A data-driven low-code generation method

Abstract

This invention discloses a data-driven low-code generation method in the field of software development. It addresses the inefficiency of existing low-code generation methods. The method includes the following steps: S1: acquiring basic data of the project to be developed; S2: analyzing the basic data to obtain project development classification data; S3: matching code development sub-models based on the project development classification data and generating project code through the code development sub-models; and S4: testing the project code and providing feedback on the test results. This invention, by acquiring and analyzing the basic data of the project to be developed to rationally select a code development model, can effectively alleviate problems such as low application performance, slow response speed, and poor user experience.

Description

Technical Field

[0001] The present invention belongs to the field of software development and relates to low-code generation technology, specifically a data-driven low-code generation method. Background Art

[0002] As the demand for software development increases, enterprises and developers seek faster and more efficient solutions to meet market changes. Traditional software development methods often require a lot of manual coding, which is not only time-consuming but also prone to errors. As an emerging solution, low-code platforms accelerate the application development process and reduce the complexity of development by minimizing the need for manual coding.

[0003] The existing low-code generation methods have the following defects:

[0004] 1. The existing low-code generation methods do not make reasonable choices for code models, resulting in low performance and slow response of generated applications, which affects user experience.

[0005] 2. When testing the generated code, existing low-code generation methods often perform functional tests on the code without performing time complexity and space complexity analysis, which usually generates a large amount of redundant code;

[0006] To this end, we propose a data-driven low-code generation method. Summary of the invention

[0007] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a data-driven low-code generation method. The present invention is based on obtaining basic data of the project to be developed, calculating the project development variation coefficient according to the cumulative demand data of the project, performing a threshold judgment on the project development variation coefficient, and obtaining the first-stage project classification data. The project development risk coefficient is calculated according to the first-stage project classification data and the project risk data, and a threshold judgment is performed on the project development risk coefficient to obtain the second-stage project classification data. The first-stage project classification data and the second-stage project classification data are defined as project development classification data, and a code development sub-model is matched according to the project development classification data. The code development sub-model is used to generate project code through the project content data, and the project code is run tested. An early warning is issued according to the test results, and the code complexity reference coefficient corresponding to the normally running project code is obtained. The project code is tested by analyzing the code complexity reference coefficient.

[0008] In order to achieve the above purpose, the present invention adopts the following technical solution, a data-driven low-code generation method also includes the following specific steps:

[0009] Step S1: Establishing a graphical user interaction interface, obtaining project content data, project cumulative demand data and project risk data through the graphical user interaction interface, and obtaining basic data of the project to be developed;

[0010] Step S2: Calculate the project development variation coefficient according to the project cumulative demand data, perform threshold judgment on the project development variation coefficient, and obtain the first-stage project classification data; calculate the project development risk coefficient according to the first-stage project classification data and the project risk data; perform threshold judgment on the project development risk coefficient, and obtain the second-stage project classification data; define the first-stage project classification data and the second-stage project classification data as the project development classification data;

[0011] Step S3: matching the code development sub-model according to the project development classification data, and using the code development sub-model to generate project code through the project content data;

[0012] Step S4: Run a test on the project code, issue an early warning based on the test results, obtain a code complexity reference coefficient corresponding to the normally running project code, and implement a test on the project code by analyzing the code complexity reference coefficient.

[0013] Furthermore, in step S1, the specific steps for acquiring the basic data of the project to be developed are as follows:

[0014] It also includes a database, the data stored in the database include the project capital investment amount corresponding to the project to be developed and the operation speed of the established project;

[0015] Step S11: Establish a data structure sub-interface, a business requirement sub-interface, and an API custom sub-interface through a front-end development tool to obtain a graphical user interaction interface, and obtain project requirement content data, project structure data, and parameter definition data through the graphical user interaction interface to obtain project content data;

[0016] Step S12: Obtaining the project cumulative demand data through the business demand sub-interface;

[0017] Step S13: Obtain project risk data through the data structure sub-interface;

[0018] Step S14: define the project content data, project risk data and project cumulative demand data as basic data of the project to be developed.

[0019] Furthermore, in step S12, the specific steps for obtaining the project cumulative demand data are as follows:

[0020] Step S121: before the project is developed, m characteristic demand monitoring points are selected and marked as the first characteristic demand monitoring point to the mth characteristic demand monitoring point respectively;

[0021] Step S122: Obtain project requirement content data through the business requirement sub-interface,

[0022] Step S123: acquiring the project demand data volume corresponding to the first characteristic demand monitoring point through the business demand sub-interface;

[0023] The step S123 is specifically as follows:

[0024] Step S1231: obtaining the number of user operations on the business requirement sub-interface corresponding to the first characteristic requirement monitoring point through the page access record to obtain the first user operation number;

[0025] Step S1232: Perform text recognition on the project demand content data corresponding to the first characteristic demand monitoring point by using a text recognition tool to obtain the total number of demands corresponding to the first characteristic demand monitoring point, thereby obtaining the first total number of demands;

[0026] Step S1233: obtaining the amount of attachment data in the project requirement content data corresponding to the first characteristic requirement monitoring point through the attachment access tool to obtain the first attachment data amount;

[0027] Step S1234: defining the first user operation number, the first total demand, and the first attachment data volume as the first project demand data volume;

[0028] Step S124: Repeat the process of acquiring the demand data volume of the first project, respectively acquire the first to m-th user operation numbers, the first to m-th demand totals, and the first to m-th attachment data volumes to obtain the project cumulative demand data.

