Software operation data transmission system and method based on digitization

Abstract

The invention discloses a software operation data transmission system and method based on digitization, and relates to the technical field of software operation data management. An error report collection mechanism is established for target software, and collected error report data are analyzed through fusion data clustering; real-time changes of defect risk coefficients are monitored for various defects occurring in each software function item, whether each software function item of target software presents regular trend development on a certain defect or not is analyzed, and in the process of analyzing the defect risk coefficients, reasons except the defects of the software are excluded to a certain extent, so that the defect risk coefficients of the target software are analyzed. Scientific software fault report data management is established for the target software, and meanwhile, data support is provided for the management terminal to make an update decision about whether to aim at the corresponding software function item of the target software or not.

Description

Technical Field

[0001] The present invention relates to the technical field of software operation data management, and in particular to a digital-based software operation data transmission system and method. Background Art

[0002] A software test case is a description of a test task for a specific software product. It embodies the test plan, method, technique and strategy. A test case is a set of test inputs, execution conditions and expected results compiled for a specific goal, used to verify whether a specific software requirement is met. Test cases play a vital role in software testing. They are the basis and foundation of software testing and are used to verify whether the software meets the expected requirements. Test cases mainly contain four contents: test case title, preconditions, test steps and expected results. The test case title mainly describes the test of a certain function; the precondition refers to the condition that the test case title needs to meet; the test steps mainly describe the operation steps of the test case; the expected result refers to the compliance with the expected requirements (development specification, requirement document, user requirements, etc.).

[0003] There are many reasons for software failures, including defects in the software itself, data problems, environmental factors, user operation errors, and incompatible system updates or upgrades. Solutions to these problems include establishing an error report collection mechanism, analyzing error reports to locate the cause of the failure, providing temporary solutions, and releasing updates after the failure is fixed. Therefore, it is particularly important to conduct intelligent transmission management of software failure error data. Summary of the invention

[0004] The object of the present invention is to provide a digital software-based data transmission system and method to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for transmitting data based on digital software operation, the method comprising:

[0006] Step S1: After the target software is put into use, every time an application fault error data is received from a user terminal for the target software, a running fault record is generated in the management terminal of the target software; the software test cases written and passed by the test platform for the target software are obtained, and the software function items tested by each software test case for the target software are obtained from the test case title of each software test case;

[0007] Step S2: receiving and storing characteristic fault records corresponding to each software function item of the target software in real time, generating a defect database corresponding to each software function item; setting a trigger condition for generating a record cluster center in the defect database of each software function item;

[0008] Step S3: The defect database of each software function item, the remaining characteristic fault records after the corresponding record clustering center is constructed, and the characteristic fault records that are newly received and stored in the future are set as the records to be clustered of each software function item;

[0009] Step S4: sorting out the application fault error data corresponding to the records to be clustered in each defect database, and the similarity distribution between the attribute data corresponding to the current record cluster center, clustering each defect database according to the similarity distribution, and determining whether to construct a new record cluster center;

[0010] Step S5: By combing the cluster distribution presented by each record cluster center in each defect database, the defect risk coefficient presented by each record cluster center is evaluated and calculated;

[0011] Step S6: monitor the change of the defect risk coefficient of each record cluster center in each defect database in real time, and assist the management terminal in making an update decision on the corresponding software function item.

[0012] Preferably, step S2 comprises:

[0013] Step S2-1: if a software function item is diagnosed as having an operation abnormality after the management terminal performs fault analysis on the application fault error data in a certain operation fault record, the certain operation fault record is determined to be a characteristic fault record of the software function item;

[0014] Step S2-2: When it is detected in real time that card(Q)≧β>0 in the defect database Q of a certain software function item, a trigger is generated to construct F record cluster centers for the certain software function item, where card(Q) represents the total number of characteristic fault records contained in the defect database Q, β is the total number threshold, and card(Q)≧F≧1;

[0015] Step S2-3: For each software function item, randomly select F characteristic fault records from the current defect database, and import them into each corresponding record cluster center respectively. At the same time, the application fault error data corresponding to the only characteristic fault record in each record cluster center at this time is used as the attribute data of each record cluster center; wherein, if F>1, it must satisfy that among the application fault error data corresponding to the F characteristic fault records, the similarity between the application fault error data corresponding to any two characteristic fault records is less than the similarity threshold.

