CN109992250B - Automatic programming method, device, server and storage medium - Google Patents
Automatic programming method, device, server and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses an automatic programming method, an automatic programming device, a server and a storage medium. The method comprises the following steps: acquiring a business demand flow, wherein the business demand flow comprises a plurality of demand modules; and inputting the service demand flow into a pre-trained flow prediction model to obtain a middleware module sequence, wherein the middleware module sequence is a sequential connection sequence of a plurality of middleware modules. According to the technical scheme of the embodiment of the invention, quick service innovation and development are realized through AI autonomous programming, the software development period is shortened, the technical threshold of terminal application system development is reduced, and the autonomous programming development of the terminal application system is realized.
Description
Technical Field
The embodiment of the invention relates to an artificial intelligence technology, in particular to an automatic programming method, an automatic programming device, a server and a storage medium.
Background
It has long been one of the dreams in the field of artificial intelligence to let AI (artificial intelligence) automatically program. Two researchers from the penbo and intel laboratories purport to implement the first AI system "AI Programmer" that can automatically generate a complete software program, which can theoretically accomplish any type of task using genetic algorithms and turing complete languages. The era of automatic AI programming has opened the screen.
Researchers have shown that they have demonstrated with this system the long-standing assumption that a fully functional program can indeed be generated automatically. Specifically to this work, AI programer simulates complex instructions using genetic algorithms in machine learning. While AI programmers now generate programs, the complexity is comparable to the results written by human novice programmers. However, researchers believe that programs written by the AI program can completely exceed the traditional scope, and are not limited by human time and intelligence.
In the wave of the intelligent transformation of bank outlets, banks are actively innovated and changed, the situation of high business innovation and high tide stacking is presented, and a plurality of novel terminal devices including but not limited to a visual counter VTM, a large-amount cash recycling machine, an intelligent number calling machine and the like are continuously emerged in the aspect of equipment introduction and improvement. The presentation of new business, the improvement of new flow, the promotion of business mode and the rapid popularization of new bank business make the application and development of financial terminals difficult under the existing low development mode and low business efficiency.
In the field of financial technology, with the rise of cashless technology, the banking industry in China faces deep changes of business modes and technical means, the financial terminal business innovation period is shorter and shorter, business implementation and online nobility take precedence, and huge technical challenges and cost pressure are brought to financial terminal service providers still using traditional technology for system development.
The quantity of domestic terminal equipment is huge, the distribution region is wide, and the equipment function interaction, the human-computer interface interaction and the background service interaction are related, so the technology is complex, the operation and maintenance cost is high, and the cost accounts for more than 80% of the whole life cycle. The current financial terminal application software development environment and development technology are complex, the requirement of developers on strong speciality is strong, and the management cost is high when the code versions are continuously increased. The manual programming development mode has low automation and intelligence degree, long development period, high cost and large software operation and maintenance risk, so that the change is urgently needed.
Disclosure of Invention
The invention provides an automatic programming method, an automatic programming device, a server and a storage medium, which are used for realizing the quick innovation and development of financial terminal application software on the premise of low cost and low technical threshold.
In a first aspect, an embodiment of the present invention provides an automatic programming method, where the method includes:
acquiring a business demand flow, wherein the business demand flow comprises a plurality of demand modules;
and inputting the service demand flow into a flow prediction model which is trained in advance to obtain a middleware module sequence, wherein the middleware module sequence is a sequential connection sequence of a plurality of middleware modules.
Optionally, the pre-training of the process prediction model includes:
acquiring a service demand flow of a plurality of training samples and a middleware module sequence corresponding to the service demand flow;
marking the business requirement flow of each training sample by using a label, storing the marked business requirement flow of each training sample into a sample feature library as a training sample set, wherein the label comprises a middleware module sequence corresponding to the business requirement flow of each training sample;
the process prediction model is trained using a training sample set.
Optionally, the process prediction model is a hidden markov model.
Optionally, the initial state probability matrix of the hidden markov model is a probability that a previous demand module is connected to a next demand module in the business demand flow.
Optionally, the hidden state transition probability matrix of the hidden markov model is a probability that each demand module of the business process correspondingly selects a different middleware module.
Optionally, the observation state transition probability matrix of the hidden markov model is a probability that a previous middleware module is connected to a next middleware module in the plurality of middleware modules.
Optionally, the middleware module includes a software support component and a hardware functional component.
