaluation result can be used to describe whether the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of the current data change field meet the second-level similarity. The proportion value consistency evaluation condition can be a condition for determining that the comparison proportion of the parameter value of the current data change field is similar to the standard proportion of parameter values ​​at the first level. The error between the parameter value proportions of the two fields that are similar at the first level (that is, the proportions of each parameter value) is less than the error threshold (which can be set by yourself, such as 5%). The target comparison proportion combination can be the combined proportion of each parameter value of the current data change field that meets the proportion value consistency evaluation condition with the standard proportion of parameter values. The parameter value proportions of the two fields that are similar at the second level meet the proportion value consistency evaluation condition in multiple comparison periods.

[0053] In an embodiment of the present invention, the parameter value comparison ratio of the current data change field can be compared with the parameter value standard ratio. If the error between the parameter value comparison ratio of the current data change field and the parameter value standard ratio is less than the error threshold, the parameter value standard ratio and the parameter value comparison ratio of the current data change field are determined to meet the ratio value consistency evaluation condition, that is, the current data change field and the target transformation field are first-level similar, and then the first-level similarity between the current data change field and the target transformation field is taken as the first-level similarity evaluation result. If the parameter value standard ratio and the parameter value comparison ratio of the current data change field do not meet the ratio value consistency evaluation condition, the parameter value ratio of the current data change field is combined. When the target comparison ratio combination is not empty, the parameter value standard ratio and the target comparison ratio combination of the current data change field are evaluated at the second level of similarity level to obtain the second level similarity evaluation result. By performing two levels of similarity evaluation, other fields with similar probability to the parameter ratio of the target transformation field can be automatically screened out to the maximum extent, that is, the relevant information of the field to be checked is determined in the form of probability statistics.

[0054] In an optional embodiment of the present invention, determining the target control ratio combination of the current data change field based on the standard ratio of the parameter value may include: combining the parameter value control ratio of the current data change field to obtain a ratio consistency evaluation combination; determining the target control ratio combination based on the standard ratio of the parameter value, the ratio consistency evaluation combination and the ratio value consistency evaluation condition.

[0055] The proportion consistency assessment combination may be a combination parameter value ratio of the parameter value of the current data change field compared with the proportion.

[0056] In an embodiment of the present invention, the parameter value comparison ratios of the current data change field can be combined, and the combined parameter value combination ratios can be used as the ratio consistency evaluation combination, so that the ratio consistency evaluation combination that meets the ratio value consistency evaluation conditions with the parameter value standard ratio can be used as the target comparison ratio combination. By combining the parameter value comparison ratios of the current data change field, a variety of combination deformations can be used to determine the potential data change field that is consistent with the parameter value probability distribution of the target transformation field, thereby improving the effect of auxiliary investigation of the data transformation field.

[0057] In an optional embodiment of the present invention, a second-level similarity level evaluation is performed based on a combination of a standard proportion of parameter values ​​and a target control proportion of current data change fields to obtain a second-level similarity evaluation result, which may include: determining multiple evaluation time periods for the second-level similarity level evaluation based on an average service timeout and an average service link length; and performing a second-level similarity level evaluation based on a combination of a standard proportion of parameter values ​​and a target control proportion of current data change fields according to the evaluation time periods to obtain a second-level similarity evaluation result.

[0058] The average service timeout may be the average execution time of all services in the financial business system. The average service link length may be the average value of the number of services related to the complete service link in the financial business system. The evaluation period may be the evaluation period of the similarity of the combination of the standard proportion of the parameter value and the target control proportion of the current data change field.

[0059] In an embodiment of the present invention, based on the average service timeout and the average service link length, combined with the network fluctuation and the width of the time interval, multiple evaluation periods for the second-level similarity level evaluation under the preset time window can be determined, and then according to the evaluation period, the parameter value standard proportion and the target control proportion of the current data change field are combined to perform the second-level similarity level evaluation to obtain the second-level similarity evaluation result. According to the evaluation period, the parameter value standard proportion and the target control proportion of the current data change field are combined to perform a similarity evaluation to avoid the target control proportion combination and the parameter value standard proportion The probability distribution is not strongly similar, and it is included in the code troubleshooting auxiliary report to affect the efficiency of the transformation field troubleshooting.

[0060] For example, the preset time window can be 24 hours or 72 hours, and the specific duration can be set by yourself. Calculate the evaluation period. Wherein, m represents the evaluation period, and a represents the bandwidth of network fluctuation and time interval, which can be set by business personnel, for example, a value greater than 2.

[0061] Step 240: Generate a code troubleshooting auxiliary report based on the similarity level evaluation results and the percentage difference set.