[0016] Preferably, step S4 comprises:

[0017] Step S4-1: extract the application fault error data corresponding to each to-be-clustered record of each software function item one by one, and compare the application fault error data corresponding to each to-be-clustered record with the attribute data of the record cluster center existing in the current defect database of each software function item in turn for similarity; before the record cluster center is newly constructed, the total number of record cluster centers that need to be compared is F, and after the record cluster center is newly constructed, the total number of record cluster centers that need to be compared is greater than F, that is, including the F record cluster centers generated at the beginning and the newly added record cluster center;

[0018] Step S4-2: for each to-be-clustered record of each software function item, the total number of record clustering centers that satisfy the similarity between the corresponding attribute data and the application fault error data of each to-be-clustered record is greater than the similarity threshold value is accumulated;

[0019] Step S4-3: Assume that the total number of the i-th record to be clustered of a certain software function item is K. When K≧1, it triggers the integration of the i-th record to be clustered into a unique record clustering center or a record clustering center with the largest similarity. When K=0, it triggers the creation of a new record clustering center for a certain software function item. When the i-th record to be clustered is integrated into the newly added record clustering center, the application fault error data corresponding to the i-th record to be clustered is used as the attribute data of the newly added record clustering center.

[0020] Step S4-4: looping through steps S4-1 to S4-3 in the defect database of each software function item until all the records to be clustered in the defect database of each software function item have a target record clustering center to be imported.

[0021] Among them, if after determining a new record clustering center for a certain software function item in the i-th record to be clustered, for the i+1-th record to be clustered of the certain software function item, the total number of record clustering centers that satisfy the similarity between the corresponding attribute data and the application fault error data of the i+1-th record to be clustered is greater than the similarity threshold within the range of all record clustering centers after covering the new record clustering center.

[0022] Preferably, step S5 comprises:

[0023] Step S5-1: Arrange the characteristic fault records collected by each record clustering center in each defect database in real time according to the order of record generation time to obtain a record sequence corresponding to each record clustering center;

[0024] Step S5-2: in each record sequence, extract the interval generation time t between each two adjacent characteristic fault records in turn, obtain the average interval generation time T corresponding to each record sequence, collect in real time the total number of user terminals N involved in feeding back and uploading application fault error data once in each record sequence, and the total number of characteristic fault records U contained in each record sequence; calculate the defect risk coefficient α presented by each record cluster center = (1 / T) × (N / U);

[0025] Among them, a record cluster center can represent a defect problem existing in a software function item. Among them, if the T corresponding to the characteristic fault record sequence in a record cluster center is longer, it means that the randomness of the user end reporting data errors for this defect problem is greater, and the tendency of the defect problem to become a stable hidden danger is smaller. If the N / U corresponding to the characteristic fault record sequence in a record cluster center is larger, it means that the randomness of the user end involved in feedback of the defect problem is stronger, that is, the possibility of the same user end repeatedly feedbacking this defect problem is lower. Because if the same user end repeatedly feedbacks this defect problem, it means that the possibility of this defect problem is not due to the objective existence of this defect in the software, but is only caused by improper use of the user end.

[0026] Preferably, step S6 includes sending an update warning prompt for a certain software function item to the management terminal when the defect risk coefficient of a certain record cluster center is monitored to be greater than the coefficient threshold in the defect database of a certain software function item; at the same time, all characteristic fault records covered by a certain record cluster center are transmitted to the management terminal to assist the management terminal in making an update decision on whether to target a certain software function item.