In a second aspect, an embodiment of the present invention further provides an automatic programming apparatus, where the apparatus includes:
the service demand acquisition unit is used for acquiring a service demand flow, and the service demand flow comprises a plurality of demand modules;
and the module sequence generating unit is used for inputting the service demand flow into a flow prediction model which is trained in advance to obtain a middleware module sequence, and the middleware module sequence is a sequential connection sequence of a plurality of middleware modules.
In a third aspect, an embodiment of the present invention further provides a server, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the automatic programming method described in any one of the foregoing embodiments when executing the program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is configured to implement the automatic programming method described in any one of the above embodiments when executed by a processor.
The embodiment of the invention realizes rapid business innovation and development through AI autonomous programming, reduces the software development period, reduces the technical threshold of terminal application system development, and realizes the autonomous programming development of the terminal application system.
Drawings
FIG. 1 is a flow chart illustrating a method for automatic programming according to a first embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating pre-training of a flow prediction model according to a first embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an automatic programming apparatus according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a server in the third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some structures related to the present invention are shown in the drawings, not all of them.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first speed difference may be referred to as a second speed difference, and similarly, the second speed difference may be referred to as a first speed difference, without departing from the scope of the present application. The first speed difference value and the second speed difference value are both speed difference values, but they are not the same speed difference value. The terms "first", "second", etc. should not be construed to indicate or imply relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise.
Example one
Fig. 1 is a flowchart illustrating an automatic programming method according to an embodiment of the present invention, where the automatic programming method is applicable to a case where an AI technique is used to implement autonomous programming, and the method may be implemented by an automatic programming apparatus, which may be implemented by software and/or hardware, and may be generally integrated in a server or a terminal device. Referring to fig. 1, the automatic programming method according to the embodiment of the present invention specifically includes the following steps:
Specifically, the business requirement process is a process required by the user to send to the server, such as a deposit process, a withdrawal process, a transfer process, and the like. Each business demand flow comprises a plurality of demand modules, for example, a deposit flow comprises a card swallowing module, a currency detecting module, a display module, a calculating module and the like; for another example, the withdrawal process includes: the device comprises a card swallowing module, a cash dispensing module, a calculating module, a display module and the like; if the transfer process comprises the following steps: a card-swallowing module, a display module, a computing module, an input module, and the like.
And 120, inputting the service demand flow into a flow prediction model which is trained in advance to obtain a middleware module sequence, wherein the middleware module sequence is a sequential connection sequence of a plurality of middleware modules.
In particular, middleware is a separate system software or service by which distributed application software shares resources between different technologies. Middleware resides on the operating system of the client/server, manages computer resources and network communications. Is software that connects two separate applications or separate systems. Connected systems, even if they have different interfaces, can still exchange information with each other through middleware. One key way to execute middleware is information transfer. Through middleware, applications can operate in a multi-platform or OS environment.
The middleware module of this embodiment is a program module or a hardware-driven program module for completing a service requirement flow, where the program module or the hardware-driven program module includes software interfaces corresponding to multiple requirement modules, and the middleware module sequence is a program module and/or a hardware-driven program module arranged in sequence in a programming process corresponding to the service requirement flow, and source codes of the program modules and/or the hardware-driven program modules of the software interfaces can be sequentially executed to form a complete program flow to complete an ATM machine to implement a corresponding service requirement flow. In this embodiment, the service demand process is input into the process prediction model that has been trained in advance, and a middleware module sequence can be obtained.
Specifically, the process of training the flow prediction model of the embodiment is shown in fig. 2, and specifically includes the following steps:
Specifically, a business requirement process of obtaining a plurality of training samples, for example: deposit flow, withdrawal flow, transfer flow, inquiry flow and encryption change flow, and acquiring a sequence defined by a plurality of middleware modules in a source code form corresponding to the function of each flow, namely a sequence formed by program modules of a plurality of software interfaces in the source code form and/or a hardware-driven program module as a training sample. The training samples can be different types of business requirement processes (such as a deposit process and a withdrawal process) or the same type of business requirement processes (both are storage processes).
Specifically, in order to train the process prediction model, input content and output content need to be acquired, the input content of the process prediction model of this embodiment is the business requirement process of each training sample, and the output content is a middleware module sequence of each training sample corresponding to the business requirement process.