[0029] Furthermore, in step S13, the specific steps for obtaining project risk data are as follows:

[0030] Step S131: Obtain project structure data corresponding to the project to be developed through the data structure sub-interface, and count the number of structure levels through the project structure data to obtain the project structure level quantity;

[0031] Step S132: Scan the data structure sub-interface through the function point recognition program to obtain the number of function points existing in the data structure sub-interface, and obtain the number of project function points;

[0032] Step S133: Acquire parameter definition data, perform character string recognition on the parameter definition data using a text recognition tool, and count the number of definition parameters existing in the character string to obtain the number of project parameter definitions;

[0033] Step S134: Calculate the number of project parameter definitions, the number of project function points, and the number of project structure levels to obtain a project complexity reference coefficient;

[0034] Step S135: obtaining the project capital investment amount and the established project operation speed corresponding to the project to be developed through the database;

[0035] Step S136: define the project capital investment amount, the established project operation speed and the project complexity reference coefficient as project risk data.

[0036] Furthermore, in step S2, the project development classification data is obtained, and the specific steps are as follows:

[0037] It also includes a database, wherein the data stored in the database also includes a threshold value for cumulative change in attachment data, a threshold value for cumulative total change in user requirements, a threshold value for cumulative number of user operations, a threshold value for reference coefficient of project complexity, a threshold value for project capital investment, and a threshold value for operation speed of a given project;

[0038] Step S21: Obtain basic data of the project to be developed, and obtain the project cumulative demand data based on the project to be developed;

[0039] Step S22: Obtaining the project development variation coefficient;

[0040] Step S23: obtaining a threshold value of a project development variation coefficient, comparing the project development variation coefficient with the threshold value of the project development variation coefficient, and obtaining the first-stage project classification data;

[0041] Step S24: Analyze the development projects corresponding to the second project development interval to obtain the second-stage project classification data;

[0042] The step S23 is specifically as follows:

[0043] Step S231: extracting the attachment data cumulative change threshold, the user demand cumulative change total threshold and the user cumulative operation number threshold from the database;

[0044] Step S232: Calculate the threshold of the cumulative change amount of attachment data, the threshold of the total number of cumulative changes in user requirements, and the threshold of the cumulative number of user operations to obtain a threshold of the project development change coefficient;

[0045] Step S233: when the project development variation coefficient is greater than or equal to the project development variation coefficient threshold, it is determined that the project is in the first project development interval;

[0046] Step S234: When the project development variation coefficient is less than the project development variation coefficient threshold, it is determined that the project is in the second project development interval.

[0047] Furthermore, in step S22, the specific steps for obtaining the project development variation coefficient are as follows:

[0048] Step S221: Obtaining the project cumulative demand data to obtain the first to mth user operation numbers, the first to mth demand totals, and the first to mth attachment data volumes;

[0049] Step S222: summing the first to mth user operation numbers to obtain the cumulative number of user operations;

[0050] Step S223: Calculate the total number of the first to mth demands to obtain the total number of cumulative changes in user demands;

[0051] Step S224: Calculate the first to mth attachment data amounts to obtain the attachment data cumulative change amount;

[0052] Step S225: Calculate the cumulative change amount of the attachment data, the cumulative total change amount of user requirements, and the cumulative number of user operations to obtain the project development change coefficient.

[0053] Furthermore, in step S24, the specific steps for obtaining the second-stage project classification data are as follows:

[0054] Step S241: obtaining project risk data corresponding to the second project development interval based on the project to be developed;

[0055] Step S242: Obtain the project complexity reference coefficient, the project capital investment amount and the established project operation speed according to the project risk data and calculate the project development risk coefficient;

[0056] Step S243: obtaining a threshold value of a project development risk coefficient, comparing the project development risk coefficient with the threshold value of the project development risk coefficient, and obtaining the second-stage project classification data;

[0057] The step S243 is specifically as follows:

[0058] Step S2431: extracting a project complexity reference coefficient threshold, a project capital investment threshold, and a predetermined project operation speed threshold from a database;

[0059] Step S2432: Calculate the project development risk coefficient threshold by using the project complexity reference coefficient threshold, the project capital investment threshold, and the established project operation speed threshold;

[0060] Step S2433: when the project development risk coefficient is greater than or equal to the project development risk coefficient threshold, it is determined that the project is in the third project development interval;

[0061] Step S2434: When the project development risk coefficient is less than the project development risk coefficient threshold, it is determined that the project is in the fourth project development interval.

[0062] Furthermore, in step S3, generating the project code also includes the following specific steps:

[0063] Step S31: Obtain project development classification data;

[0064] Step S32: obtaining an existing code generation model and remotely connecting it to the graphical user interaction interface, wherein the code generation model obtains project content data from the graphical user interaction interface;

[0065] Step S33: the code generation model matches the code development sub-model according to the project development classification data, and generates the project code according to the project content data;

[0066] The step S33 is specifically as follows:

[0067] Step S331: When the project is in the first project development interval, match it to the agile development sub-model;

[0068] Step S332: When the project is in the third project development interval, match it to the iterative development sub-model;

[0069] Step S333: When the project is in the fourth project development interval, it is matched to the waterfall development sub-model.

[0070] Furthermore, in step S4, the project code is tested and the test results are fed back. The specific steps are as follows:

[0071] Also included is a database, where the data stored in the database also includes a code time complexity threshold and a code space complexity threshold;

[0072] Step S41: obtaining a project code, linking the project code to a first test program, and performing a running test on the project code through the first test program;

[0073] Step S411: If the project code can run normally, determine that the project code is a normal running code;

[0074] Step S412: If the project code cannot run normally, the project code is judged to be an abnormal running code, and the first test program issues an abnormal warning;

[0075] Step S42: Performing a complexity test on the normal running code;

[0076] Step S421: Obtain the accumulated running time of the code corresponding to the normal running code through the second test program, and mark it as the code time complexity;

[0077] Step S422: obtaining the memory usage of the normal running code during the running process through the third test program, and marking it as the code space complexity;

[0078] Step S423: Calculate the code time complexity and the code space complexity to obtain a code complexity reference coefficient;

[0079] Step S424: Obtain a code complexity reference coefficient threshold, and compare the code complexity reference coefficient with the code complexity reference coefficient threshold.