[0027] In order to better implement the above method, a software operation data transmission system is also proposed, which includes: a software operation data transmission management module, a cluster center trigger construction management module, a cluster center management module, a defect risk coefficient evaluation calculation module, and an update warning prompt sending management module;

[0028] The software operation data transmission management module is used to receive the operation fault record generated in the management terminal of the target software after the target software is put into use; wherein, each time an application fault error data is received from a user end for feedback and upload of the target software, an operation fault record is generated in the management terminal of the target software; the software test cases written and passed by the test platform for the target software are obtained, and the software function items targeted by each software test case for the target software are obtained from the test case title of each software test case;

[0029] The cluster center trigger construction management module is used to receive and store the characteristic fault records corresponding to each software function item of the target software in real time, and generate a defect database corresponding to each software function item; set the trigger conditions for constructing and generating the record cluster center in the defect database of each software function item;

[0030] The cluster center management module is used to set the defect database of each software function item, the remaining characteristic fault records after the corresponding construction and generation of the record cluster center, and the characteristic fault records that will be newly received and stored in the future, as the records to be clustered of each software function item; sort out the application fault error data corresponding to the records to be clustered in each defect database, and the similarity distribution between the attribute data corresponding to the current record cluster center, perform clustering processing on each defect database according to the similarity distribution, and make a judgment on whether to build a new record cluster center;

[0031] The defect risk coefficient evaluation and calculation module is used to evaluate and calculate the defect risk coefficient presented by each record cluster center by combing the cluster distribution presented by each record cluster center in each defect database;

[0032] The update warning prompt sending management module is used to monitor the changes in the defect risk coefficient of each record cluster center in each defect database in real time, and assist the management terminal in making an update decision on whether to target the corresponding software function items.

[0033] Preferably, the cluster center management module includes a cluster judgment management unit and a new addition judgment unit;

[0034] The clustering judgment management unit is used to perform clustering processing on each defect database according to the similarity distribution between the application fault error data corresponding to all the records to be clustered in each defect database and the attribute data corresponding to the cluster center of the current record;

[0035] A new judgment unit is added to judge whether to construct a new record clustering center for each defect database based on the similarity distribution between the application fault error data corresponding to all the records to be clustered in each defect database and the attribute data corresponding to the current record clustering center.

[0036] Preferably, the defect risk coefficient evaluation and calculation module includes a distribution combing unit and a calculation and evaluation unit;

[0037] A distribution combing unit, used to comb the cluster distribution presented by each record cluster center in each defect database;

[0038] The calculation and evaluation unit is used to receive the data in the distribution combing unit and evaluate and calculate the defect risk coefficient presented by each record cluster center.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention establishes an error report collection mechanism for the target software, and analyzes the collected error report data through fusion data clustering analysis means, monitors the real-time changes of the defect risk coefficient of various defects occurring in each software function item, and analyzes whether each software function item of the target software shows a regular trend of development in a certain defect, and in the process of analyzing the defect risk coefficient, excludes to a certain extent the reasons other than the defects of the software itself, and at the same time realizes the establishment of scientific software fault error data management for the target software, provides data support for the management terminal when making an update decision on the corresponding software function item of the target software. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of a flow chart of a digital software-based data transmission method of the present invention;

[0041] Figure 2 The present invention is a structural schematic diagram of a digital software-based data transmission system. DETAILED DESCRIPTION

[0042] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0043] Example: Figure 1-Figure 2 As shown, the present invention provides a digital software-based data transmission method, the method comprising:

[0044] Step S1: After the target software is put into use, every time an application fault error data is received from a user terminal for the target software, a running fault record is generated in the management terminal of the target software; the software test cases written and passed by the test platform for the target software are obtained, and the software function items tested by each software test case for the target software are obtained from the test case title of each software test case;

[0045] Step S2: receiving and storing characteristic fault records corresponding to each software function item of the target software in real time, generating a defect database corresponding to each software function item; setting a trigger condition for generating a record cluster center in the defect database of each software function item;