[0062] In an optional embodiment of the present invention, a code troubleshooting auxiliary report is generated based on the similarity level evaluation results and the percentage difference set, which may include: determining the location information of the target field to be troubleshooted based on the similarity level evaluation results; generating a code troubleshooting auxiliary report based on the percentage difference set, the location information of the target field to be troubleshooted, and the similarity level evaluation results.

[0063] The target field to be checked may be a field that has similarity with the target transformation field and needs to be checked. Exemplarily, the target field to be checked may include a data change field that is first-level similar to the target transformation field, or that satisfies both first-level similarity and second-level similarity. The location information of the target field to be checked may include, but is not limited to, a specific row and a specific field of the target field to be checked in a log or database.

[0064] In an embodiment of the present invention, the target field to be checked in the financial business system can be determined based on the similarity level assessment result, and the location information of the target field to be checked can be determined based on the database and / or log of the financial business system, so as to sort the similarity level assessment results using the percentage difference set, and determine the recording location of the location information of the target field to be checked based on the sorting result, and generate a code troubleshooting auxiliary report. By recording the location information of the determined target field to be checked in the code troubleshooting auxiliary report, the transformation field to be checked can be quickly located, and the field troubleshooting efficiency can be improved, and the percentage difference set can also realize the sorting of important information in the code troubleshooting auxiliary report.

[0065] In a specific example, the standard proportion of parameter values ​​of the target transformation field in the log or table of the financial business system can be counted (for example, for the target transformation field A, the proportion of values ​​of aa is 13%, the proportion of values ​​of ab is 19%, and the proportion of values ​​of ba is 68%). Optionally, a field in a table in the database, or a specific input parameter of a method recorded in the log can be used as the target transformation field.

[0066] Furthermore, the parameter value comparison ratios of all other data change fields in the database table and the log records are counted, and the parameter value comparison ratios of the current data change field are compared with the parameter value standard ratios. If the ratio value consistency assessment condition is met, it is considered to be the first level of similarity (the current data change field value of cc accounts for 13%, the value of cd accounts for 19%, and the value of dd accounts for 68%, which is completely consistent. At this time, the probability distribution is consistent, and the ratio allows a certain error. When the value of cc is within 13%×0.95 to 13%×1.05, it can also be regarded as consistent with the ba value ratio of the target transformation field A);

[0067] After combining the current data change fields that do not meet the first level of similarity, if the proportion of the parameter value standard meets the proportion value consistency evaluation condition (assuming that the current data change field has 6 values, the first and second values ​​together account for 13%, the third and sixth values ​​together account for 19%, and the remaining two values ​​add up to 68%, then it is considered to be a target control proportion combination that meets the proportion value consistency evaluation condition, and a certain error is also allowed, and the error range is similar to the first level), further split the evaluation period, split a day into m periods, and compare the target control proportion group of the current data change field in the m periods after the split Whether the target control ratio combination and parameter value standard ratio are consistent (for example, m=24, once an hour in a day, the target control ratio combination and parameter value standard ratio of the current data change field are compared, and each time period meets the proportion value consistency evaluation conditions, which is considered to meet the requirements; it should be noted that the combination method of the target control ratio combination is not allowed to change, and must be consistent with the combination method counted before the split time period, that is, each evaluation time period must be the parameter value ratio of the first and second parameter values ​​​​added for statistical comparison, and the value must be 13%, and the same applies to others), if consistent, it is regarded as the second level of similarity.

[0068] The results generated by the above steps are automatically organized into a code troubleshooting auxiliary report. The location information of the data change fields that are determined to meet the first-level similarity or second-level similarity are listed in the report. The first-level similarity and the second-level similarity are listed separately, and each is sorted from small to large according to the percentage difference. The percentage difference is calculated as follows:

[0069] Among them, i is the ordinal number of the parameter value ratio, R i is the proportion of the i-th value of the target transformation field, r i It is the proportion of the i-th value (or combination) of the data change field that is first similar or second similar to the target transformation field.

[0070] The code troubleshooting auxiliary report is sent to the operation and maintenance personnel. When the operation and maintenance personnel are troubleshooting, they can perform code troubleshooting based on the information listed in the code troubleshooting auxiliary report, which greatly increases the troubleshooting efficiency.

