Quality evaluation information generation method and system based on big data

Abstract

The invention discloses a quality evaluation information generation method and system based on big data, and the method comprises the steps: collecting service data and test data of a preset product, and carrying out the preprocessing of the service data and the test data; performing validity screening on the service data to obtain valid data, and obtaining quality data through the valid data; the quality data comprises first quality and second quality; the first quality represents a product quality score based on service data; the second quality represents the service satisfaction of the user; correcting the quality data by adopting the test data to obtain corrected data, and constructing a quality evaluation information generation model according to the corrected data; and optimizing the quality evaluation information generation model according to the evaluation error, inputting to-be-evaluated data into the quality evaluation information generation model, and outputting a generation result. The method improves the precision of quality evaluation information generation, has good interpretability, and can be directly used in a quality evaluation information generation system.

Description

Technical Field

[0001] The present invention relates to the field of quality evaluation, and in particular to a method and system for generating quality evaluation information based on big data. Background Art

[0002] In today's era of rapid development of information technology, product quality evaluation no longer relies solely on traditional testing and user feedback methods, but is increasingly inclined to use big data technology for comprehensive, objective and efficient analysis. With the widespread application of technologies such as cloud computing, the Internet of Things and artificial intelligence, companies can collect massive amounts of product service data and test data, which provide unprecedented rich materials for product quality evaluation. However, how to effectively use these big data to generate accurate and reliable quality evaluation information has become a problem that needs to be solved urgently.

[0003] Traditional quality evaluation methods often focus on physical performance testing of products or subjective user experience surveys. Although these methods can reflect the quality of products to a certain extent, they often have limitations. For example, physical performance testing may not fully cover various scenarios in the actual use of the product, and the user's subjective experience may be affected by a variety of factors such as personal preferences and usage habits, resulting in a lack of objectivity and accuracy in the evaluation results.

[0004] Therefore, Kou Dai invented a new method for generating quality evaluation information to improve the accuracy and reliability of quality evaluation. Summary of the invention

[0005] The purpose of the present invention is to provide a method for generating quality evaluation information based on big data.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions: The present invention comprises the following steps: Collecting service data and test data of a preset product, and preprocessing the service data and the test data; Performing validity screening on the service data to obtain valid data, and obtaining quality data through the valid data; the quality data includes a first quality and a second quality; the first quality represents a product quality score based on the service data; the second quality represents a user's service satisfaction; Using the test data to correct the quality data to obtain corrected data, and constructing a quality evaluation information generation model according to the corrected data; The quality evaluation information generation model is optimized according to the evaluation error, the data to be evaluated is input into the quality evaluation information generation model, and the generation result is output.

[0007] Furthermore, the method of screening the service data for validity to obtain valid data includes: Compare the product problems and service data reported by users to calculate the correlation between product problems and service data: , The sth product problem is , the jth service data is , product issues and service data The correlation is , the control factor is , the optimization coefficient is , service data The reference value is , product issues The reference value is , the amount of service data is , the number of product issues is , product problems Service data appears in the case of The probability of ; The service data with a correlation greater than 0.3719 is output as valid data.

[0008] Furthermore, the method for obtaining the first quality includes: Extract quality-related data from valid data and calculate product performance correlation of quality-related data; Sort the quality-related data by time to obtain the time series quality data, and calculate the product quality score based on the time series quality data: , The product quality score of the i-th batch is , the number of customers is Q, and the adjustable coefficient is , the evaluation score of the vth customer’s zth valid data is , the product performance correlation of the zth valid data of the vth customer is , the zth valid data of the vth customer is The standard value of valid data is , the number of valid data is , the error coefficient is , valid data and standard value The variance of , the maintenance ratio of the i-th batch of products is , the time weight is ; Output the product quality score as the first quality.