[0046] Wherein, step S2 comprises:

[0047] Step S2-1: if a software function item is diagnosed as having an operation abnormality after the management terminal performs fault analysis on the application fault error data in a certain operation fault record, the certain operation fault record is determined to be a characteristic fault record of the software function item;

[0048] Step S2-2: When it is detected in real time that card(Q)≧β>0 in the defect database Q of a certain software function item, a trigger is generated to construct F record cluster centers for the certain software function item, where card(Q) represents the total number of characteristic fault records contained in the defect database Q, β is the total number threshold, and card(Q)≧F≧1;

[0049] Step S2-3: For each software function item, randomly select F characteristic fault records from the current defect database, and import them into each corresponding record cluster center respectively, and use the application fault error data corresponding to the only characteristic fault record in each record cluster center at this time as the attribute data of each record cluster center; wherein, if F>1, it must be satisfied that among the application fault error data corresponding to the F characteristic fault records, the similarity between any two application fault error data corresponding to the characteristic fault records is less than the similarity threshold;

[0050] Step S3: The defect database of each software function item, the remaining characteristic fault records after the corresponding record clustering center is constructed, and the characteristic fault records that are newly received and stored in the future are set as the records to be clustered of each software function item;

[0051] Step S4: sorting out the application fault error data corresponding to the records to be clustered in each defect database, and the similarity distribution between the attribute data corresponding to the current record cluster center, clustering each defect database according to the similarity distribution, and determining whether to construct a new record cluster center;

[0052] Wherein, step S4 comprises:

[0053] Step S4-1: extracting application fault error data corresponding to each to-be-clustered record of each software function item one by one, and comparing the application fault error data corresponding to each to-be-clustered record with the attribute data of the record cluster center existing in the current defect database of each software function item in turn for similarity;

[0054] Step S4-2: for each to-be-clustered record of each software function item, the total number of record clustering centers that satisfy the similarity between the corresponding attribute data and the application fault error data of each to-be-clustered record is greater than the similarity threshold value is accumulated;

[0055] Step S4-3: Assume that the total number of the i-th record to be clustered of a certain software function item is K. When K≧1, it triggers the integration of the i-th record to be clustered into a unique record clustering center or a record clustering center with the largest similarity. When K=0, it triggers the creation of a new record clustering center for a certain software function item. When the i-th record to be clustered is integrated into the newly added record clustering center, the application fault error data corresponding to the i-th record to be clustered is used as the attribute data of the newly added record clustering center.

[0056] Step S4-4: looping through steps S4-1 to S4-3 in the defect database of each software function item until all the records to be clustered in the defect database of each software function item have a target imported record cluster center;

[0057] For example, β=4, when it is detected in real time that there are more than 4 characteristic fault records in the defect database Q of a certain software function item, it is triggered to build F record cluster centers for the certain software function item. If there are 6 characteristic fault records in the current defect database Q, where F can be selected in the range of 6≧F≧1; if the similarity between the application fault error data corresponding to any two characteristic fault records is less than the similarity threshold, three record cluster centers are selected to be built for the certain software function item;

[0058] Therefore, the remaining three characteristic fault records in the defect database Q, as well as the characteristic fault records that will be newly received and stored in the future, are all records to be clustered of each software function item;

[0059] If within the range of three record cluster centers, the total number of record cluster centers whose corresponding attribute data and the application fault error data of the first record to be clustered satisfy a similarity greater than the similarity threshold is 2, namely the first record cluster center and the third record cluster center; among which, the similarity between the attribute data of the first record cluster center and the application fault error data of the first record to be clustered is greater than the similarity between the attribute data of the third record cluster center and the application fault error data of the first record to be clustered, therefore, it can be known that the first record to be clustered should be merged into the first record cluster center;

[0060] If within the range of three record cluster centers, the total number of record cluster centers whose similarity between the corresponding attribute data and the application fault error data of the second record to be clustered is greater than the similarity threshold is 0, a new record cluster center is added to a certain software function item;

