New energy intelligent modeling operator operation test method, system, equipment and medium

Abstract

The invention discloses a new energy intelligent modeling operator operation test method, system and device and a medium. The method comprises the steps that a canvas json to be processed is acquired; according to the to-be-processed canvas json, on the basis of a trained time sequence model LSTM, prediction is carried out to obtain production data; analyzing the actual operation result of the to-be-processed canvas json to obtain actual production data; the predicted production data is compared with the actually obtained production data, new energy intelligent modeling operator operation testing is completed, and the method, the system, the equipment and the medium can avoid manual testing.

Description

Technical Field

[0001] The present invention belongs to the field of new energy, and relates to a new energy intelligent modeling operator operation test method, system, equipment and medium. Background Art

[0002] In intelligent modeling tools, traditional functional testing methods usually rely on manually setting parameters and executing operator operations. Testers execute each operator step by step and record the output results based on known input data and predefined operator logic. This method is widely used to ensure that operators work as expected and can process input data correctly. However, traditional functional testing methods have the following problems:

[0003] 1. Time-consuming and highly repetitive: Traditional testing methods require manual execution of each operator, which is very time-consuming when there are a large number of operators.

[0004] 2. Error-prone: The manual testing process is susceptible to human factors such as operational errors or data entry errors.

[0005] 3. Limited coverage: In complex models, it is difficult to ensure that all possible input combinations are tested, resulting in insufficient test coverage.

[0006] 4. Difficulty in processing large-scale data: For models that need to process large amounts of data, traditional testing methods may not be able to process and analyze the data efficiently.

[0007] 5. Unable to adapt to dynamic changes: If the operators in the model are frequently updated or changed, traditional testing methods require constant adjustment of test cases, which increases maintenance costs.

[0008] 6. Lack of predictive capabilities: Traditional testing methods are mainly used to verify current operation results, but cannot predict future outputs or identify potential problems. Summary of the invention

[0009] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a new energy intelligent modeling operator operation test method, system, equipment and medium, which can avoid manual testing.

[0010] In order to achieve the above object, the present invention adopts the following technical scheme:

[0011] In one aspect, the present invention provides a new energy intelligent modeling operator operation test method, comprising:

[0012] Get the canvas json to be processed;

[0013] According to the canvas json to be processed, based on the trained time series model LSTM, production data is predicted;

[0014] Parse the actual running results of the canvas json to be processed to obtain the actual production data;

[0015] Compare the predicted production data with the actual production data to complete the new energy intelligent modeling operator operation test.

[0016] The new energy intelligent modeling operator calculation test method of the present invention is further improved in that:

[0017] Furthermore, the process of predicting the production data based on the canvas json to be processed and the trained time series model LSTM also includes:

[0018] Build a dataset;

[0019] Build the time series model LSTM;

[0020] The time series model LSTM is trained based on the data set to obtain a trained time series model LSTM.

[0021] Furthermore, the process of constructing the data set is:

[0022] Get several canvas jsons, parse each canvas json, and build a dataset based on the parsing results.

[0023] Furthermore, the input quantity in the data set is the input features of operator A, the input features of operator B and the input features of operator C, and the output quantity is the output features of operator C.

[0024] In a second aspect of the present invention, the present invention provides a new energy intelligent modeling operator operation test system, comprising:

[0025] Get module, used to get the canvas json to be processed;

[0026] A prediction module is used to predict production data based on the canvas json to be processed and the trained time series model LSTM;

[0027] The parsing module is used to parse the actual running results of the canvas json to be processed and obtain the actual production data;

[0028] The comparison module is used to compare the predicted production data with the actual production data to complete the new energy intelligent modeling operator operation test.

[0029] The new energy intelligent modeling operator calculation test system of the present invention is further improved in that:

[0030] Furthermore, it also includes:

[0031] The first building module is used to build a data set;

[0032] The second building module is used to build the time series model LSTM;

[0033] The training module is used to train the time series model LSTM based on the data set to obtain a trained time series model LSTM.

[0034] Furthermore, the process of constructing the data set is:

[0035] Get several canvas jsons, parse each canvas json, and build a dataset based on the parsing results.

[0036] Furthermore, the input quantity in the data set is the input features of operator A, the input features of operator B and the input features of operator C, and the output quantity is the output features of operator C.

[0037] In the third aspect of the present invention, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the new energy intelligent modeling operator operation test method when executing the computer program.

[0038] In a fourth aspect of the present invention, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the new energy intelligent modeling operator operation test method are implemented.

[0039] The present invention has the following beneficial effects:

[0040] The new energy intelligent modeling operator operation test method, system, device and medium described in the present invention predict production data according to the canvas json to be processed based on the trained time series model LSTM during specific operation, without the need to manually update test cases. By identifying the deviation between the output value and the predicted value, it helps to discover potential operation errors or data problems to solve problems in the manual testing process, and the testing accuracy and efficiency are high. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0045] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0046] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0047] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0048] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0050] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0051] Embodiment 1

[0052] refer to Figure 1 The new energy intelligent modeling operator operation test method of the present invention comprises the following steps:

[0053] 1) Parse the canvas json to obtain the operator type, operator input value, operator output value, operator order and the output value of the last operator; the canvas has operators A, B and C, where the input of operator B depends on the output of operator A, and the input of operator C depends on the output of operator B. The features include the input features of operator A, the input features of operator B and the input features of operator C, and the predicted output value is the output feature of operator C.