[0080] Furthermore, the step S424 further includes the following steps:

[0081] Step S4241: extracting a code time complexity threshold and a code space complexity threshold from a database;

[0082] Step S4242: Calculate the code time complexity threshold and the code space complexity threshold to obtain a code complexity reference coefficient threshold;

[0083] Step S4243: When the code complexity reference coefficient is greater than or equal to the code complexity reference coefficient threshold, the system performs a code optimization pop-up window;

[0084] Step S4244: When the code complexity reference coefficient is less than the code complexity reference coefficient threshold, code generation is completed.

[0085] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0086] 1. The present invention obtains basic data of the project to be developed, analyzes the basic data of the project to be developed, and reasonably selects the code development model, which can effectively alleviate the problems of low application performance, slow response speed, and poor user experience;

[0087] 2. The present invention performs time complexity and space complexity analysis on the generated project code and provides feedback to the developer based on the analysis results, which can effectively reduce the possibility of generating a large amount of redundant code. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0089] Figure 1 It is a diagram of the implementation steps of the present invention;

[0090] Figure 2 is the overall system block diagram of the present invention;

[0091] Figure 3 A flow chart of the agile development sub-model in the present invention;

[0092] Figure 4 Develop sub-model flow chart for iteration in the present invention;

[0093] Figure 5 This is the flowchart of the waterfall development sub-model in the present invention. DETAILED DESCRIPTION

[0094] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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 are within the scope of protection of the present invention.

[0095] Embodiment 1

[0096] See also Figure 2 ,The present invention provides a technical solution: a data-driven low-code generation system, comprising a data acquisition module, a data analysis module, a code generation module and a code testing module respectively connected to a server;

[0097] The data acquisition module acquires the basic data of the project to be developed;

[0098] It also includes a database, the data stored in the database include the project capital investment amount corresponding to the project to be developed and the operation speed of the established project;

[0099] Use front-end development tools to create data structure sub-interfaces, business requirements sub-interfaces, and API custom sub-interfaces to obtain a graphical user interaction interface;

[0100] Obtain the project cumulative demand data through the business demand sub-interface, as follows:

[0101] Before the project is developed, m characteristic demand monitoring points are selected and marked as the first characteristic demand monitoring point to the mth characteristic demand monitoring point respectively;

[0102] Obtain project requirement content data through the business requirement sub-interface,

[0103] The project demand data volume corresponding to the first characteristic demand monitoring point is obtained through the business demand sub-interface, as follows:

[0104] Obtain the number of user operations on the business requirement sub-interface corresponding to the first characteristic requirement monitoring point through the page access record to obtain the first user operation number;

[0105] Perform text recognition on the project demand content data corresponding to the first characteristic demand monitoring point by using a text recognition tool, obtain the total number of demands corresponding to the first characteristic demand monitoring point, and obtain the first total number of demands;

[0106] Obtaining the amount of attachment data in the project demand content data corresponding to the first characteristic demand monitoring point through an attachment access tool to obtain a first attachment data amount;

[0107] It should be noted here that:

[0108] The project requirement content data involved here is input by the customer through the business requirement sub-interface;

[0109] In this application, the number of user operations involved specifically refers to the number of operations performed by the user on the business demand sub-interface. In this application, it is assumed that the user performs operations on the business demand sub-interface, which means that the user has a certain degree of uncertainty about the demand;

[0110] The business requirements sub-interface also supports users to create requirements by selecting, configuring, and combining predefined modules and components through a graphical interface;

[0111] The attachments involved here include but are not limited to pictures and videos involved in the project development process;

[0112] The text recognition tool involved here can be Google Cloud Vision API;

[0113] The first user operation number, the first demand total number and the first attachment data volume are defined as the first project demand data volume;

[0114] Repeat the process of acquiring the demand data volume of the first project, respectively acquire the first to m-th user operation numbers, the first to m-th demand totals, and the first to m-th attachment data volumes to obtain the project cumulative demand data;

[0115] Get project risk data through the data structure sub-interface, as follows:

[0116] Obtain the project structure data corresponding to the project to be developed through the data structure sub-interface, and count the number of structural levels through the project structure data to obtain the project structure level quantity;

[0117] It should be noted here that:

[0118] The number of structural hierarchies refers to the number of hierarchical levels contained in the data structure or system structure defined in the project; in this application, it is specifically reflected in the number of occurrences of the selection structure and the cumulative number of nested layers of the loop structure;

[0119] Scan the data structure sub-interface through the function point recognition program to obtain the number of function points existing in the data structure sub-interface and obtain the number of project function points;

[0120] Obtain parameter definition data, perform string recognition on the parameter definition data using a text recognition tool, and count the number of definition parameters in the statistical string to obtain the number of project parameter definitions;

[0121] It should be noted here that:

[0122] The parameter definition data designed here is defined by the user;

[0123] In this application, the function point identification program involved may be Selenium;

[0124] The text recognition tool involved here can be Google Cloud Vision API;

[0125] The project complexity reference coefficient is obtained by calculating the number of project parameter definitions, the number of project function points and the number of project structure levels;

[0126] Calculate the project complexity reference coefficient, the specific formula configuration is as follows:

[0127] Xfs=Xcd*a1+Xdg+Xjc;

[0128] Among them, Xfs is the reference coefficient of project complexity, Xcd is the number of project parameter definitions, Xdg is the number of project function points, Xjc is the number of project structure levels, a1 is the set proportional coefficient and a1 is greater than 0;

[0129] Obtain the project capital investment amount and the established project operation speed corresponding to the project to be developed through the database;

[0130] The project capital investment, the established project operation speed and the project complexity reference coefficient are defined as project risk data;

[0131] Define project requirement content data, project structure data and parameter definition data as project content data;

[0132] Define project content data, project risk data and project cumulative demand data as basic data for projects to be developed;

[0133] The data acquisition module acquires the basic data of the project to be developed and transmits it to the data analysis module;