[0061] In summary, when analyzing the similarity between the corresponding attribute data and the application fault error data of the third record to be clustered, the range of the reference record cluster center should include the record cluster center newly added based on the second record to be clustered; therefore, it can be known that the total number of record cluster centers that meet the similarity greater than the similarity threshold should be accumulated within the range of 4 record cluster centers. If the total number is greater than 0, repeat the above clustering judgment method. If the total number is equal to 0, then trigger the addition of a record cluster center for a certain software function item, and repeat the above clustering judgment method or add a new record cluster center after waiting for the fourth record to be clustered to be newly received and stored in the defect database Q;

[0062] Step S5: By combing the cluster distribution presented by each record cluster center in each defect database, the defect risk coefficient presented by each record cluster center is evaluated and calculated;

[0063] Wherein, step S5 comprises:

[0064] Step S5-1: Arrange the characteristic fault records collected by each record clustering center in each defect database in real time according to the order of record generation time to obtain a record sequence corresponding to each record clustering center;

[0065] Step S5-2: in each record sequence, extract the interval generation time t between each two adjacent characteristic fault records in turn, obtain the average interval generation time T corresponding to each record sequence, collect in real time the total number of user terminals N involved in feeding back and uploading application fault error data once in each record sequence, and the total number of characteristic fault records U contained in each record sequence; calculate the defect risk coefficient α presented by each record cluster center = (1 / T) × (N / U);

[0066] Step S6: monitor the defect risk coefficient changes of each record cluster center in each defect database in real time, and assist the management terminal in making an update decision on the corresponding software function item;

[0067] Among them, step S6 includes sending an update warning prompt for a certain software function item to the management terminal when the defect risk coefficient of a certain record cluster center is monitored to be greater than the coefficient threshold in the defect database of a certain software function item; at the same time, all characteristic fault records covered by a certain record cluster center are transmitted to the management terminal to assist the management terminal in making an update decision on whether to target a certain software function item.

[0068] In order to better implement the above method, a software operation data transmission system is also proposed, which includes: a software operation data transmission management module, a cluster center trigger construction management module, a cluster center management module, a defect risk coefficient evaluation calculation module, and an update warning prompt sending management module;

[0069] The software operation data transmission management module is used to receive the operation fault record generated in the management terminal of the target software after the target software is put into use; wherein, each time an application fault error data is received from a user end for feedback and upload of the target software, an operation fault record is generated in the management terminal of the target software; the software test cases written and passed by the test platform for the target software are obtained, and the software function items targeted by each software test case for the target software are obtained from the test case title of each software test case;

[0070] The cluster center trigger construction management module is used to receive and store the characteristic fault records corresponding to each software function item of the target software in real time, and generate a defect database corresponding to each software function item; set the trigger conditions for constructing and generating the record cluster center in the defect database of each software function item;

[0071] The cluster center management module is used to set the defect database of each software function item, the remaining characteristic fault records after the corresponding construction and generation of the record cluster center, and the characteristic fault records that will be newly received and stored in the future, as the records to be clustered of each software function item; sort out the application fault error data corresponding to the records to be clustered in each defect database, and the similarity distribution between the attribute data corresponding to the current record cluster center, perform clustering processing on each defect database according to the similarity distribution, and make a judgment on whether to build a new record cluster center;

[0072] Among them, the cluster center management module includes a cluster judgment management unit and a new judgment unit;

[0073] The clustering judgment management unit is used to perform clustering processing on each defect database according to the similarity distribution between the application fault error data corresponding to all the records to be clustered in each defect database and the attribute data corresponding to the cluster center of the current record;

[0074] A new judgment unit is added to judge whether to construct a new record clustering center for each defect database based on the similarity distribution between the application fault error data corresponding to all the records to be clustered in each defect database and the attribute data corresponding to the current record clustering center.

