Application system integration method and system based on machine intelligence

Abstract

The invention discloses an application system integration method and system based on machine intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: capturing an Internet fusion technology through multi-source data in an application system, and processing the Internet fusion technology through a distributed calculation framework; according to the information knowledge graph of the machine intelligence, constructing a machine intelligence retrieval model, and optimizing parameters of the retrieval model through machine learning; and matching a division result through a user feedback mechanism, and completing information retrieval service based on application system integration. According to the method, the efficiency and the accuracy of information retrieval service can be remarkably improved, challenges of mass data of the internet can be effectively handled, rapid and accurate information retrieval can be realized, continuous improvement of retrieval service quality is ensured, high flexibility is achieved, variable information retrieval requirements and rapid iteration of service are supported, and the method is suitable for large-scale popularization and application. And more personalized and intelligent information retrieval experience is provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an application system integration method and system based on machine intelligence. Background Art

[0002] With the rapid development of Internet technology, the amount of global information has exploded, and people's demand for information retrieval and management has also increased. Against this background, information services such as Internet search services, online news services, and website navigation have become an important part of modern life. Users not only hope to find the information they need quickly and accurately, but also expect to obtain a more personalized and intelligent service experience. This requires continuous advancement of related technologies to adapt to the development trend of the information society.

[0003] However, the existing Internet information service technology still has many shortcomings. For example, in terms of the accuracy and relevance of information retrieval, the existing technology is difficult to meet the high requirements of users. In terms of personalized recommendation, although there has been some progress, the accuracy and timeliness of recommendations still need to be improved. In addition, the ability to integrate information from different sources is weak, which requires users to switch between multiple platforms to obtain complete information. To address these problems, the present invention proposes an application system integration method based on machine intelligence, which significantly improves the efficiency and quality of information retrieval by introducing more advanced algorithms and models, and provides users with a more intelligent and convenient service experience. Summary of the invention

[0004] In view of the problems existing in the existing application system integration methods based on machine intelligence, the present invention proposes an application system integration method and system based on machine intelligence.

[0005] Therefore, the purpose of the present invention is to provide an application system integration method and system based on machine intelligence. In order to solve the problems of low efficiency and insufficient accuracy of information retrieval services in the prior art, this invention adopts technical means such as multi-source data capture and processing based on machine intelligence, optimization of retrieval model parameters and microservice architecture integration to improve the efficiency and accuracy of information retrieval.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides an application system integration method based on machine intelligence, which includes: capturing Internet fusion technology through multi-source data in the application system, and processing the Internet fusion technology using a distributed computing framework; constructing a machine intelligence retrieval model based on the information knowledge graph of machine intelligence, and optimizing the parameters of the retrieval model through machine learning; integrating the optimized retrieval model parameters using a microservice architecture, dividing the integrated retrieval model, matching the division results through a user feedback mechanism, and completing information retrieval services based on application system integration.

[0008] As a preferred solution of the machine intelligence-based application system integration method of the present invention, wherein: the Internet fusion technology includes collecting massive information data through the Internet channel, and the Internet fusion technology includes crawling massive information data through multi-source data in the application system, and the crawling steps are as follows:

[0009] Define pre-processing rules for multi-source data within the application system;

[0010] Add exception handling mechanism to verify the defined preprocessing rules;

[0011] Use automated tools to manage pre-processing rules that do not show any anomalies, triggering the application system display page that captures massive amounts of information data;

[0012] Defining the preprocessing rules of the multi-source data in the application system includes introducing a character encoding conversion function, recording the field positions of the multi-source data and identifying the field positions of the multi-source data in the stage of defining the preprocessing rules of the multi-source data in the application system;

[0013] Use machine intelligence learning algorithms to train field locations, and use sample data obtained during the training process to train the model and locate the field locations.

[0014] The adding of the exception handling mechanism includes setting a defined request timeout in the application system display page;

[0015] When the definition process captures an abnormal character encoding in the application system display page, the preprocessing rule stage of defining the multi-source data in the application system times out, and the abnormal information is recorded at this time.

[0016] As a preferred solution of the machine intelligence-based application system integration method of the present invention, wherein: the pre-processing rules after the verification definition include using test execution to verify the pre-processing rules, the scenarios of the pre-processing rules include abnormal information and normal information, and the test execution includes automated testing and manual testing;

[0017] When abnormal information is recorded, manual testing is used to verify the preprocessing rules;

[0018] When no abnormal information is recorded, normal information is recorded at this time, and the preprocessing rules are verified by automated testing;

[0019] The verification of the pre-processing rules by manual testing includes dynamically adjusting the pre-processing rules according to changes in the abnormal information and information from the user feedback mechanism;

[0020] The use of test execution to verify preprocessing rules includes dynamically adjusting the number of retries and the time interval according to the number of recorded exception information and the time interval, generating an automated test based on the dynamically adjusted number of retries and the time interval, and completing the function of automatically feeding back exception information.