[0134] It should be noted here that:

[0135] Users can expand low-code generation system components through API custom sub-interfaces, allowing users to customize platform functions programmatically;

[0136] The data analysis module analyzes the basic data of the development project to obtain the project development classification data;

[0137] It also includes a database, wherein the data stored in the database also includes a threshold value for cumulative change in attachment data, a threshold value for cumulative total change in user requirements, a threshold value for cumulative number of user operations, a threshold value for reference coefficient of project complexity, a threshold value for project capital investment, and a threshold value for operation speed of a given project;

[0138] It should be noted here that:

[0139] The thresholds for cumulative changes in attachment data, cumulative changes in user requirements, cumulative user operation times, project complexity reference coefficient, project capital investment, and established project operation speed involved here are all obtained by obtaining the average values ​​of cumulative changes in attachment data, cumulative changes in user requirements, cumulative user operation times, project complexity reference coefficient, project capital investment, and established project operation speed corresponding to historical development projects;

[0140] Obtain the basic data of the projects to be developed, and obtain the cumulative demand data of the projects based on the basis of the projects to be developed;

[0141] Obtain the cumulative demand data of the project to obtain the first to mth user operation numbers, the first to mth demand totals, and the first to mth attachment data volumes;

[0142] Sum the operation numbers of the first to mth users to obtain the cumulative number of user operations;

[0143] It should be noted here that:

[0144] The cumulative number of user operations involved here is the sum of the number of user operations corresponding to the m characteristic demand monitoring points;

[0145] The total number of user demands is calculated by calculating the total number of demands from the first to the mth;

[0146] Calculate the total number of cumulative changes in user requirements. The specific formula configuration is as follows:

[0147] Yxb=|Xq1-Xq2|+|Xq2-Xq3|+······+Xq(m-1)-Xqm|;

[0148] Among them, Yxb is the total number of cumulative changes in user demand, Xq1 to Xqm are the total number of the first to mth demands respectively;

[0149] The amount of the first to mth attachment data is calculated to obtain the cumulative change amount of the attachment data;

[0150] Calculate the cumulative change of attachment data. The specific formula configuration is as follows:

[0151] Fsb=|Fs1-Fs2|+|Fs2-Fs3|+······+Fs(m-1)-Fsm|;

[0152] Among them, Fsb is the cumulative change of the attachment data, and Fs1 to Fsm are the first to mth attachment data respectively;

[0153] The project development change coefficient is obtained by calculating the cumulative change amount of the attachment data, the total cumulative change amount of user requirements and the cumulative number of user operations;

[0154] Calculate the project development variation coefficient, the specific formula configuration is as follows:

[0155] Xkb=Fsb*b1+Yxb+Ycs;

[0156] Among them, Xkb is the project development change coefficient, Fsb is the cumulative change of attachment data, Yxb is the total cumulative change of user needs, Ycs is the cumulative number of user operations, b1 is the set proportional coefficient and b1 is greater than 0;

[0157] Obtain the threshold value of the project development variation coefficient, compare the project development variation coefficient with the threshold value of the project development variation coefficient, and obtain the first-stage project classification data;

[0158] The threshold of the project development coefficient of variation is obtained as follows:

[0159] Extracting from the database the threshold of the cumulative change amount of attachment data, the threshold of the total number of cumulative changes in user requirements, and the threshold of the cumulative number of user operations;

[0160] The threshold of cumulative change amount of attachment data, the threshold of cumulative total change amount of user requirements and the threshold of cumulative number of user operations are calculated to obtain the threshold of project development change coefficient;

[0161] The project development coefficient of variation threshold is calculated, and the specific formula configuration is as follows:

[0162] Xkby=Fsby*b1+Yxby+Ycsy;

[0163] Among them, Xkby is the threshold of the project development change coefficient, Fsby is the threshold of the cumulative change of attachment data, Yxby is the threshold of the total number of cumulative changes in user needs, Ycsy is the threshold of the cumulative number of user operations, b1 is the set proportional coefficient and b1 is greater than 0;

[0164] The numerical comparison process is as follows:

[0165] When the project development variation coefficient is greater than or equal to the project development variation coefficient threshold, it is determined that the project is in the first project development interval;

[0166] When the project development variation coefficient is less than the project development variation coefficient threshold, it is determined that the project is in the second project development interval;

[0167] It should be noted here that:

[0168] In this application, the project development uncertainty corresponding to the first project development interval is greater than the project corresponding to the first project development interval;

[0169] The development uncertainties involved here include, but are not limited to, frequent changes in requirements and high project flexibility;

[0170] The development projects corresponding to the second project development interval were analyzed to obtain the second phase project classification data, as follows:

[0171] Obtain project risk data corresponding to the development interval of the second project based on the project to be developed;

[0172] Obtain the reference coefficient of project complexity, the amount of project capital investment and the established project operation speed based on the project risk data and calculate the project development risk coefficient;

[0173] Calculate the project development risk coefficient, the specific formula configuration is as follows:

[0174]

[0175] Xkf is the project development risk coefficient, Xfs is the project complexity reference coefficient, Xzj is the project capital investment, Xys is the established project operation speed, c2 is the set proportional coefficient and c2 is greater than 0;

[0176] Obtaining a threshold value of a project development risk factor, comparing the project development risk factor with the threshold value of the project development risk factor, and obtaining the second-stage project classification data;

[0177] The project development risk factor threshold is obtained as follows:

[0178] Extracting reference thresholds of project complexity coefficient, project capital investment amount and established project operation speed from the database;

[0179] The project complexity reference coefficient threshold, the project capital investment threshold and the established project operation speed threshold are calculated to obtain the project development risk coefficient threshold;

[0180] Calculate the project development risk factor threshold, the specific formula configuration is as follows:

[0181]