[0075] The defect risk coefficient evaluation and calculation module is used to evaluate and calculate the defect risk coefficient presented by each record cluster center by combing the cluster distribution presented by each record cluster center in each defect database;

[0076] Among them, the defect risk coefficient evaluation and calculation module includes a distribution combing unit and a calculation evaluation unit;

[0077] A distribution combing unit, used to comb the cluster distribution presented by each record cluster center in each defect database;

[0078] The calculation and evaluation unit is used to receive the data in the distribution combing unit and evaluate and calculate the defect risk coefficient presented by each record cluster center.

[0079] The update warning prompt sending management module is used to monitor the changes in the defect risk coefficient of each record cluster center in each defect database in real time, and assist the management terminal in making an update decision on whether to target the corresponding software function items.

[0080] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital software-based data transmission method, Features: The method comprises: Step S1: After the target software is put into use, every time an application fault error data is received from a user terminal for the target software, a running fault record is generated in the management terminal of the target software; the software test cases written and passed by the test platform for the target software are obtained, and the software function items tested by each software test case for the target software are obtained from the test case title of each software test case; Step S2: receiving and storing characteristic fault records corresponding to each software function item of the target software in real time, generating a defect database corresponding to each software function item; setting a trigger condition for generating a record cluster center in the defect database of each software function item; Step S3: the defect database of each software function item, the remaining characteristic fault records after the corresponding record clustering center is constructed, and the characteristic fault records newly received and stored in the future are set as the records to be clustered of the software function items; Step S4: sorting out the application fault error data corresponding to the records to be clustered in each defect database, and the similarity distribution between the attribute data corresponding to the current record cluster center, clustering each defect database according to the similarity distribution, and determining whether to construct a new record cluster center; Step S5: By combing the cluster distribution presented by each record cluster center in each defect database, the defect risk coefficient presented by each record cluster center is evaluated and calculated; Step S6: monitor the change of the defect risk coefficient of each record cluster center in each defect database in real time, and assist the management terminal in making an update decision on the corresponding software function item.

2. A digital software-based data transmission method according to claim 1, Features: The step S2 comprises: Step S2-1: if a software function item is diagnosed as having an operation abnormality after the management terminal performs fault analysis on the application fault error data in a certain operation fault record, it is determined that the certain operation fault record is a characteristic fault record of the certain software function item; Step S2-2: When it is monitored in real time that card(Q)≧β>0 is satisfied in the defect database Q of a certain software function item, triggering the construction of F record cluster centers for the certain software function item, where card(Q) represents the total number of characteristic fault records contained in the defect database Q, β is the total number threshold, and card(Q)≧F≧1; Step S2-3: For each software function item, randomly select F characteristic fault records from the current defect database, and import them into each corresponding record cluster center respectively, and at the same time use the application fault error data corresponding to the only characteristic fault record in each record cluster center at this time as the attribute data of each record cluster center; wherein, if F>1, it must satisfy that among the application fault error data corresponding to the F characteristic fault records, the similarity between the application fault error data corresponding to any two characteristic fault records is less than the similarity threshold.

3. A digital software-based data transmission method according to claim 2, Features: The step S4 comprises: Step S4-1: extracting application fault error data corresponding to each to-be-clustered record of each software function item one by one, and comparing the application fault error data corresponding to each to-be-clustered record with the attribute data of the record cluster center existing in the current defect database of each software function item in turn for similarity; Step S4-2: for each to-be-clustered record of each software function item, the total number of record clustering centers that satisfy the similarity between the corresponding attribute data and the application fault error data of each to-be-clustered record is greater than the similarity threshold value is accumulated; Step S4-3: Assume that the total number of the i-th record to be clustered of a certain software function item is K. When K≧1, it triggers the integration of the i-th record to be clustered into a unique record clustering center or a record clustering center with the largest similarity. When K=0, it triggers the creation of a new record clustering center for the certain software function item. When the i-th record to be clustered is integrated into the newly added record clustering center, the application fault error data corresponding to the i-th record to be clustered is used as the attribute data of the newly added record clustering center. Step S4-4: looping through steps S4-1 to S4-3 in the defect database of each software function item until all the records to be clustered in the defect database of each software function item have a target record clustering center to be imported.

