Evaluation method for evaluating input and output of application system

Abstract

The invention discloses an evaluation method for evaluating input and output of an application system, and relates to the technical field of data evaluation. The method comprises the following steps: initializing input data of the application system; taking the input data as reference input data, and taking the reference input data as input of an output prediction model for operation to obtain predicted output data; updating various data in the reference input data to obtain updated input data; taking the updated input data as the input of an output prediction model for operation to obtain predicted output data; and updating the data with the maximum output comprehensive scoring parameter in the reference input data and the latest input data as the reference input data, updating each data in the reference input data again, and taking the latest reference input data as the final input data until an iteration stop condition is met. The method disclosed by the invention can maximize the output data of the system and improve the input-output rate under the condition that the total input is fixed.

Description

Technical Field

[0001] The present invention belongs to the technical field of data evaluation, and in particular relates to an evaluation method for evaluating the input-output of an application system. Background Art

[0002] With the rapid development of information technology, the role of application systems in enterprise operations has become increasingly prominent. Their input and output not only affect the economic benefits of the enterprise, but also directly affect the competitiveness and sustainable development capabilities of the enterprise. Therefore, it is necessary to evaluate the input and output of application systems.

[0003] At present, traditional input-output evaluation often focuses on the evaluation of the company's own benefits, while ignoring user-side considerations. The input-output evaluation of the application system is often difficult to fully reflect the true value of the application system, resulting in problems such as mismatch between input and output and low output rate, which is not conducive to the long-term sustainable development of the enterprise.

[0004] Therefore, how to provide an effective solution to accurately and reasonably evaluate the input-output of the application system and improve the input-output has become an urgent problem to be solved in the prior art. Summary of the invention

[0005] The purpose of the present invention is to provide an evaluation method for evaluating the input-output of an application system, so as to solve the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an evaluation method for evaluating the input-output of an application system, comprising:

[0008] Initialize the investment data of the application system based on the total investment of the application system and the proportion of each investment to the total investment, the investment data including human resource cost, development cost and maintenance cost;

[0009] The input data is used as the benchmark input data, and the benchmark input data is used as the input of the pre-trained output prediction model to perform calculations to obtain the predicted output data of the application system, wherein the predicted output data includes user growth rate, application revenue, and user satisfaction;

[0010] Based on the total investment of the application system and the proportion of each investment to the total investment, each data in the benchmark investment data is updated to obtain updated investment data, wherein the absolute value of the difference between each data in the updated investment data and the corresponding data in the investment data before the update is lower than a preset threshold;

[0011] Using the updated input data as input of the output prediction model to perform calculations to obtain predicted output data of the application system;

[0012] Compare the output comprehensive scoring parameters of the predicted output data corresponding to the baseline input data with the output comprehensive scoring parameters of the predicted output data corresponding to the latest input data, where the output comprehensive scoring parameters of the predicted output data are determined based on the user growth rate, application revenue and user satisfaction in the predicted output data;

[0013] The data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data is updated as the benchmark input data, and based on the total investment of the application system and the proportion of each input in the total investment, each data in the benchmark input data is updated to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, until the iteration stop condition is reached, and the latest benchmark input data is used as the final input data.

[0014] Based on the above disclosed content, the present invention initializes the input data of the application system based on the total investment of the application system and the proportion range of each investment in the total investment, the input data including human resource cost, development cost and maintenance cost; uses the input data as benchmark input data, and uses the benchmark input data as input of a pre-trained output prediction model for calculation to obtain predicted output data of the application system, the predicted output data including user growth rate, application revenue and user satisfaction; based on the total investment of the application system and the proportion range of each investment in the total investment, updates each data in the benchmark input data to obtain updated input data; uses the updated input data as input of the output prediction model for calculation to obtain predicted output data of the application system; compares the predicted output data corresponding to the benchmark input data The output comprehensive scoring parameter of the output data is compared with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, where the output comprehensive scoring parameter of the predicted output data is determined based on the user growth rate, application revenue and user satisfaction in the predicted output data; the data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data is updated as the benchmark input data, and the data in the benchmark input data is updated again based on the total investment of the application system and the proportion of each investment to the total investment, so as to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, until the iteration stop condition is reached, the latest benchmark input data is used as the final input data. In this way, in the input-output evaluation of the application system, in addition to application revenue, the input-output of the application system is accurately and reasonably evaluated based on user growth rate and user satisfaction, and at the same time, when the total investment is fixed, the output data of the system is maximized from multiple perspectives such as user growth rate, application revenue and user satisfaction, so as to improve the input-output rate and benefit the long-term sustainable development of the enterprise.