Taking a deposit process as an example, marking the deposit process of each training sample by using a sequence defined by a plurality of middleware modules in a source code for realizing the deposit function as a label, and storing the marked deposit process of each training sample into a sample feature library as a training sample set, namely a deposit training sample set.
And 230, training the process prediction model by using a training sample set.
Specifically, taking the above deposit process as an example, after the marked deposit process of each training sample is stored in the sample feature library as a training sample set, each sample in the deposit training sample set is used to train the process prediction model.
Preferably, the process prediction model is a hidden markov model. Specifically, the hidden markov model includes 2 state sets and 3 probability matrices, which are: hidden state S, observable state O, initial state probability matrix, hidden state transition probability matrix, and observed state transition probability matrix.
Among them, hidden state S is a state actually hidden in a markov model, satisfies markov properties, and is generally not obtained by direct observation. The financial terminal application platform business process based on middleware unit operation is composed of a series of middleware sequences meeting Markov, and the middleware sequences form a hidden state S. The observable state O is associated with the hidden state in the model and can be obtained by direct observation, and the number of observable states does not necessarily need to be consistent with the number of hidden states. The embodiment of the invention is composed of a series of element operation interface sequences, and each element operation interface is an interface of a certain middleware model.
Preferably, the initial state probability matrix of the hidden markov model is a probability that a previous demand module is connected with a next demand module in the business demand flow.
Preferably, the hidden state transition probability matrix of the hidden markov model is a probability that each demand module of the business process correspondingly selects different middleware modules.
Preferably, the observation state transition probability matrix of the hidden markov model is a probability that a previous middleware module is connected to a next middleware module in the plurality of middleware modules.
Preferably, the middleware module comprises a software support component and a hardware functional component.
Specifically, the software support assembly includes: the system comprises one or more of a public interface, an Agent component, a TCPClient component, a TCPServer component, an Http component, a UDP component, an SFTP component, a serial port component, an ISO8583 component timeout mechanism component, a file operation component, a screen operation component, a configuration component, an XML component, a text transcoding component, a database component, a PBOC component, a character list component, a card table component, an encryption and decryption component and a system tool component.
The hardware functional components comprise: one or more of a public interface, a barcode scanner (BCR), a Camera (CAM), a Cash Dispensing Module (CDM), a card printing device (CEU), a check/fingerprint scanner (CHK), a deposit module (CIM), a card issuing module (CRD), a card reader module (IDC), a keypad (PIN), a Printer (PTR), a sensing device (SIU), a background terminal (TTU), a vendor mode (VDM), a Ukey device (Ukey), and a camera (camera). The hardware functional component and the software supporting component are mutually independent and support plug and play, and each component can be independently upgraded without influencing other modules.
The embodiment of the invention realizes rapid business innovation and development realization through AI autonomous programming, reduces the software development period, reduces the technical threshold of terminal application system development, and realizes the autonomous programming development of the terminal application system.
Example two
The automatic programming device provided by the embodiment of the invention can execute the automatic programming method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. Fig. 3 is a schematic structural diagram of an automatic programming apparatus according to a second embodiment of the present invention. Referring to fig. 3, the automatic programming apparatus provided in the embodiment of the present invention may specifically include:
a service requirement obtaining unit 310, configured to obtain a service requirement flow, where the service requirement flow includes multiple requirement modules;
the module sequence generating unit 320 is configured to input the service demand flow into a flow prediction model which has been trained in advance, so as to obtain a middleware module sequence, where the middleware module sequence is a sequential connection sequence of a plurality of middleware modules.
Optionally, the module sequence generating unit is further configured to:
acquiring service requirement flows of a plurality of training samples and middleware module sequences corresponding to the service requirement flows;
marking the business requirement flow of each training sample by using a label, storing the marked business requirement flow of each training sample into a sample feature library as a training sample set, wherein the label comprises a middleware module sequence corresponding to the business requirement flow of each training sample;
the process prediction model is trained using a training sample set.
Optionally, the process prediction model is a hidden markov model.
Optionally, the initial state probability matrix of the hidden markov model is a probability that a previous demand module is connected to a next demand module in the business demand flow.
Optionally, the hidden state transition probability matrix of the hidden markov model is a probability that each demand module of the business process correspondingly selects a different middleware module.
Optionally, the observation state transition probability matrix of the hidden markov model is a probability that a previous middleware module is connected to a next middleware module in the plurality of middleware modules.