[0182] Xkf is the threshold of the project development risk coefficient, Xfs is the threshold of the project complexity reference coefficient, Xzj is the threshold of the project capital investment, Xys is the threshold of the established project operation speed, c2 is the set proportional coefficient and c2 is greater than 0;

[0183] The specific numerical comparison process is as follows:

[0184] When the project development risk coefficient is greater than or equal to the project development risk coefficient threshold, the project is judged to be in the third project development interval;

[0185] When the project development risk coefficient is less than the project development risk coefficient threshold, the project is judged to be in the fourth project development interval;

[0186] It should be noted here that:

[0187] The project risks involved in the development projects corresponding to the third project development interval are greater than those involved in the development projects corresponding to the fourth project development interval;

[0188] Among them, the project risks involved here include the development risks brought about by the large project capital investment and the high development difficulty corresponding to the high development requirements;

[0189] The first phase project classification data and the second phase project classification data are defined as project development classification data;

[0190] The data analysis module acquires the project development classification data and transmits it to the code generation module;

[0191] The code generation module matches the code development sub-model according to the project development classification data and generates project code through the development model;

[0192] Obtain project development classification data;

[0193] The classified data of projects to be developed include the first project development interval, the third project development interval and the fourth project development interval;

[0194] An existing code generation model is obtained and remotely connected to the graphical user interface, and the code generation model obtains project content data from the graphical user interface;

[0195] It should be noted here that:

[0196] The project requirement content data, project structure data and parameter definition data involved here are all input by the user through the graphical user interaction interface;

[0197] The code generation model matches the code development sub-model according to the project development classification data, and generates project code according to the project content data;

[0198] The code development sub-model is matched as follows:

[0199] When the project is in the first project development phase, match it to the agile development sub-model, see Figure 3 ;

[0200] When the project is in the third project development phase, match it to the iterative development sub-model, see Figure 4 ;

[0201] When the project is in the fourth project development phase, match it to the waterfall development sub-model, see Figure 5 ;

[0202] It should be noted here that:

[0203] In this application, the code generation model involved may be OpenAI Codex;

[0204] The code generation module obtains the project code and transmits it to the code testing module;

[0205] The code testing module tests the project code and provides feedback on the test results;

[0206] Also included is a database, where the data stored in the database also includes a code time complexity threshold and a code space complexity threshold;

[0207] It should be noted here that:

[0208] The code time complexity threshold involved here is the maximum code time complexity when the current project is in a normal state, and the code space complexity threshold involved here is the maximum code space complexity when the current project is in a normal state;

[0209] Obtaining a project code, linking the project code to a first test program, and running a test on the project code through the first test program;

[0210] If the project code can run normally, the project code is judged to be a normal running code;

[0211] If the project code cannot run normally, the project code is judged to be an abnormal running code, and the first test program issues an abnormal warning;

[0212] Perform complexity testing on the normal running code, as follows:

[0213] Obtain the accumulated running time of the code corresponding to the normal running code through the second test program, and mark it as the code time complexity;

[0214] The memory usage of the normal running code during the running process is obtained through the third test program, and is marked as the code space complexity;

[0215] It should be noted here that:

[0216] The cumulative running time of the code involved here is the time difference between the code start running time value and the code end running time value;

[0217] The memory usage involved here refers to the memory space occupied during the code execution;

[0218] The first test program involved here is the error reporting component in the code running program, the second test program is the time component in the code running program, and the third test program is the memory monitoring program;

[0219] The code time complexity and code space complexity are calculated to obtain the code complexity reference coefficient;

[0220] Calculate the code complexity reference coefficient, the specific formula configuration is as follows:

[0221] Dmf = Sjf*Kjf*s1;

[0222] Where Dmf is the code complexity reference coefficient, Sjf is the code time complexity, Kjf is the code space complexity, s1 is the set proportional coefficient and s1 is greater than 0;

[0223] Obtaining a code complexity reference coefficient threshold, and comparing the code complexity reference coefficient with the code complexity reference coefficient threshold;

[0224] The code complexity reference coefficient threshold is obtained as follows:

[0225] Extracting code time complexity threshold and code space complexity threshold from the database;

[0226] The code time complexity threshold and the code space complexity threshold are calculated to obtain a code complexity reference coefficient threshold;

[0227] The code complexity reference coefficient threshold is calculated, and the specific formula configuration is as follows:

[0228] Dmfy=Sjfy*Kjfy*s1;

[0229] Wherein, Dmfy is the code complexity reference coefficient threshold, Sjfy is the code time complexity threshold, Kjfy is the code space complexity threshold, s1 is the set proportional coefficient and s1 is greater than 0;

[0230] The numerical comparison process is as follows:

[0231] When the code complexity reference coefficient is greater than or equal to the code complexity reference coefficient threshold, the system will pop up a code optimization window;

[0232] When the code complexity reference coefficient is less than the code complexity reference coefficient threshold, code generation is completed;

[0233] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0234] Embodiment 2

[0235] See also Figure 1 Based on another concept of the same invention, a data-driven low-code generation method is proposed, comprising the following steps:

[0236] Step S1: Obtain basic data of the project to be developed;

[0237] It also includes a database, the data stored in the database include the project capital investment amount corresponding to the project to be developed and the operation speed of the established project;

[0238] Step S11: Establish a data structure sub-interface, a business requirement sub-interface, and an API custom sub-interface through a front-end development tool to obtain a graphical user interaction interface, and obtain project requirement content data, project structure data, and parameter definition data through the graphical user interaction interface to obtain project content data;

[0239] Step S12: Obtaining the project cumulative demand data through the business demand sub-interface;

[0240] Step S121: before the project is developed, m characteristic demand monitoring points are selected and marked as the first characteristic demand monitoring point to the mth characteristic demand monitoring point respectively;

[0241] Step S122: Obtain project requirement content data through the business requirement sub-interface,

[0242] Step S123: acquiring the project demand data volume corresponding to the first characteristic demand monitoring point through the business demand sub-interface;