[0015] In a possible design, initializing the investment data of the application system based on the total investment of the application system and the proportion range of each investment to the total investment includes:

[0016] Based on the total investment of the application system and the proportion range of each investment in the total investment, the investment data of the application system is randomly initialized.

[0017] In a possible design, based on the total investment of the application system and the proportion of each investment to the total investment, each data in the benchmark investment data is updated to obtain updated investment data, including:

[0018] Based on the total investment of the application system and the proportion range of each investment to the total investment, each data in the benchmark investment data is updated multiple times to obtain multiple updated investment data.

[0019] In a possible design, the comprehensive scoring parameters of the predicted output data are α×C1+β×C2+γ×C3+δ, where C1, C2 and C3 represent user growth rate, application revenue and user satisfaction respectively, α, β and γ are weighting coefficients, and δ is a preset value.

[0020] In a possible design, the iteration stopping condition is that the number of iterations reaches a preset number of iterations or the output comprehensive scoring parameters of the predicted output data corresponding to the input data converge.

[0021] In one possible design, the method further includes:

[0022] The final input data is used as the input of the output prediction model to perform calculations to obtain the final predicted output data of the application system.

[0023] In one possible design, the method further includes generating an input-output evaluation report based on the final input data and the final predicted output data.

[0024] In a second aspect, the present invention provides an evaluation device for evaluating the input-output of an application system, comprising:

[0025] An initialization unit, configured to initialize the investment data of the application system based on the total investment of the application system and the proportion of each investment to the total investment, wherein the investment data includes human resource cost, development cost and maintenance cost;

[0026] A computing unit, used to use the input data as benchmark input data, and use the benchmark input data as input of a pre-trained output prediction model to perform computing to obtain predicted output data of the application system, wherein the predicted output data includes user growth rate, application revenue, and user satisfaction;

[0027] An updating unit, configured to update each item of data in the benchmark input data based on the total input of the application system and the proportion of each item of data in the total input, to obtain updated input data, wherein the absolute value of the difference between each item of data in the updated input data and the corresponding item of data in the input data before the update is lower than a preset threshold;

[0028] The computing unit is further used to use the updated input data as input of the output prediction model to perform computing to obtain predicted output data of the application system;

[0029] A comparison unit, used to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, wherein the output comprehensive scoring parameter of the predicted output data is determined based on the user growth rate, application revenue and user satisfaction in the predicted output data;

[0030] The updating unit is also used to update the data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data as the benchmark input data, and re-update each data in the benchmark input data based on the total investment of the application system and the proportion of each input in the total investment, so as to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, until the iteration stop condition is reached, and the latest benchmark input data is used as the final input data.

[0031] In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the input-output evaluation method of the evaluation application system as described in the first aspect or any possible design of the first aspect.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, executes the input-output evaluation method of the evaluation application system according to the first aspect or any possible design of the first aspect.

[0033] In a fifth aspect, the present invention provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute the input-output evaluation method for an evaluation application system as described in the first aspect or any possible design of the first aspect.