[0009] Furthermore, a method for obtaining the second quality includes: Extract user feedback data from valid data, sort the user feedback data according to time, and obtain time series feedback data; Based on the user feedback data in the valid data, the sentiment dictionary is used to obtain the sentiment score, and the sentiment score and the excited feedback frequency are used to obtain the user's evaluation score; Calculate user service satisfaction based on time series feedback data: , , The first parameter is , the second parameter is , the sentiment score of the vth customer’s zth valid data is , the frequency of excited feedback of the zth valid data of the vth customer is , the offset coefficient is , the service satisfaction of the i-th batch of products is , the zth valid data of the vth customer is The standard value of valid data is , the number of customers is Q, and the number of valid data is ; Service satisfaction is output as the second quality.

[0010] Furthermore, the method of using the test data to correct the quality data to obtain corrected data includes: Compare the product data and test data in the service data, and calculate the deviation between the product data and test data in the service data: , The cth product data is , the cth test data is , the kernel density estimation function is , the number of test data is N, and the square of the norm is , the error factor is , the control coefficient is ; According to the similarity of the test data, a decision tree is used to classify and calculate the amount of information of the test data: , The amount of information for the xth category is , the number of test data for the xth category is , the cth test data of the xth category is , the average value of the test data for the xth category is , test data The probability of occurrence is ; The quality data is corrected by the deviation and information content, and the expression is: , The cth quality data is , the cth correction data is .

[0011] Furthermore, a method for constructing a quality evaluation information generation model based on the correction data includes: The objective function of the quality evaluation information generation model is constructed based on the corrected data. The expression is: , The loss function is , the objective function of the i-th batch is , the quality weight is , the satisfaction weight is , the service satisfaction of the i-th batch of products is , the product quality score of the i-th batch is ; The quality evaluation information generation model includes random forest algorithm, feature extraction algorithm, and deep learning algorithm; The random forest algorithm divides the input data into training data and test data in a ratio of 7:2; The feature extraction algorithm extracts the features of the training data through the encoder part of the neural network to obtain the service features; The deep learning algorithm takes the target function as the learning object, uses the self-attention mechanism to capture the global dependency, and generates quality evaluation information based on the global dependency; The test data is input into the quality evaluation information generation model to obtain the predicted quality evaluation information, and the regularization parameter of the quality evaluation information generation model is adjusted according to the variance of the predicted quality evaluation information and the actual quality evaluation information.

[0012] Furthermore, the method for optimizing the quality evaluation information generation model according to the evaluation error includes: Introducing particle population, taking the variance of evaluation error as fitness error, and performing chaotic mapping on particle population, the expression is: , The chaotic random number from 0 to 1 is b, and the w+1th dimension is , the wth dimension is ; Calculate the fitness of the particle, take the position of the particle with the largest fitness as the optimal position, and update the position of the particle. The expression is: , The random numbers from 1 to 2 are , the updated position of the wth dimension of the ath particle in the t+1th iteration is , the initial position of the ath particle in the wth dimension is , a random number from 0 to 1 is , the Cauchy operator is , the optimal position of the tth iteration is ; Introduce the disturbance factor and update the particle position to obtain the disturbance position. The expression is: , , The perturbed position of the wth dimension of the ath particle in the t+1th iteration is , the updated position of the wth dimension of the ath particle in the tth iteration is , the random numbers from 0 to 1 are , , , the perturbation factor for the tth iteration is , the maximum number of iterations is , the current number of iterations is t; The corrected position is obtained by using the position updated by the standard deviation, and the expression is: , The normally distributed random numbers The standard deviation of , the perturbed position of the ath particle in the wth dimension at the tth iteration is , the corrected position of the wth dimension of the ath particle in the t+1th iteration is , the lower bound of the w-th dimension is , the upper bound of the w-th dimension is ; Continue to iterate until the maximum number of iterations is reached, then stop iterating, otherwise update the perturbation factor.