[0071] The technical solution of the embodiment of the present invention obtains the number of each target parameter value of the target transformation field, thereby determining the standard proportion of parameter values ​​based on the number of each target parameter value and the total number of parameters of the target transformation field, and counting the comparison proportion of parameter values ​​of each data change field, and then performing a similarity level assessment based on the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field to obtain a similarity level assessment result, and calculating a proportion difference set corresponding to the similarity level assessment result, and further generating a code troubleshooting auxiliary report based on the similarity level assessment result and the proportion difference set. This solution determines the standard proportion of parameter values ​​based on the ratio of the number of target parameter values ​​of the target transformation field to the total number of their corresponding parameters, and uses this proportion as the standard proportion. It compares the proportions with the parameter values ​​of other fields to evaluate the similarity of probability distribution, that is, from the perspective of probability distribution, determines the fields that need to be checked and are associated with the target transformation field, so as to generate a code troubleshooting auxiliary report. Without intruding into the financial business system, based on the code troubleshooting auxiliary report, it automatically filters out the codes for field changes on most financial business processing chains, greatly reducing the manpower cost required for troubleshooting, and solving the problems of low troubleshooting efficiency, long troubleshooting cycle and high labor cost after field transformation in the existing financial business system. It can realize automated auxiliary troubleshooting after field transformation in the financial business system, greatly reduce labor costs, and effectively improve troubleshooting efficiency.

[0072] Embodiment 3

[0073] Figure 3 This is a schematic diagram of the structure of a field modification troubleshooting auxiliary device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device comprises:

[0074] The parameter value proportion statistics module 310 is used to determine the standard proportion of the parameter value of the target transformation field, and to count the comparison proportion of the parameter value of each data change field;

[0075] The similarity level evaluation and proportion difference calculation module 320 is used to perform similarity level evaluation according to the standard proportion of parameter values ​​and the comparison proportion of parameter values ​​of each data change field, obtain a similarity level evaluation result, and calculate a proportion difference set corresponding to the similarity level evaluation result;

[0076] The report generation module 330 is used to generate a code troubleshooting auxiliary report based on the similarity level evaluation results and the percentage difference set.

[0077] The technical solution of the embodiment of the present invention determines the standard proportion of the parameter value of the target transformation field, and counts the comparison proportion of the parameter value of each data change field, thereby performing a similarity level assessment based on the standard proportion of the parameter value and the comparison proportion of the parameter value of each data change field, obtaining a similarity level assessment result, and calculating the percentage difference set corresponding to the similarity level assessment result, and then generating a code troubleshooting auxiliary report based on the similarity level assessment result and the percentage difference set. Without intruding into the financial business system, this solution automatically filters out the codes for field changes on most financial business processing chains based on the code troubleshooting auxiliary report, greatly reducing the manpower cost required for troubleshooting, and solving the problems of low troubleshooting efficiency, long troubleshooting cycle, and high labor cost after field transformation in existing financial business systems. It can realize automatic auxiliary troubleshooting after field transformation in financial business systems, greatly reducing labor costs, and effectively improving troubleshooting efficiency.

[0078] Optionally, the parameter value proportion statistics module 310 is specifically used to obtain the number of each target parameter value of the target transformation field; based on the number of each target parameter value and the total number of parameters of the target transformation field, determine the standard proportion of the parameter value.

[0079] Optionally, the similarity level evaluation result includes the first level similarity evaluation result and / or the second level similarity evaluation result; the similarity level evaluation and proportion difference calculation module 320 includes a similarity level evaluation unit and a proportion difference calculation unit. The similarity level evaluation unit is used to determine that the current data change field and the target transformation field are first-level similar when the parameter value standard proportion and the parameter value comparison proportion of the current data change field meet the proportion value consistency evaluation condition, and obtain the first level similarity evaluation result; when the parameter value standard proportion and the parameter value comparison proportion of the current data change field do not meet the proportion value consistency evaluation condition, determine the target comparison proportion combination of the current data change field according to the parameter value standard proportion; perform a second level similarity level evaluation according to the parameter value standard proportion and the target comparison proportion combination of the current data change field to obtain the second level similarity evaluation result. The proportion difference calculation unit is specifically used to perform the aforementioned operation of calculating the proportion difference set corresponding to the similarity level evaluation result.

[0080] Optionally, a similarity level assessment unit is used to combine the parameter value control ratios of the current data change field to obtain a ratio consistency assessment combination; and determine the target control ratio combination based on the parameter value standard ratios, the ratio consistency assessment combination and the ratio value consistency assessment conditions.

[0081] Optionally, a similarity level evaluation unit is used to determine multiple evaluation time periods for the second-level similarity level evaluation based on the average service timeout period and the average service link length; according to the evaluation time period, the standard proportion of the parameter value and the target control proportion of the current data change field are combined to perform a second-level similarity level evaluation to obtain the second-level similarity evaluation result.