[0034] Beneficial effects:

[0035] When evaluating the input-output of an application system, the present invention not only performs an accurate and reasonable evaluation of the input-output of the application system based on the user growth rate and user satisfaction, etc. At the same time, under the condition of a fixed total investment, the output data of the system is maximized from multiple perspectives such as user growth rate, application revenue and user satisfaction, thereby improving the input-output ratio and facilitating the long-term sustainable development of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of an evaluation method for evaluating the input and output of an application system provided in an embodiment of the present application;

[0037] Figure 2 A schematic block diagram of an input-output evaluation device for evaluating an application system provided in an embodiment of the present application;

[0038] Figure 3 A schematic block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0040] It should be understood that although the terms first, second, etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another unit. For example, a first unit can be referred to as a second unit, and similarly, a second unit can be referred to as a first unit without departing from the scope of the exemplary embodiments of the present invention.

[0041] It should be understood that the term "and / or" that may appear in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" that may appear in this article describes another type of association object relationship, indicating that two relationships may exist. For example, A / and B can represent two situations: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0042] In order to accurately and reasonably evaluate the input-output of an application system and improve the input-output, an embodiment of the present application provides an evaluation method for evaluating the input-output of an application system. The evaluation method for evaluating the input-output of an application system can accurately and reasonably evaluate the input-output of the application system, and at the same time, when the total input is fixed, it can maximize the output data of the system and improve the output rate of input.

[0043] The input-output evaluation method of the evaluation application system provided in the embodiment of the present application can be applied to a server or a user terminal. It can be understood that the execution subject does not constitute a limitation on the embodiment of the present application.

[0044] The following is a detailed description of the input-output evaluation method of the evaluation application system provided in the embodiment of the present application.

[0045] like Figure 1 As shown, it is a flowchart of the input-output evaluation method of the evaluation application system provided in the first aspect of the embodiment of the present application. The input-output evaluation method of the evaluation application system may include but is not limited to the following steps S101-S106.

[0046] Step S101 . Initialize the investment data of the application system based on the total investment of the application system and the proportion range of each investment in the total investment.

[0047] The input data may include but is not limited to human resource costs, development costs and maintenance costs.

[0048] In an embodiment of the present application, the proportion range of each investment (i.e., human resource cost, development cost, and maintenance cost) in the total investment can be pre-defined. When initializing the investment data of the application system, the investment data of the application system can be randomly initialized within the corresponding proportion range based on the total investment of the application system and the proportion range of each investment in the total investment.

[0049] Step S102: Using the input data as benchmark input data, and using the benchmark input data as input of a pre-trained output prediction model to perform calculations to obtain predicted output data of the application system.

[0050] The predicted output data may include, but is not limited to, user growth rate, application revenue, and user satisfaction.

[0051] In the embodiment of the present application, each item of input data will affect one or more items of output data. Under the same total input, different proportions of user growth rate, application revenue and user satisfaction in the input data will lead to significant differences in user growth rate, application revenue and user satisfaction in the output data. For example, in general, as maintenance costs increase, the user experience of the application system will be better, so user satisfaction will also increase accordingly.

[0052] Therefore, the embodiment of the present application pre-trains an output prediction model for output prediction. When training, the output prediction model can be trained with input data (human resource cost, development cost and maintenance cost) of the same type of application system as sample input and output data (user growth rate, application revenue and user satisfaction) of the same type of application system as sample output. After initializing the input data of the application system, the input data can be used as the benchmark input data, and the benchmark input data can be used as the input of the pre-trained output prediction model to obtain the predicted output data of the application system.

[0053] Step S103: Based on the total investment of the application system and the proportion of each investment to the total investment, update each data in the benchmark investment data to obtain updated investment data.

[0054] The absolute value of the difference between each item of data in the updated input data and the corresponding item of data in the input data before the update is lower than a preset threshold value. The preset threshold value should not be too large and can be set according to actual conditions.

[0055] In one or more embodiments, when updating each item of data in the benchmark input data, the data in the benchmark input data may be updated multiple times based on the total input of the application system and the proportion of each item of input to the total input, to obtain multiple updated input data. In this way, when updating, multiple updated input data may be obtained in the same batch, so that the output data of the system can be maximized as soon as possible later.