[0021] As a preferred solution of the machine intelligence-based application system integration method of the present invention, the processing of the Internet fusion technology includes allocating the weight of the abnormal information based on the character encoding conversion function and the task scheduling algorithm of the distributed computing framework;

[0022] The allocation of the weight of the abnormal information includes calculating the dynamic adjustment factor based on the adjustment task queue length and the resource utilization rate of the application system during the dynamic adjustment process. The specific formula for calculating the dynamic adjustment factor is:

[0023]

[0024] Among them, γ and δ represent weight coefficients, represents the ratio of the task queue length, (1-U) represents the remaining resource utilization of the application system, U represents the resource utilization of the application system, and D i represents the dynamic adjustment factor;

[0025] By dynamically adjusting the factors, and building a machine intelligence information knowledge graph based on the severity and frequency of abnormal information, the severity includes fatal errors, errors, warnings, and notifications, and the frequency includes analyzing the number of occurrences of the severity;

[0026] According to the changes in the dynamic adjustment factor, the node positions of the information knowledge graph are dynamically updated to complete the construction of the machine intelligent retrieval model.

[0027] As a preferred solution of the machine intelligence-based application system integration method of the present invention, wherein: the optimization of the parameters of the retrieval model includes deriving abnormal information data from the information knowledge graph for training, and labeling the training data;

[0028] The exporting of abnormal information data from the information knowledge graph for training includes standardizing the character encoding conversion function, wherein the standardization includes converting the character encoding into a numerical variable using one-hot encoding, obtaining a numerical variable rule by iterating the numerical variable N times, traversing all numerical variable rules, evaluating the performance of each numerical variable rule, and performing data segmentation on the performance, wherein the data segmentation includes a training set, a validation set, and a test set;

[0029] The training set includes a probability model for constructing an objective function according to the next numerical variable rule of N iterations;

[0030] The validation set includes sampling points for searching the probability model in all numerical variable rules;

[0031] The test set includes optimizing sampling points based on information from a user feedback mechanism.

[0032] As a preferred solution of the machine intelligence-based application system integration method of the present invention, wherein: the integration of the optimized retrieval model parameters includes integrating the optimized sampling points into the microservice architecture of the application system, and the microservice architecture includes data acquisition service, data preprocessing service, model training service, model deployment service, user feedback service and information retrieval service;

[0033] The division of the integrated retrieval model includes functional division and regional division, and the functional division includes data preprocessing service, model training service and user feedback service;

[0034] The said regional division includes data acquisition services, model deployment services and information retrieval services;

[0035] According to the division results of the retrieval model, the user feedback mechanism is traced back;

[0036] Verify the information of the user feedback mechanism and the division result. The verification steps are as follows:

[0037] When the information of the user feedback mechanism is inconsistent with the division result, the exported abnormal information data is trained for the second time;

[0038] When the information of the user feedback mechanism is classified into any of the above-mentioned severity levels, the user feedback mechanism information is recorded and the reasons for the classification of the information of the user feedback mechanism are analyzed;

[0039] When the information from the user feedback mechanism is not relevant to the partitioning results, the microservice architecture is adjusted.

[0040] As a preferred solution of the machine intelligence-based application system integration method described in the present invention, the adjustment of the microservice architecture includes combining automated testing to automatically collect information on user feedback mechanisms that are not related to the division results, changing the information knowledge graph of machine intelligence, adding a machine intelligence retrieval model, and completing a variety of information retrieval services.

[0041] In the second aspect, an embodiment of the present invention provides an application system integration system based on machine intelligence, which includes: a processing module, which captures Internet fusion technology through multi-source data in the application system, and processes the Internet fusion technology using a distributed computing framework; an optimization module, which builds a machine intelligence retrieval model based on the information knowledge graph of machine intelligence, and optimizes the parameters of the retrieval model through machine learning; an integration module, which uses a microservice architecture to integrate the optimized retrieval model parameters, divides the integrated retrieval model, matches the division results through a user feedback mechanism, and completes information retrieval services based on application system integration.

[0042] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned machine intelligence-based application system integration method.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned machine intelligence-based application system integration method is implemented.

[0044] The beneficial effects of the present invention are as follows: the present invention can significantly improve the efficiency and accuracy of information retrieval services by adopting advanced means such as multi-source data capture and processing based on machine intelligence, optimization of retrieval model parameters, and microservice architecture integration. The present invention can not only effectively cope with the challenges of massive data on the Internet and achieve fast and accurate information retrieval, but also continuously optimize the retrieval model through a continuous user feedback mechanism to ensure continuous improvement in the quality of retrieval services. In addition, the present invention also has a high degree of flexibility and scalability, supports changing information retrieval needs and rapid iteration of services, and provides users with a more personalized and intelligent information retrieval experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are 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. Among them:

[0046] Figure 1 A specific flow chart of an application system integration method and system based on machine intelligence provided by an embodiment of the present invention.