[0013] In a second aspect, a quality evaluation information generation system based on big data includes: Data collection module: used to collect service data and test data of preset products, and pre-process the service data and the test data; A screening quality module: used for screening the service data for validity to obtain valid data, and obtaining quality data through the valid data; the quality data includes a first quality and a second quality; the first quality represents a product quality score based on the service data; the second quality represents a user's service satisfaction; Correction construction module: used to correct the quality data using the test data to obtain corrected data, and to construct a quality evaluation information generation model according to the corrected data; Optimization generation module: used to optimize the quality evaluation information generation model according to the evaluation error, input the data to be evaluated into the quality evaluation information generation model, and output the generation result.

[0014] In a third aspect, an embodiment of the present application further provides an electronic device, including: A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0015] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0016] The beneficial effects of the present invention are: The present invention is a method and system for generating quality evaluation information based on big data. Compared with the prior art, the present invention has the following technical effects: The present invention can improve the accuracy of quality evaluation information generation through preprocessing, effectiveness screening, obtaining quality data, correcting data, model building and model optimization steps, thereby improving the precision of quality evaluation information generation. The quality evaluation information generation can be optimized, which can greatly save resources and improve work efficiency. It can realize the automatic generation of quality evaluation information for product services, and perform data correction and multi-data fusion on the generation of product service quality evaluation information in real time. It is of great significance to the generation of product service quality evaluation information, can adapt to the generation of quality evaluation information of different standards and different quality evaluation information generation needs, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of the steps of a method for generating quality evaluation information based on big data according to the present invention; Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0018] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0019] The present invention provides a method and system for generating quality evaluation information based on big data, comprising the following steps: like Figure 1 As shown, in this embodiment, the following steps are included: Collecting service data and test data of a preset product, and preprocessing the service data and the test data; In actual evaluation, service data includes transaction data, user data, product data, market data, user survey data, third-party data, service request data, service result data, technical data, and feedback data; Take XXX product as the research object and collect service data and test data in the fall of 2024; Performing validity screening on the service data to obtain valid data, and obtaining quality data through the valid data; the quality data includes a first quality and a second quality; the first quality represents a product quality score based on the service data; the second quality represents a user's service satisfaction; In actual evaluation, effective data includes user data, product data, market data, user survey data, third-party data, service request data, service result data, and feedback data; The first mass is 0.872, the second mass is 0.638; Using the test data to correct the quality data to obtain corrected data, and constructing a quality evaluation information generation model according to the corrected data; In the actual evaluation, the correction data of the first and second masses were 0.859 and 0.651, respectively; The quality evaluation information generation model is optimized according to the evaluation error, the data to be evaluated is input into the quality evaluation information generation model, and the generation result is output.

[0020] In this embodiment, the method for performing validity screening on the service data to obtain valid data includes: Compare the product problems and service data reported by users to calculate the correlation between product problems and service data: , The sth product problem is , the jth service data is , product issues and service data The correlation is , the control factor is , the optimization coefficient is , service data The reference value is , product issues The reference value is , the amount of service data is , the number of product issues is , product problems Service data appears in the case of The probability of ; The service data with a correlation greater than 0.3719 is output as valid data.

[0021] In this embodiment, the method for obtaining the first quality includes: Extract quality-related data from valid data and calculate product performance correlation of quality-related data; Sort the quality-related data by time to obtain the time series quality data, and calculate the product quality score based on the time series quality data: , The product quality score of the i-th batch is , the number of customers is Q, and the adjustable coefficient is , the evaluation score of the vth customer’s zth valid data is , the product performance correlation of the zth valid data of the vth customer is , the zth valid data of the vth customer is The standard value of valid data is , the number of valid data is , the error coefficient is , valid data and standard value The variance of , the maintenance ratio of the i-th batch of products is , the time weight is ; Output the product quality score as the first quality.