[0056] Step S104: Use the updated input data as input to the output prediction model to perform calculations to obtain predicted output data of the application system.

[0057] It can be understood that if a plurality of updated input data are obtained in one update, the plurality of updated input data can be used as inputs of the output prediction model for calculation respectively to obtain a plurality of predicted output data of the application system.

[0058] Step S105: Compare the output comprehensive scoring parameters of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameters of the predicted output data corresponding to the latest input data.

[0059] Among them, the output comprehensive scoring parameters of the predicted output data can be determined based on the user growth rate, application revenue and user satisfaction in the predicted output data. Specifically, the output comprehensive scoring parameters of the predicted output data can be expressed as α×C1+β×C2+γ×C3+δ, where C1, C2 and C3 represent the user growth rate, application revenue and user satisfaction respectively, α, β and γ are all weighting coefficients, and δ is a preset value. α, β and γ can be determined based on the importance attached to the user growth rate, application revenue and user satisfaction.

[0060] Step S106. Update the data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data as the benchmark input data, and re-update each data in the benchmark input data based on the total input of the application system and the proportion of each input in the total input, so as to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, until the iteration stop condition is reached, and the latest benchmark input data is used as the final input data.

[0061] In the embodiment of the present application, after comparing the output comprehensive scoring parameters of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameters of the predicted output data corresponding to the latest input data, the data with the largest output comprehensive scoring parameters between the benchmark input data and the latest input data can be updated as the benchmark input data. Then, based on the total input of the application system and the proportion of each input to the total input, the data in the benchmark input data is updated again, and the output comprehensive scoring parameters of the predicted output data corresponding to the benchmark input data are compared with the output comprehensive scoring parameters of the predicted output data corresponding to the latest input data.

[0062] Repeat the above steps until the iteration stop condition is reached, and use the latest benchmark input data as the final input data. At this time, the output comprehensive scoring parameter of the predicted output data corresponding to the final input data reaches the maximum value or is close to the maximum value. At this time, with the total investment fixed, the system output data is maximized by comprehensively considering multiple angles such as user growth rate, application revenue and user satisfaction.

[0063] The iteration stopping condition may be that the number of iterations reaches a preset number of iterations or the output comprehensive scoring parameters of the predicted output data corresponding to the input data converge.

[0064] In one or more embodiments, after updating the data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data as the benchmark input data, the final input data can also be used as the input of the output prediction model for calculation to obtain the final predicted output data of the application system, and an input-output evaluation report can be generated based on the final input data and the final predicted output data.

[0065] In summary, the input-output evaluation method for evaluating an application system provided by the present invention initializes the input data of the application system based on the total input of the application system and the proportion range of each input to the total input, the input data including human resource cost, development cost and maintenance cost; uses the input data as benchmark input data, and uses the benchmark input data as input of a pre-trained output prediction model for calculation to obtain predicted output data of the application system, the predicted output data including user growth rate, application revenue and user satisfaction; based on the total input of the application system and the proportion range of each input to the total input, updates each data in the benchmark input data to obtain updated input data; uses the updated input data as input of the output prediction model for calculation to obtain predicted output data of the application system; compares the benchmark input data According to the size of the output comprehensive scoring parameter of the corresponding predicted output data and the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, wherein the output comprehensive scoring parameter of the predicted output data is determined based on the user growth rate, application revenue and user satisfaction in the predicted output data; the data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data is updated as the benchmark input data, and based on the total investment of the application system and the proportion of each investment in the total investment, the various data in the benchmark input data are updated to compare the size of the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data and the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, until the iteration stop condition is reached, and the latest benchmark input data is used as the final input data. In this way, when evaluating the input-output of the application system, in addition to application revenue, the input-output of the application system is accurately and reasonably evaluated based on user growth rate and user satisfaction, thereby automating the evaluation process. This enables rapid processing and analysis of data from large-scale application systems and is suitable for complex application environments. At the same time, with the total investment fixed, the system's output data is maximized from multiple angles such as user growth rate, application revenue, and user satisfaction, thereby improving the input-output ratio, benefiting the long-term sustainable development of the enterprise, and facilitating practical application and promotion.