Optionally, the middleware module includes a software support component and a hardware functional component.
The embodiment of the invention realizes rapid business innovation and development realization through AI autonomous programming, reduces the software development period, reduces the technical threshold of terminal application system development, and realizes the autonomous programming development of the terminal application system.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a server according to a third embodiment of the present invention, as shown in fig. 4, the server includes a processor 410, a memory 420, an input device 430, and an output device 440; the number of the processors 410 in the server may be one or more, and one processor 410 is taken as an example in fig. 4; the processor 410, the memory 420, the input device 430 and the output device 440 in the server may be connected by a bus or other means, and the bus connection is exemplified in fig. 4.
The memory 420 serves as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the automatic programming method in the embodiment of the present invention (e.g., the service requirement acquisition unit 210 and the module sequence generation unit 220 in the automatic programming apparatus). The processor 410 executes various functional applications of the server and data processing by executing software programs, instructions and modules stored in the memory 420, that is, implements the above-described auto-programming method.
Namely:
acquiring a business demand flow, wherein the business demand flow comprises a plurality of demand modules;
and inputting the service demand flow into a flow prediction model which is trained in advance to obtain a middleware module sequence, wherein the middleware module sequence is a sequential connection sequence of a plurality of middleware modules.
The memory 420 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 420 may further include memory located remotely from the processor 410, which may be connected to the device/terminal/server via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 430 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the server. The output device 440 may include a display device such as a display screen.
Example four
A fourth embodiment of the present invention further provides a storage medium containing computer-executable instructions which, when executed by a computer processor, perform a method of automated programming, the method comprising:
acquiring a business demand flow, wherein the business demand flow comprises a plurality of demand modules;
and inputting the service demand flow into a flow prediction model which is trained in advance to obtain a middleware module sequence, wherein the middleware module sequence is a sequential connection sequence of a plurality of middleware modules.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also perform related operations in the automatic programming method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the search apparatus, each included unit and each included module are merely divided according to functional logic, but are not limited to the above division, as long as corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements and substitutions will now be apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (9)
1. A method of automated programming, comprising:
acquiring a business demand flow, wherein the business demand flow comprises a plurality of demand modules;
inputting the service demand flow into a flow prediction model which is trained in advance to obtain a middleware module sequence, wherein the middleware module sequence is a sequential connection sequence of a plurality of middleware modules;
the pre-training of the process prediction model comprises the following steps:
acquiring a service demand flow of a plurality of training samples and a middleware module sequence corresponding to the service demand flow;
marking the business requirement flow of each training sample by using a label, storing the marked business requirement flow of each training sample into a sample characteristic library as a training sample set, wherein the label comprises a middleware module sequence corresponding to the business requirement flow of each training sample;
the process prediction model is trained using a training sample set.
2. The automated programming method of claim 1, wherein the process prediction model is a hidden markov model.
3. The automated programming method of claim 2, wherein the initial state probability matrix of the hidden markov model is a probability of a previous demand module connecting to a subsequent demand module in the business demand flow.
4. The automated programming method of claim 3, wherein the hidden Markov model hidden state transition probability matrix is a probability of selecting a different middleware module for each requirement module of the business requirement process.
5. The automated programming method of claim 4, wherein the hidden Markov model's observed state transition probability matrix is a probability that a preceding middleware module connects to a succeeding middleware module in the plurality of middleware modules.
6. The automated programming method according to claim 1, wherein the middleware module comprises a software support component and a hardware functional component.
7. An automated programming apparatus, comprising:
the service demand acquisition unit is used for acquiring a service demand flow, and the service demand flow comprises a plurality of demand modules;
a module sequence generating unit, configured to input the service demand flow into a pre-trained flow prediction model to obtain a middleware module sequence, where the middleware module sequence is a sequential connection sequence of multiple middleware modules;
the module sequence generating unit is further configured to:
acquiring a service demand flow of a plurality of training samples and a middleware module sequence corresponding to the service demand flow;
marking the business requirement flow of each training sample by using a label, storing the marked business requirement flow of each training sample into a sample feature library as a training sample set, wherein the label comprises a middleware module sequence corresponding to the business requirement flow of each training sample;
the process prediction model is trained using a training sample set.
8. A server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of automated programming according to any of claims 1-6.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of automatic programming according to any one of claims 1 to 6.
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