[0054] 2) Obtain several canvas jsons, parse each canvas json, and construct a data set according to the parsing results, wherein the input quantity in the data set is the input features of operator A, the input features of operator B, and the input features of operator C, and the output quantity is the output features of operator C, that is, production data;

[0055] 3) Build the time series model LSTM;

[0056] 4) Training the time series model LSTM based on the data set to obtain a trained time series model LSTM;

[0057] 5) Obtain the canvas json to be processed, parse the canvas json to be processed, obtain the input features of operator A, operator B and operator C corresponding to the canvas json to be processed, and input the input features of operator A, operator B and operator C into the trained time series model LSTM to predict the production data;

[0058] 6) Parse the running results of the canvas json to be processed to obtain the actual generated data;

[0059] 7) Compare the predicted production data with the actual production data to determine whether the predicted production data is consistent with the actual production data

[0060] It should be noted that the present invention can significantly improve the efficiency and quality of testing work by introducing machine learning technology in the test process of the operator operation output results of the intelligent modeling tool. Compared with the traditional manual testing method, the machine learning prediction test method has the following significant advantages: compared with the traditional manual functional testing, machine learning can reduce the consumption of manpower and time resources; machine learning can handle a large number of input combinations and improve test coverage; it can quickly adapt to the update or change of the operator without manually updating the test case; identify the deviation between the output value and the predicted value, and help discover potential operation errors or data problems.

[0061] Embodiment 2

[0062] The present invention provides a new energy intelligent modeling operator operation test system, comprising:

[0063] Get module, used to get the canvas json to be processed;

[0064] A prediction module is used to predict production data based on the canvas json to be processed and the trained time series model LSTM;

[0065] The parsing module is used to parse the actual running results of the canvas json to be processed and obtain the actual production data;

[0066] The comparison module is used to compare the predicted production data with the actual production data to complete the new energy intelligent modeling operator operation test.

[0067] As an implementation mode of the present invention, it also includes:

[0068] The first building module is used to build a data set;

[0069] The second building module is used to build the time series model LSTM;

[0070] The training module is used to train the time series model LSTM based on the data set to obtain a trained time series model LSTM.

[0071] As an implementation manner of the present invention, the process of constructing the data set is:

[0072] Get several canvas jsons, parse each canvas json, and build a dataset based on the parsing results.

[0073] As an implementation manner of the present invention, the input quantity in the data set is the input features of operator A, the input features of operator B and the input features of operator C, and the output quantity is the output features of operator C.

[0074] The division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0075] Embodiment 3

[0076] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of implementing the new energy intelligent modeling operator operation test method include: obtaining a canvas json to be processed; predicting production data based on the trained time series model LSTM according to the canvas json to be processed; parsing the actual operation results of the canvas json to be processed to obtain the actual production data; comparing the predicted production data with the actual production data to complete the new energy intelligent modeling operator operation test. Construct a data set; construct a time series model LSTM; train the time series model LSTM based on the data set to obtain a trained time series model LSTM. Furthermore, the process of constructing the data set is: obtaining several canvas jsons, parsing each canvas json, and constructing a data set according to the parsing results. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, network interface, and memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0077] Embodiment 4

[0078] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of implementing the new energy intelligent modeling operator operation test method include: obtaining a canvas json to be processed; predicting production data based on the trained time series model LSTM according to the canvas json to be processed; parsing the actual operation results of the canvas json to be processed to obtain actual production data; comparing the predicted production data with the actual production data to complete the new energy intelligent modeling operator operation test. Construct a data set; construct a time series model LSTM; train the time series model LSTM based on the data set to obtain the trained time series model LSTM. Further, the process of constructing the data set is: obtaining several canvas jsons, parsing each canvas json, and constructing a data set according to the parsing result. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, disk, etc.

[0079] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0080] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0081] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0083] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0084] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0085] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A new energy intelligent modeling operator operation test method, characterized in that: include: Get the canvas json to be processed; According to the canvas json to be processed, based on the trained time series model LSTM, production data is predicted; Parse the actual running results of the canvas json to be processed to obtain the actual production data; Compare the predicted production data with the actual production data to complete the new energy intelligent modeling operator operation test.

2. The new energy intelligent modeling operator operation test method according to claim 1 is characterized in that: The method further includes: predicting the production data based on the trained time series model LSTM according to the canvas json to be processed, and: Build a dataset; Build the time series model LSTM; The time series model LSTM is trained based on the data set to obtain a trained time series model LSTM.

3. The new energy intelligent modeling operator operation test method according to claim 2 is characterized in that: The process of constructing the data set is as follows: Get several canvas jsons, parse each canvas json, and build a dataset based on the parsing results.

4. The new energy intelligent modeling operator operation test method according to claim 3 is characterized in that: The input quantity in the data set is the input features of operator A, the input features of operator B and the input features of operator C, and the output quantity is the output features of operator C.

5. A new energy intelligent modeling operator operation test system, characterized in that: include: Get module, used to get the canvas json to be processed; A prediction module is used to predict production data based on the canvas json to be processed and the trained time series model LSTM; The parsing module is used to parse the actual running results of the canvas json to be processed and obtain the actual production data; The comparison module is used to compare the predicted production data with the actual production data to complete the new energy intelligent modeling operator operation test.

6. The new energy intelligent modeling operator operation test system according to claim 5 is characterized in that: Also includes: The first building module is used to build a data set; The second building module is used to build the time series model LSTM; The training module is used to train the time series model LSTM based on the data set to obtain a trained time series model LSTM.

7. The new energy intelligent modeling operator operation test system according to claim 6 is characterized in that: The process of constructing the data set is as follows: Get several canvas jsons, parse each canvas json, and build a dataset based on the parsing results.

8. The new energy intelligent modeling operator operation test system according to claim 7 is characterized in that: The input quantity in the data set is the input features of operator A, the input features of operator B and the input features of operator C, and the output quantity is the output features of operator C.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the new energy intelligent modeling operator operation test method as described in any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the new energy intelligent modeling operator operation test method as described in any one of claims 1 to 4 are implemented.

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