[0066] See also Figure 2 According to a second aspect of an embodiment of the present application, there is provided an evaluation device for evaluating the input-output of an application system, the evaluation device for evaluating the input-output of an application system comprising:

[0067] An initialization unit, configured to initialize the investment data of the application system based on the total investment of the application system and the proportion of each investment to the total investment, wherein the investment data includes human resource cost, development cost and maintenance cost;

[0068] A computing unit, used to use the input data as benchmark input data, and use the benchmark input data as input of a pre-trained output prediction model to perform computing to obtain predicted output data of the application system, wherein the predicted output data includes user growth rate, application revenue, and user satisfaction;

[0069] An updating unit, configured to update each item of data in the benchmark input data based on the total input of the application system and the proportion of each item of data in the total input, to obtain updated input data, wherein the absolute value of the difference between each item of data in the updated input data and the corresponding item of data in the input data before the update is lower than a preset threshold;

[0070] The computing unit is further used to use the updated input data as input of the output prediction model to perform computing to obtain predicted output data of the application system;

[0071] A comparison unit, used to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, wherein the output comprehensive scoring parameter of the predicted output data is determined based on the user growth rate, application revenue and user satisfaction in the predicted output data;

[0072] The updating unit is also used to update the data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data as the benchmark input data, and re-update each data in the benchmark input data based on the total investment of the application system and the proportion of each input in the total investment, so as to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, until the iteration stop condition is reached, and the latest benchmark input data is used as the final input data.

[0073] The working process, working details and technical effects of the input-output evaluation device for evaluating the application system provided in the second aspect of this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0074] like Figure 3 As shown, the third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the input-output evaluation method of the evaluation application system as described in the first aspect of the embodiment.

[0075] For specific examples, the memory may include but is not limited to random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out memory (FIFO) and / or first-in-last-out memory (FILO), etc.; the processor may be but is not limited to a microprocessor of the STM32F105 series, an ARM (Advanced RISC-Machines), an X86 or other architecture processor, or a processor with an integrated NPU (neural-network processing units); the transceiver may be but is not limited to a WiFi (Wireless Fidelity) wireless transceiver, a Bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee protocol (a low-power local area network protocol based on the IEEE802.15.4 standard, ZigBee) wireless transceiver, a 3G transceiver, a 4G transceiver and / or a 5G transceiver, etc.

[0076] The fourth aspect of this embodiment provides a computer-readable storage medium storing instructions for the input-output evaluation method of the evaluation application system described in the first aspect of the embodiment, that is, the computer-readable storage medium stores instructions, and when the instructions are run on a computer, the input-output evaluation method of the evaluation application system described in the first aspect is executed. The computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive, and / or a memory stick, etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0077] A fifth aspect of this embodiment provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute the input-output evaluation method for evaluating an application system as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0078] It should be understood that certain details are provided in the following description to facilitate a complete understanding of the example embodiments. However, it should be understood by those of ordinary skill in the art that the example embodiments can be implemented without these certain details. For example, the system can be shown in a block diagram to avoid obscuring the example with unnecessary details. In other examples, well-known processes, structures, and techniques may not be shown in unnecessary detail to avoid obscuring the example embodiments.