[0082] Optionally, the report generation module 330 is specifically used to determine the location information of the target field to be checked according to the similarity level evaluation result, and generate the code troubleshooting auxiliary report based on the proportion difference set, the location information of the target field to be checked, and the similarity level evaluation result.

[0083] The field transformation and troubleshooting auxiliary device provided in the embodiment of the present invention can execute the field transformation and troubleshooting auxiliary method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0084] Embodiment 4

[0085] Figure 4 A schematic diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0086] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0087] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0088] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the field transformation troubleshooting auxiliary method.

[0089] In some embodiments, the field transformation troubleshooting auxiliary method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the field transformation troubleshooting auxiliary method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the field transformation troubleshooting auxiliary method in any other appropriate manner (for example, by means of firmware).

[0090] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0092] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0093] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0094] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0095] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of traditional physical hosts and VPS servers, which are difficult to manage and have weak business scalability.

[0096] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0097] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A field transformation troubleshooting auxiliary method, characterized in that: include: Determine the standard proportion of parameter values ​​for the target transformation fields, and calculate the comparison proportion of parameter values ​​for each data change field; Perform similarity level assessment according to the standard proportion of the parameter value and the comparison proportion of the parameter value of each of the data change fields to obtain a similarity level assessment result, and calculate a proportion difference set corresponding to the similarity level assessment result; A code troubleshooting auxiliary report is generated based on the similarity level evaluation result and the proportion difference set.

2. The method according to claim 1, characterized in that Determine the standard proportion of parameter values ​​for the target transformation fields, including: Obtaining the number of each target parameter value of the target transformation field; Based on the number of each target parameter value and the total number of parameters of the target transformation field, the standard proportion of the parameter value is determined.

3. The method according to claim 1, characterized in that The similarity level assessment results include first level similarity assessment results and / or second level similarity assessment results; A similarity level assessment is performed based on the standard proportion of the parameter value and the comparison proportion of the parameter value of each of the data change fields to obtain a similarity level assessment result, including: When the standard proportion of the parameter value and the parameter value of the current data change field are compared and the proportion meets the proportion value consistency evaluation condition, it is determined that the current data change field and the target transformation field are first-level similar, and the first-level similarity evaluation result is obtained; When the parameter value standard proportion and the parameter value comparison proportion of the current data change field do not meet the proportion value consistency evaluation condition, determine the target comparison proportion combination of the current data change field according to the parameter value standard proportion; A second level similarity level assessment is performed based on a combination of the standard proportion of the parameter value and the target comparison proportion of the current data change field to obtain the second level similarity assessment result.

4. The method according to claim 3, characterized in that: Determining the target comparison ratio combination of the current data change field according to the parameter value standard ratio includes: Combining the parameter values ​​of the current data change fields according to the proportions to obtain a proportion consistency evaluation combination; The target control proportion combination is determined according to the parameter value standard proportion, the proportion consistency evaluation combination and the proportion value consistency evaluation condition.

5. The method according to claim 3, characterized in that: According to the combination of the standard proportion of the parameter value and the target comparison proportion of the current data change field, a second-level similarity level assessment is performed to obtain the second-level similarity assessment result, including: Determining a plurality of evaluation periods for the second-level similarity level evaluation based on an average service timeout and an average service link length; According to the evaluation period, the standard proportion of the parameter value and the target control proportion of the current data change field are combined to perform a second-level similarity level evaluation to obtain the second-level similarity evaluation result.

6. The method according to claim 1, characterized in that Generate a code troubleshooting auxiliary report based on the similarity level evaluation result and the percentage difference set, including: Determine the location information of the target field to be checked according to the similarity level evaluation result; The code troubleshooting auxiliary report is generated based on the percentage difference set, the location information of the target field to be checked, and the similarity level evaluation result.

7. A field modification and troubleshooting auxiliary device, characterized in that: include: The parameter value ratio statistics module is used to determine the standard ratio of parameter values ​​of the target transformation field and to count the comparison ratio of parameter values ​​of each data change field; A similarity level assessment and proportion difference calculation module is used to perform a similarity level assessment according to the standard proportion of the parameter value and the comparison proportion of the parameter value of each of the data change fields, obtain a similarity level assessment result, and calculate a proportion difference set corresponding to the similarity level assessment result; A report generation module is used to generate a code troubleshooting auxiliary report based on the similarity level evaluation result and the proportion difference set.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the field transformation troubleshooting auxiliary method described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the field transformation and troubleshooting auxiliary method described in any one of claims 1-6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the field transformation troubleshooting auxiliary method according to any one of claims 1 to 6.

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