[0022] In this embodiment, the method for obtaining the second quality includes: Extract user feedback data from valid data, sort the user feedback data according to time, and obtain time series feedback data; Based on the user feedback data in the valid data, the sentiment dictionary is used to obtain the sentiment score, and the sentiment score and the excited feedback frequency are used to obtain the user's evaluation score; Calculate user service satisfaction based on time series feedback data: , , The first parameter is , the second parameter is , the sentiment score of the vth customer’s zth valid data is , the frequency of excited feedback of the zth valid data of the vth customer is , the offset coefficient is , the service satisfaction of the i-th batch of products is , the zth valid data of the vth customer is The standard value of valid data is , the number of customers is Q, and the number of valid data is ; Service satisfaction is output as the second quality.

[0023] In this embodiment, the method of using the test data to correct the quality data to obtain corrected data includes: Compare the product data and test data in the service data, and calculate the deviation between the product data and test data in the service data: , The cth product data is , the cth test data is , the kernel density estimation function is , the number of test data is N, and the square of the norm is , the error factor is , the control coefficient is ; According to the similarity of the test data, a decision tree is used to classify and calculate the amount of information of the test data: , The amount of information for the xth category is , the number of test data for the xth category is , the cth test data of the xth category is , the average value of the test data for the xth category is , test data The probability of occurrence is ; The quality data is corrected by the deviation and information content, and the expression is: , The cth quality data is , the cth correction data is .

[0024] In this embodiment, the method for constructing a quality evaluation information generation model according to the correction data includes: The objective function of the quality evaluation information generation model is constructed based on the corrected data. The expression is: , The loss function is , the objective function of the i-th batch is , the quality weight is , the satisfaction weight is , the service satisfaction of the i-th batch of products is , the product quality score of the i-th batch is ; The quality evaluation information generation model includes random forest algorithm, feature extraction algorithm, and deep learning algorithm; The random forest algorithm divides the input data into training data and test data in a ratio of 7:2; The feature extraction algorithm extracts the features of the training data through the encoder part of the neural network to obtain the service features; The deep learning algorithm takes the target function as the learning object, uses the self-attention mechanism to capture the global dependency, and generates quality evaluation information based on the global dependency; The test data is input into the quality evaluation information generation model to obtain the predicted quality evaluation information, and the regularization parameter of the quality evaluation information generation model is adjusted according to the variance of the predicted quality evaluation information and the actual quality evaluation information.

[0025] In this embodiment, the method for optimizing the quality evaluation information generation model according to the evaluation error includes: Introducing particle population, taking the variance of evaluation error as fitness error, and performing chaotic mapping on particle population, the expression is: , The chaotic random number from 0 to 1 is b, and the w+1th dimension is , the wth dimension is ; Calculate the fitness of the particle, take the position of the particle with the largest fitness as the optimal position, and update the position of the particle. The expression is: , The random numbers from 1 to 2 are , the updated position of the wth dimension of the ath particle in the t+1th iteration is , the initial position of the ath particle in the wth dimension is , a random number from 0 to 1 is , the Cauchy operator is , the optimal position of the tth iteration is ; Introduce the disturbance factor and update the particle position to obtain the disturbance position. The expression is: , , The perturbed position of the wth dimension of the ath particle in the t+1th iteration is , the updated position of the wth dimension of the ath particle in the tth iteration is , the random numbers from 0 to 1 are , , , the perturbation factor for the tth iteration is , the maximum number of iterations is , the current number of iterations is t; The corrected position is obtained by using the position updated by the standard deviation, and the expression is: , The normally distributed random numbers The standard deviation of , the perturbed position of the ath particle in the wth dimension at the tth iteration is , the corrected position of the wth dimension of the ath particle in the t+1th iteration is , the lower bound of the w-th dimension is , the upper bound of the w-th dimension is ; Continue to iterate until the maximum number of iterations is reached, then stop iterating, otherwise update the perturbation factor.