[0079] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the input-output of an application system, characterized in that: include: Initialize the investment data of the application system based on the total investment of the application system and the proportion of each investment to the total investment, the investment data including human resource cost, development cost and maintenance cost; The input data is used as the benchmark input data, and the benchmark input data is used as the input of the pre-trained output prediction model to perform calculations to obtain the predicted output data of the application system, wherein the predicted output data includes user growth rate, application revenue, and user satisfaction; Based on the total investment of the application system and the proportion of each investment to the total investment, each data in the benchmark investment data is updated to obtain updated investment data, wherein the absolute value of the difference between each data in the updated investment data and the corresponding data in the investment data before the update is lower than a preset threshold; Using the updated input data as input of the output prediction model to perform calculations to obtain predicted output data of the application system; Compare the output comprehensive scoring parameters of the predicted output data corresponding to the baseline input data with the output comprehensive scoring parameters of the predicted output data corresponding to the latest input data, where the output comprehensive scoring parameters of the predicted output data are determined based on the user growth rate, application revenue and user satisfaction in the predicted output data; The data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data is updated as the benchmark input data, and based on the total investment of the application system and the proportion of each input in the total investment, each data in the benchmark input data is updated to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, until the iteration stop condition is reached, and the latest benchmark input data is used as the final input data.

2. The input-output evaluation method of the evaluation application system according to claim 1, characterized in that: Initializing the investment data of the application system based on the total investment of the application system and the proportion of each investment to the total investment includes: Based on the total investment of the application system and the proportion range of each investment in the total investment, the investment data of the application system is randomly initialized.

3. The input-output evaluation method of the evaluation application system according to claim 1, characterized in that: Based on the total investment of the application system and the proportion of each investment to the total investment, each data in the benchmark investment data is updated to obtain updated investment data, including: Based on the total investment of the application system and the proportion of each investment to the total investment, each data in the benchmark investment data is updated multiple times to obtain multiple updated investment data.

4. The input-output evaluation method of the evaluation application system according to claim 1, characterized in that: The comprehensive scoring parameters of the predicted output data are α×C1+β×C2+γ×C3+δ, where C1, C2 and C3 represent user growth rate, application revenue and user satisfaction respectively, α, β and γ are all weighting coefficients, and δ is a preset value.

5. The input-output evaluation method of the evaluation application system according to claim 1, characterized in that: The iteration stopping condition is that the number of iterations reaches a preset number of iterations or the output comprehensive scoring parameters of the predicted output data corresponding to the input data converge.

6. The input-output evaluation method of the evaluation application system according to claim 1, characterized in that: The method further comprises: The final input data is used as the input of the output prediction model to perform calculations to obtain the final predicted output data of the application system.

7. The input-output evaluation method of the evaluation application system according to claim 6, characterized in that: The method further comprises generating an input-output evaluation report based on the final input data and the final predicted output data.

8. An evaluation device for evaluating the input-output of an application system, characterized in that: include: An initialization unit, configured to initialize the investment data of the application system based on the total investment of the application system and the proportion of each investment to the total investment, wherein the investment data includes human resource cost, development cost and maintenance cost; A computing unit, used to use the input data as benchmark input data, and use the benchmark input data as input of a pre-trained output prediction model to perform computing to obtain predicted output data of the application system, wherein the predicted output data includes user growth rate, application revenue, and user satisfaction; An updating unit, configured to update each item of data in the benchmark input data based on the total input of the application system and the proportion of each item of data in the total input, to obtain updated input data, wherein the absolute value of the difference between each item of data in the updated input data and the corresponding item of data in the input data before the update is lower than a preset threshold; The computing unit is further used to use the updated input data as input of the output prediction model to perform computing to obtain predicted output data of the application system; A comparison unit, used to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, wherein the output comprehensive scoring parameter of the predicted output data is determined based on the user growth rate, application revenue and user satisfaction in the predicted output data; The updating unit is also used to update the data with the largest output comprehensive scoring parameter between the benchmark input data and the latest input data as the benchmark input data, and re-update each data in the benchmark input data based on the total investment of the application system and the proportion of each input in the total investment, so as to compare the output comprehensive scoring parameter of the predicted output data corresponding to the benchmark input data with the output comprehensive scoring parameter of the predicted output data corresponding to the latest input data, until the iteration stop condition is reached, and the latest benchmark input data is used as the final input data.

9. An electronic device, characterized in that: It comprises a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the input-output evaluation method of the evaluation application system as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or the instruction is executed by a computer, the input-output evaluation method of the evaluation application system according to any one of claims 1 to 7 is implemented.

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