[0047] Figure 2 A schematic diagram of an application system integration method based on machine intelligence and a system integration method provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0051] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0052] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0053] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] Example 1

[0055] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides an application system integration method based on machine intelligence, comprising:

[0056] S1: Capture Internet fusion technology through multi-source data within the application system and process it using a distributed computing framework.

[0057] Among them, Internet fusion technology includes using the Internet to collect massive information data, and crawling through multi-source data in the application system. Internet fusion technology includes crawling massive information data through multi-source data in the application system. The crawling steps are as follows:

[0058] Define pre-processing rules for multi-source data within the application system;

[0059] Add exception handling mechanism to verify the defined preprocessing rules;

[0060] Use automated tools to manage pre-processing rules that do not show any anomalies, triggering the application system display page that captures massive amounts of information data;

[0061] Defining the preprocessing rules of multi-source data in the application system includes introducing a character encoding conversion function. In the stage of defining the preprocessing rules of multi-source data in the application system, the field positions of the multi-source data are recorded and the field positions of the multi-source data are identified;

[0062] Use machine intelligence learning algorithms to train field locations, and use sample data obtained during the training process to train the model and locate the field locations.

[0063] Adding an exception handling mechanism includes setting a defined request timeout in the application system display page;

[0064] When the definition process captures an abnormal character encoding in the application system display page, the preprocessing rule stage of defining the multi-source data in the application system times out, and the abnormal information is recorded at this time.

[0065] Furthermore, through the multi-source data interface within the application system, massive information data is collected from various channels on the Internet, such as social media, news websites, etc. The average daily data volume can reach tens of TB. First, the pre-processing rules of the multi-source data in the application system are defined. This step includes introducing a character encoding conversion function to solve the problem of character encoding differences between different data sources. At the same time, the field positions of the multi-source data are recorded. Identifying the positions of these fields is crucial for subsequent data processing. Experimental results show that this method can achieve a field position recognition rate of more than 98%. Then, the machine intelligence learning algorithm is used to train the field position, and the model is built using the large amount of sample data accumulated during the training process to achieve field Accurately identify the location. To ensure the effectiveness and stability of the preprocessing rules, an exception handling mechanism is added, including setting the request timeout to 30 seconds in the application system display page. When encountering abnormal character encoding during the preprocessing rule definition process, the system will automatically time out and record the abnormal information, thereby reducing invalid waiting time and improving processing efficiency. Statistics show that the exception handling mechanism can reduce about 80% of invalid waiting time. Finally, use automated tools to manage those preprocessing rules that have no exceptions, trigger the data capture process of the application system display page, and ensure the efficiency and stability of data capture. The whole process fully reflects the advantages of the present invention in processing large-scale Internet data.

[0066] S1.1: Verifying the defined preprocessing rules includes using test execution to verify the preprocessing rules. The scenarios of the preprocessing rules include abnormal information and normal information. Test execution includes automated testing and manual testing.

[0067] When abnormal information is recorded, manual testing is used to verify the preprocessing rules;

[0068] When no abnormal information is recorded, normal information is recorded at this time, and the preprocessing rules are verified by automated testing;

[0069] Verify preprocessing rules using manual testing, including dynamically adjusting preprocessing rules based on changes in exception information and information from user feedback mechanisms;

[0070] Utilizing the test execution to verify the preprocessing rules includes dynamically adjusting the number of retries and the time interval according to the number of recorded exception information and the time interval, generating an automated test based on the dynamically adjusted number of retries and the time interval, and completing the function of automatically feeding back exception information.

[0071] Furthermore, the preprocessing rules after verification of the definition include using test execution to check the effect of the preprocessing rules. The test scenario includes both abnormal information and normal information. The test execution methods are divided into automated testing and manual testing. When the system records abnormal information, manual testing is used to verify the preprocessing rules. The manual test dynamically adjusts the preprocessing rules based on the changing trend of the abnormal information and the information provided by the user feedback mechanism to adapt to the changes in the data source and improve the accuracy of data processing; when no abnormal information is recorded, that is, the system records normal information, automated testing is used to verify the preprocessing rules. Automated testing can automatically adjust the number of retries and time intervals according to the number of recorded abnormal information and time intervals. For example, if the system detects that a certain abnormal information has appeared more than 5 times in the past 24 hours, it will automatically increase the frequency of checking the abnormality and reduce the time interval of retries, so as to identify and handle abnormal situations more quickly. Based on the dynamically adjusted number of retries and time intervals, the system can automatically generate a new automated test process to realize automatic feedback and processing of abnormal information. The whole process ensures the effectiveness and reliability of the data preprocessing rules and improves the efficiency and quality of data processing.