4. A digital software-based data transmission method according to claim 3, Features: The step S5 comprises: Step S5-1: Arrange the characteristic fault records collected by each record clustering center in each defect database in real time according to the order of record generation time to obtain a record sequence corresponding to each record clustering center; Step S5-2: In each record sequence, extract the interval generation time t between each two adjacent characteristic fault records in turn, obtain the average interval generation time T corresponding to each record sequence, and collect in real time the total number N of user terminals involved in feeding back and uploading application fault error data once in each record sequence, as well as the total number U of characteristic fault records contained in each record sequence; calculate the defect risk coefficient α presented by each record cluster center = (1 / T) × (N / U).

5. A digital software-based data transmission method according to claim 4, Features: The step S6 includes sending an update warning prompt for a certain software function item to the management terminal when the defect risk coefficient of a certain record cluster center is monitored to be greater than the coefficient threshold in the defect database of a certain software function item; at the same time, all characteristic fault records covered by the certain record cluster center are transmitted to the management terminal to assist the management terminal in making an update decision on whether to target a certain software function item.

6. A software operation data transmission system, used to execute a digital software operation data transmission method according to any one of claims 1 to 5, Features: The system includes: a software operation data transmission management module, a cluster center trigger construction management module, a cluster center management module, a defect risk coefficient evaluation calculation module, and an update warning prompt sending management module; The software operation data transmission management module is used to receive the operation fault record generated in the management terminal of the target software after the target software is put into use; wherein, each time an application fault error data is received from a user end for feedback and upload of the target software, an operation fault record is generated in the management terminal of the target software; the software test cases written and passed by the test platform for the target software are obtained, and the software function items targeted by each software test case for the target software are obtained from the test case title of each software test case; The cluster center trigger construction management module is used to receive and store the characteristic fault records corresponding to each software function item of the target software in real time, and generate a defect database corresponding to each software function item; and set the trigger conditions for constructing and generating the record cluster center in the defect database of each software function item; The cluster center management module is used to set the defect database of each software function item, the remaining characteristic fault records after the corresponding construction and generation of the record cluster center, and the characteristic fault records that will be newly received and stored in the future, as the records to be clustered of the software function items; sort out the application fault error data corresponding to the records to be clustered in each defect database, and the similarity distribution between the attribute data corresponding to the current record cluster center, perform clustering processing on each defect database according to the similarity distribution, and make a judgment on whether to newly build a record cluster center; The defect risk coefficient evaluation and calculation module is used to evaluate and calculate the defect risk coefficient presented by each record cluster center by combing the cluster distribution presented by each record cluster center in each defect database; The update warning prompt sending management module is used to monitor the defect risk coefficient changes of each record cluster center in each defect database in real time, and assist the management terminal in making an update decision on whether to target the corresponding software function item.

7. A software operation data transmission system according to claim 6, Features: The cluster center management module includes a cluster judgment management unit and a new addition judgment unit; The clustering judgment management unit is used to perform clustering processing on each defect database according to the similarity distribution between the application fault error data corresponding to all the records to be clustered in each defect database and the attribute data corresponding to the cluster center of the current record; The newly added judgment unit is used to judge whether to construct a new record clustering center for each defect database based on the similarity distribution between the application fault error data corresponding to all the records to be clustered in each defect database and the attribute data corresponding to the current record clustering center.

8. A software operation data transmission system according to claim 6, Features: The defect risk coefficient evaluation and calculation module includes a distribution combing unit and a calculation and evaluation unit; The distribution combing unit is used to comb the cluster distribution presented by each record cluster center in each defect database; The calculation and evaluation unit is used to receive the data in the distribution combing unit and evaluate and calculate the defect risk coefficient presented by each record cluster center.

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