[0243] In step S123, the specific steps are as follows:

[0244] Step S1231: obtaining the number of user operations on the business requirement sub-interface corresponding to the first characteristic requirement monitoring point through the page access record to obtain the first user operation number;

[0245] Step S1232: Perform text recognition on the project demand content data corresponding to the first characteristic demand monitoring point by using a text recognition tool to obtain the total number of demands corresponding to the first characteristic demand monitoring point, thereby obtaining the first total number of demands;

[0246] Step S1233: obtaining the amount of attachment data in the project requirement content data corresponding to the first characteristic requirement monitoring point through the attachment access tool to obtain the first attachment data amount;

[0247] Step S1234: defining the first user operation number, the first total demand, and the first attachment data volume as the first project demand data volume;

[0248] Step S124: Repeat the process of acquiring the demand data volume of the first project, respectively acquire the first to m-th user operation numbers, the first to m-th demand totals, and the first to m-th attachment data volumes to obtain the project cumulative demand data;

[0249] Step S13: Obtain project risk data through the data structure sub-interface;

[0250] The step S13 further includes the following steps:

[0251] Step S131: Obtain project structure data corresponding to the project to be developed through the data structure sub-interface, and count the number of structure levels through the project structure data to obtain the project structure level quantity;

[0252] Step S132: Scan the data structure sub-interface through the function point recognition program to obtain the number of function points existing in the data structure sub-interface, and obtain the number of project function points;

[0253] Step S133: Acquire parameter definition data, perform character string recognition on the parameter definition data using a text recognition tool, and count the number of definition parameters existing in the character string to obtain the number of project parameter definitions;

[0254] Step S134: Calculate the number of project parameter definitions, the number of project function points, and the number of project structure levels to obtain a project complexity reference coefficient;

[0255] Step S135: obtaining the project capital investment amount and the established project operation speed corresponding to the project to be developed through the database;

[0256] Step S136: defining the project capital investment amount, the established project operation speed and the project complexity reference coefficient as project risk data;

[0257] Step S14: defining the project content data, project risk data and project cumulative demand data as basic data of the project to be developed;

[0258] Step S2: Analyze the basic data of the project to be developed to obtain project development classification data;

[0259] It also includes a database, wherein the data stored in the database also includes a threshold value for cumulative change in attachment data, a threshold value for cumulative total change in user requirements, a threshold value for cumulative number of user operations, a threshold value for reference coefficient of project complexity, a threshold value for project capital investment, and a threshold value for operation speed of a given project;

[0260] Step S21: Obtain basic data of the project to be developed, and obtain the project cumulative demand data based on the project to be developed;

[0261] Step S22: Obtaining the project development variation coefficient;

[0262] The step S22 further includes the following specific steps:

[0263] Step S221: Obtaining the project cumulative demand data to obtain the first to mth user operation numbers, the first to mth demand totals, and the first to mth attachment data volumes;

[0264] Step S222: summing the first to mth user operation numbers to obtain the cumulative number of user operations;

[0265] Step S223: Calculate the total number of the first to mth demands to obtain the total number of cumulative changes in user demands;

[0266] Step S224: Calculate the first to mth attachment data amounts to obtain the attachment data cumulative change amount;

[0267] Step S225: Calculate the cumulative change amount of the attachment data, the cumulative total change amount of the user demand, and the cumulative number of user operations to obtain the project development change coefficient;

[0268] Step S23: obtaining a threshold value of a project development variation coefficient, comparing the project development variation coefficient with the threshold value of the project development variation coefficient, and obtaining the first-stage project classification data;

[0269] The step S23 further includes the following specific steps:

[0270] Step S231: extracting the attachment data cumulative change threshold, the user demand cumulative change total threshold and the user cumulative operation number threshold from the database;

[0271] Step S232: Calculate the threshold of the cumulative change amount of attachment data, the threshold of the total number of cumulative changes in user requirements, and the threshold of the cumulative number of user operations to obtain a threshold of the project development change coefficient;

[0272] Step S233: when the project development variation coefficient is greater than or equal to the project development variation coefficient threshold, it is determined that the project is in the first project development interval;

[0273] Step S234: when the project development variation coefficient is less than the project development variation coefficient threshold, it is determined that the project is in the second project development interval;

[0274] Step S24: Analyze the development projects corresponding to the second project development interval to obtain the second-stage project classification data;

[0275] The step S24 further includes the following specific steps:

[0276] Step S241: obtaining project risk data corresponding to the second project development interval based on the project to be developed;

[0277] Step S242: Obtain the project complexity reference coefficient, the project capital investment amount and the established project operation speed according to the project risk data and calculate the project development risk coefficient;

[0278] Step S243: obtaining a threshold value of a project development risk coefficient, comparing the project development risk coefficient with the threshold value of the project development risk coefficient, and obtaining the second-stage project classification data;

[0279] The step S243 further includes the following specific steps:

[0280] Step S2431: extracting a project complexity reference coefficient threshold, a project capital investment threshold, and a predetermined project operation speed threshold from a database;

[0281] Step S2432: Calculate the project development risk coefficient threshold by using the project complexity reference coefficient threshold, the project capital investment threshold, and the established project operation speed threshold;

[0282] Step S2433: when the project development risk coefficient is greater than or equal to the project development risk coefficient threshold, it is determined that the project is in the third project development interval;

[0283] Step S2434: when the project development risk coefficient is less than the project development risk coefficient threshold, it is determined that the project is in the fourth project development interval;

[0284] Step S25: defining the first-stage project classification data and the second-stage project classification data as project development classification data;

[0285] Step S3: matching the code development sub-model according to the project development classification data, and generating the project code through the development model;

[0286] Step S31: Obtain project development classification data;

[0287] Step S32: obtaining an existing code generation model and remotely connecting it to the graphical user interaction interface, wherein the code generation model obtains project content data from the graphical user interaction interface;