[0026] In a second aspect, a quality evaluation information generation system based on big data includes: Data collection module: used to collect service data and test data of preset products, and pre-process the service data and the test data; A screening quality module: used for screening the service data for validity to obtain valid data, and obtaining quality data through the valid data; the quality data includes a first quality and a second quality; the first quality represents a product quality score based on the service data; the second quality represents a user's service satisfaction; Correction construction module: used to correct the quality data using the test data to obtain corrected data, and to construct a quality evaluation information generation model according to the corrected data; Optimization generation module: used to optimize the quality evaluation information generation model according to the evaluation error, input the data to be evaluated into the quality evaluation information generation model, and output the generation result.

[0027] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0028] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0029] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0030] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a quality evaluation information generation device based on big data at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned quality evaluation information generation methods based on big data.

[0031] The above application Figure 1The method for generating quality evaluation information based on big data disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0032] The electronic device may also perform Figure 1 A quality evaluation information generation method based on big data is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0033] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, any one of the aforementioned quality evaluation information generation methods based on big data is executed.

[0034] 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 combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0035] 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.

[0036] 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.

[0037] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0038] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0039] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0040] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0041] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0042] 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 take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0043] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating quality evaluation information based on big data, characterized in that: The following steps are involved: Collecting service data and test data of a preset product, and preprocessing the service data and the test data; Performing validity screening on the service data to obtain valid data, and obtaining quality data through the valid data; the quality data includes a first quality and a second quality; the first quality represents a product quality score based on the service data; The second quality represents the user's service satisfaction; Using the test data to correct the quality data to obtain corrected data, and constructing a quality evaluation information generation model according to the corrected data; The quality evaluation information generation model is optimized according to the evaluation error, the data to be evaluated is input into the quality evaluation information generation model, and the generation result is output.

2. According to the method for generating quality evaluation information based on big data as described in claim 1, it is characterized in that: The method for screening the service data for validity to obtain valid data comprises: Compare the product problems and service data reported by users to calculate the correlation between product problems and service data: ， The sth product problem is , the jth service data is , product issues and service data The correlation is , the control factor is , the optimization coefficient is , service data The reference value is , product issues The reference value is , the amount of service data is , the number of product issues is , product problems Service data appears in the case of The probability of ; The service data with a correlation greater than 0.3719 is output as valid data.

3. According to the method for generating quality evaluation information based on big data as claimed in claim 1, it is characterized in that: The method for obtaining the first quality includes: Extract quality-related data from valid data and calculate product performance correlation of quality-related data; Sort the quality-related data by time to obtain the time series quality data, and calculate the product quality score based on the time series quality data: ， The product quality score of the i-th batch is , the number of customers is Q, and the adjustable coefficient is , the evaluation score of the vth customer’s zth valid data is , the product performance correlation of the zth valid data of the vth customer is , the zth valid data of the vth customer is The standard value of valid data is , the number of valid data is , the error coefficient is , valid data and standard value The variance of , the maintenance ratio of the i-th batch of products is , the time weight is ; Output the product quality score as the first quality.

4. The method for generating quality evaluation information based on big data according to claim 1, characterized in that: The method for obtaining the second quality comprises: Extract user feedback data from valid data, sort the user feedback data according to time, and obtain time series feedback data; Based on the user feedback data in the valid data, the sentiment dictionary is used to obtain the sentiment score, and the sentiment score and the excited feedback frequency are used to obtain the user's evaluation score; Calculate user service satisfaction based on time series feedback data: ， ， The first parameter is , the second parameter is , the sentiment score of the vth customer’s zth valid data is , the frequency of excited feedback of the zth valid data of the vth customer is , the offset coefficient is , the service satisfaction of the i-th batch of products is , the zth valid data of the vth customer is The standard value of valid data is , the number of customers is Q, and the number of valid data is ; Service satisfaction is output as the second quality.

5. The method for generating quality evaluation information based on big data according to claim 1, characterized in that: The method of using the test data to correct the quality data to obtain corrected data comprises: Compare the product data and test data in the service data, and calculate the deviation between the product data and test data in the service data: ， The cth product data is , the cth test data is , the kernel density estimation function is , the number of test data is N, and the square of the norm is , the error factor is , the control coefficient is ; According to the similarity of the test data, a decision tree is used to classify and calculate the amount of information of the test data: ， The amount of information for the xth category is , the number of test data for the xth category is , the cth test data of the xth category is , the average value of the test data for the xth category is , test data The probability of occurrence is ; The quality data is corrected by the deviation and information content, and the expression is: ， The cth quality data is , the cth correction data is .

6. The method for generating quality evaluation information based on big data according to claim 1, characterized in that: The method for constructing a quality evaluation information generation model according to the correction data comprises: The objective function of the quality evaluation information generation model is constructed based on the corrected data. The expression is: ， The loss function is , the objective function of the i-th batch is , the quality weight is , the satisfaction weight is , the service satisfaction of the i-th batch of products is , the product quality score of the i-th batch is ; The quality evaluation information generation model includes random forest algorithm, feature extraction algorithm, and deep learning algorithm; The random forest algorithm divides the input data into training data and test data in a ratio of 7:2; The feature extraction algorithm extracts the features of the training data through the encoder part of the neural network to obtain the service features; The deep learning algorithm takes the target function as the learning object, uses the self-attention mechanism to capture the global dependency, and generates quality evaluation information based on the global dependency; The test data is input into the quality evaluation information generation model to obtain the predicted quality evaluation information, and the regularization parameter of the quality evaluation information generation model is adjusted according to the variance of the predicted quality evaluation information and the actual quality evaluation information.

7. The method for generating quality evaluation information based on big data according to claim 1, characterized in that: The method for optimizing the quality evaluation information generation model according to the evaluation error includes: Introducing particle population, taking the variance of evaluation error as fitness error, and performing chaotic mapping on particle population, the expression is: ， The chaotic random number from 0 to 1 is b, and the w+1th dimension is , the wth dimension is ; Calculate the fitness of the particle, take the position of the particle with the largest fitness as the optimal position, and update the position of the particle. The expression is: ， The random numbers from 1 to 2 are , the updated position of the wth dimension of the ath particle in the t+1th iteration is , the initial position of the ath particle in the wth dimension is , a random number from 0 to 1 is , the Cauchy operator is , the optimal position of the tth iteration is ; Introduce the disturbance factor and update the particle position to obtain the disturbance position. The expression is: ， ， The perturbed position of the wth dimension of the ath particle in the t+1th iteration is , the updated position of the wth dimension of the ath particle in the tth iteration is , the random numbers from 0 to 1 are , , , the perturbation factor for the tth iteration is , the maximum number of iterations is , the current number of iterations is t; The corrected position is obtained by using the position updated by the standard deviation, and the expression is: ， The normally distributed random numbers The standard deviation of , the perturbed position of the ath particle in the wth dimension at the tth iteration is , the corrected position of the wth dimension of the ath particle at the t+1th iteration is , the lower bound of the w-th dimension is , the upper bound of the w-th dimension is ; Continue to iterate until the maximum number of iterations is reached, then stop iterating, otherwise update the perturbation factor.

8. A quality evaluation information generation system based on big data, used to execute the method according to any one of claims 1 to 7, characterized in that: include: Data collection module: used to collect service data and test data of preset products, and pre-process the service data and the test data; A screening quality module: used for screening the service data for validity to obtain valid data, and obtaining quality data through the valid data; the quality data includes a first quality and a second quality; the first quality represents a product quality score based on the service data; The second quality represents the user's service satisfaction; Correction construction module: used to correct the quality data using the test data to obtain corrected data, and to construct a quality evaluation information generation model according to the corrected data; Optimization generation module: used to optimize the quality evaluation information generation model according to the evaluation error, input the data to be evaluated into the quality evaluation information generation model, and output the generation result.

9. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 7.

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