[0288] Step S33: the code generation model matches the code development sub-model according to the project development classification data, and generates the project code according to the project content data;

[0289] The step S33 further includes the following specific steps:

[0290] Step S331: When the project is in the first project development interval, match it to the agile development sub-model;

[0291] Step S332: When the project is in the third project development interval, match it to the iterative development sub-model;

[0292] Step S333: When the project is in the fourth project development interval, match it to the waterfall development sub-model;

[0293] Step S34: the code generation module obtains the project code and transmits it to the code testing module;

[0294] Step S4: Test the project code and provide feedback on the test results;

[0295] Also included is a database, where the data stored in the database also includes a code time complexity threshold and a code space complexity threshold;

[0296] Step S41: obtaining a project code, linking the project code to a first test program, and performing a running test on the project code through the first test program;

[0297] Step S411: If the project code can run normally, determine that the project code is a normal running code;

[0298] Step S412: If the project code cannot run normally, the project code is judged to be an abnormal running code, and the first test program issues an abnormal warning;

[0299] Step S42: Performing a complexity test on the normal running code;

[0300] Step S421: Obtain the accumulated running time of the code corresponding to the normal running code through the second test program, and mark it as the code time complexity;

[0301] Step S422: obtaining the memory usage of the normal running code during the running process through the third test program, and marking it as the code space complexity;

[0302] Step S423: Calculate the code time complexity and the code space complexity to obtain a code complexity reference coefficient;

[0303] Step S424: obtaining a code complexity reference coefficient threshold, and comparing the code complexity reference coefficient with the code complexity reference coefficient threshold;

[0304] The step S424 further includes the following steps:

[0305] Step S4241: extracting a code time complexity threshold and a code space complexity threshold from a database;

[0306] Step S4242: Calculate the code time complexity threshold and the code space complexity threshold to obtain a code complexity reference coefficient threshold;

[0307] Step S4243: When the code complexity reference coefficient is greater than or equal to the code complexity reference coefficient threshold, the system performs a code optimization pop-up window;

[0308] Step S4244: when the code complexity reference coefficient is less than the code complexity reference coefficient threshold, code generation is completed;

[0309] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data-driven low-code generation method, characterized in that: include: Step S1: Establishing a graphical user interaction interface, obtaining project content data, project cumulative demand data and project risk data through the graphical user interaction interface, and obtaining basic data of the project to be developed; The specific steps to obtain project risk data are as follows: Step S131: Obtain project structure data corresponding to the project to be developed through the data structure sub-interface, and count the number of structure levels through the project structure data to obtain the project structure level quantity; Step S132: Scan the data structure sub-interface through the function point recognition program to obtain the number of function points existing in the data structure sub-interface, and obtain the number of project function points; Step S133: Acquire parameter definition data, perform character string recognition on the parameter definition data, and count the number of definition parameters existing in the statistical character string to obtain the number of project parameter definitions; Step S134: Calculate the number of project parameter definitions, the number of project function points, and the number of project structure levels to obtain a project complexity reference coefficient; Step S135: Obtain the project capital investment amount and the established project operation speed corresponding to the project to be developed; Step S136: defining the project capital investment amount, the established project operation speed and the project complexity reference coefficient as project risk data; Step S2: Calculate the project development variation coefficient according to the project cumulative demand data, perform threshold judgment on the project development variation coefficient, and obtain the first-stage project classification data; calculate the project development risk coefficient according to the first-stage project classification data and the project risk data; perform threshold judgment on the project development risk coefficient, and obtain the second-stage project classification data; define the first-stage project classification data and the second-stage project classification data as the project development classification data; Calculate the project development risk coefficient, the specific formula configuration is as follows: ； Xkf is the project development risk coefficient, Xfs is the project complexity reference coefficient, Xzj is the project capital investment, Xys is the established project operation speed, c2 is the set proportional coefficient and c2 is greater than 0; Step S3: matching the code development sub-model according to the project development classification data, and generating the project code through the code development sub-model; In step S3, generating the project code further includes the following specific steps: Step S31: Obtain project development classification data; Step S32: obtaining an existing code generation model and remotely connecting it to the graphical user interaction interface, wherein the code generation model obtains project content data from the graphical user interaction interface; Step S33: the code generation model matches the code development sub-model according to the project development classification data, and generates the project code according to the project content data; Step S4: Run a test on the project code, issue an early warning based on the test results, obtain the code complexity reference coefficient corresponding to the normally running project code, analyze and test the code complexity reference coefficient, and provide feedback on the test results.

2. According to the data-driven low-code generation method of claim 1, it is characterized in that: It also includes a database, the data stored in the database include the project capital investment amount corresponding to the project to be developed and the operation speed of the established project; In step S1, the specific steps for obtaining the basic data of the project to be developed are as follows: Step S11: Establish a data structure sub-interface, a business requirement sub-interface, and an API custom sub-interface to obtain a graphical user interaction interface, and obtain project requirement content data, project structure data, and parameter definition data through the graphical user interaction interface to obtain project content data; Step S12: Obtaining the project cumulative demand data through the business demand sub-interface; Step S13: Obtain project risk data through the data structure sub-interface; Step S14: define the project content data, project risk data and project cumulative demand data as basic data of the project to be developed.

3. According to a data-driven low-code generation method according to claim 2, it is characterized in that: In step S12, the specific steps for obtaining the project cumulative demand data are as follows: Step S121: select m characteristic demand monitoring points and mark them as the first characteristic demand monitoring point to the mth characteristic demand monitoring point respectively; Step S122: Obtain project requirement content data through the business requirement sub-interface, Step S123: acquiring the project demand data volume corresponding to the first characteristic demand monitoring point through the business demand sub-interface; The step S123 is specifically as follows: Step S1231: Obtain the number of user operations on the business demand sub-interface corresponding to the first characteristic demand monitoring point to obtain a first user operation number; Step S1232: Perform text recognition on the project demand content data corresponding to the first characteristic demand monitoring point, obtain the total number of demands corresponding to the first characteristic demand monitoring point, and obtain the first total number of demands; Step S1233: acquiring the amount of attachment data in the project requirement content data corresponding to the first characteristic requirement monitoring point, and obtaining the first attachment data amount; Step S1234: defining the first user operation number, the first total demand, and the first attachment data volume as the first project demand data volume; Step S124: Repeat the process of acquiring the demand data volume of the first project, respectively acquire the first to m-th user operation numbers, the first to m-th demand totals, and the first to m-th attachment data volumes to obtain the project cumulative demand data.

4. According to a data-driven low-code generation method according to claim 1, it is characterized in that: It also includes a database, wherein the data stored in the database also includes a threshold value for cumulative change in attachment data, a threshold value for cumulative total change in user requirements, a threshold value for cumulative number of user operations, a threshold value for reference coefficient of project complexity, a threshold value for project capital investment, and a threshold value for operation speed of a given project; In step S2, the project development classification data is obtained, and the specific steps are as follows: Step S21: Obtain basic data of the project to be developed, and obtain the project cumulative demand data based on the project to be developed; Step S22: Obtaining the project development variation coefficient; Step S23: obtaining a threshold value of a project development variation coefficient, comparing the project development variation coefficient with the threshold value of the project development variation coefficient, and obtaining the first-stage project classification data; The specific steps of step S23 are as follows: Step S231: extracting the attachment data cumulative change threshold, the user demand cumulative change total threshold and the user cumulative operation number threshold from the database; Step S232: Calculate the threshold of the cumulative change amount of attachment data, the threshold of the total number of cumulative changes in user requirements, and the threshold of the cumulative number of user operations to obtain a threshold of the project development change coefficient; Step S233: when the project development variation coefficient is greater than or equal to the project development variation coefficient threshold, it is determined that the project is in the first project development interval; Step S234: when the project development variation coefficient is less than the project development variation coefficient threshold, it is determined that the project is in the second project development interval; Step S24: Analyze the development projects corresponding to the second project development interval to obtain second-stage project classification data.

5. A data-driven low-code generation method according to claim 4, characterized in that: In step S22, the specific steps for obtaining the project development variation coefficient are as follows: Step S221: Obtaining the project cumulative demand data to obtain the first to mth user operation numbers, the first to mth demand totals, and the first to mth attachment data volumes; Step S222: summing the first to mth user operation numbers to obtain the cumulative number of user operations; Step S223: Calculate the total number of the first to mth demands to obtain the total number of cumulative changes in user demands; Step S224: Calculate the first to mth attachment data amounts to obtain the attachment data cumulative change amount; Step S225: Calculate the cumulative change amount of the attachment data, the cumulative total change amount of user requirements, and the cumulative number of user operations to obtain the project development change coefficient.

6. According to a data-driven low-code generation method according to claim 4, it is characterized in that: In step S24, the specific steps for obtaining the second-stage project classification data are as follows: Step S241: obtaining project risk data corresponding to the second project development interval based on the project to be developed; Step S242: Obtain the project complexity reference coefficient, the project capital investment amount and the established project operation speed according to the project risk data and calculate the project development risk coefficient; Step S243: obtaining a threshold value of a project development risk coefficient, comparing the project development risk coefficient with the threshold value of the project development risk coefficient, and obtaining the second-stage project classification data; The step S243 is specifically as follows: Step S2431: extracting a reference coefficient threshold of project complexity, a threshold of project capital investment, and a threshold of a predetermined project operation speed; Step S2432: Calculate the project development risk coefficient threshold by using the project complexity reference coefficient threshold, the project capital investment threshold, and the established project operation speed threshold; Step S2433: when the project development risk coefficient is greater than or equal to the project development risk coefficient threshold, it is determined that the project is in the third project development interval; Step S2434: When the project development risk coefficient is less than the project development risk coefficient threshold, it is determined that the project is in the fourth project development interval.

7. A data-driven low-code generation method according to claim 6, characterized in that: The specific steps of step S33 are as follows: Step S331: When the project is in the first project development interval, match it to the agile development sub-model; Step S332: When the project is in the third project development interval, match it to the iterative development sub-model; Step S333: When the project is in the fourth project development interval, it is matched to the waterfall development sub-model.

8. The data-driven low-code generation method according to claim 1, characterized in that: Also included is a database, where the data stored in the database also includes a code time complexity threshold and a code space complexity threshold; In step S4, the project code is tested and the test results are fed back. The specific steps are as follows: Step S41: obtaining a project code, linking the project code to a first test program, and performing a running test on the project code through the first test program; Step S411: If the project code can run normally, determine that the project code is a normal running code; Step S412: If the project code cannot run normally, the project code is judged to be an abnormal running code, and the first test program issues an abnormal warning; Step S42: Performing a complexity test on the normal running code; Step S421: Obtain the accumulated running time of the code corresponding to the normal running code through the second test program, and mark it as the code time complexity; Step S422: obtaining the memory usage of the normal running code during the running process through the third test program, and marking it as the code space complexity; Step S423: Calculate the code time complexity and the code space complexity to obtain a code complexity reference coefficient; Step S424: Obtain a code complexity reference coefficient threshold, and compare the code complexity reference coefficient with the code complexity reference coefficient threshold.

9. A data-driven low-code generation method according to claim 8, characterized in that: The step S424 further includes the following steps: Step S4241: extracting a code time complexity threshold and a code space complexity threshold; Step S4242: Calculate the code time complexity threshold and the code space complexity threshold to obtain a code complexity reference coefficient threshold; Step S4243: When the code complexity reference coefficient is greater than or equal to the code complexity reference coefficient threshold, the system performs a code optimization pop-up window; Step S4244: When the code complexity reference coefficient is less than the code complexity reference coefficient threshold, code generation is completed.

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