[0072] S1.2: Processing Internet fusion technology includes assigning weights to abnormal information using a task scheduling algorithm based on a distributed computing framework based on character encoding conversion functions;

[0073] The weight allocation of abnormal information includes calculating the dynamic adjustment factor based on the adjustment task queue length and the resource utilization rate of the application system during the dynamic adjustment process. The specific formula for calculating the dynamic adjustment factor is:

[0074]

[0075] Among them, γ and δ represent weight coefficients, represents the ratio of the task queue length, (1-U) represents the remaining resource utilization of the application system, U represents the resource utilization of the application system, and D i represents the dynamic adjustment factor;

[0076] By dynamically adjusting factors, we build a machine intelligence information knowledge graph based on the severity and frequency of abnormal information. Severity includes fatal errors, errors, warnings, and notifications, and frequency includes the number of occurrences of the analyzed severity.

[0077] According to the changes in the dynamic adjustment factors, the node positions of the information knowledge graph are dynamically updated to complete the construction of the machine intelligent retrieval model.

[0078] Furthermore, the task scheduling algorithm of the distributed computing framework is used to intelligently allocate the weight of the abnormal information. The dynamic adjustment factor is calculated by dynamically adjusting the task queue length and the resource utilization of the application system. For example, when the task queue length ratio increases by 20% and the remaining resource utilization of the application system drops to 10%, the dynamic adjustment factor will increase accordingly to optimize task allocation and improve processing efficiency. This process takes into account the severity of the abnormal information (such as fatal errors, errors, warnings and notifications) and its frequency of occurrence. By analyzing the number of occurrences of abnormal information of different severities within a specific time period, for example, no more than 5 fatal errors are recorded per month, while warning information may be as high as hundreds of times, a machine intelligent information knowledge graph is constructed based on this. The change of the dynamic adjustment factor directly affects the update of the node position in the information knowledge graph, ensuring that the information graph can reflect the latest distribution of abnormal information in real time, and finally completing the construction of the machine intelligent retrieval model. This process not only improves the pertinence and effectiveness of abnormal information processing, but also provides accurate data support for subsequent information retrieval services.

[0079] S2: Based on the information knowledge graph of machine intelligence, a machine intelligence retrieval model is constructed, and the parameters of the retrieval model are optimized through machine learning.

[0080] Among them, optimizing the parameters of the retrieval model includes exporting abnormal information data from the information knowledge graph for training and labeling the training data;

[0081] Exporting abnormal information data from the information knowledge graph for training includes standardizing the character encoding conversion function, which includes using one-hot encoding to convert character encoding into numerical variables, iterating the numerical variables N times to obtain numerical variable rules, traversing all numerical variable rules, evaluating the performance of each numerical variable rule, and performing data segmentation on the performance, where the data segmentation includes training set, validation set and test set;

[0082] The training set includes a probability model of the target function constructed according to the next numerical variable rule of N iterations;

[0083] The validation set includes sampling points for searching the probability model in all numerical variable rules;

[0084] The test set includes optimization of sampling points based on information from user feedback mechanisms.

[0085] Preferably, the process of building a machine intelligence retrieval model based on the information knowledge graph of machine intelligence includes exporting abnormal information data from the information knowledge graph for training and annotating the training data. Specifically, after exporting the abnormal information data, the character encoding conversion function is first standardized, and the character encoding is converted into a numerical variable using a one-hot encoding method. The numerical variable is iterated multiple times (such as 1000 times) to extract the numerical variable rules, and then all the numerical variable rules are traversed to evaluate the performance of each rule. After the performance evaluation, the data is divided into three parts: a training set, a validation set, and a test set, wherein the training set is used to The next numerical variable rule builds a probability model of the objective function. The validation set is used to search for the best sampling points of the probability model in all numerical variable rules, and the test set is used to further optimize these sampling points based on the information of the user feedback mechanism to ensure the accuracy and practicality of the model. For example, in one practice, the training set accounts for 70% of the total data volume, the validation set accounts for 15%, and the test set also accounts for 15%. Through such a data segmentation strategy, the model training process can make full use of the existing data while maintaining the model's good prediction ability for future data, and ultimately achieve effective optimization of the retrieval model parameters. The key parameters in the retrieval model process are shown in Table 1 below:

[0086] Table 1 Key parameters in the retrieval model process

[0087]

[0088] Table 1 shows the key parameter settings for building machine intelligence retrieval models in five different practical cases, including the number of iterations, data set segmentation ratio (training set, validation set, test set), character encoding conversion method, data preprocessing method, and model optimization goal. By comparing these parameters, we can see that the details of model training are adjusted according to specific needs in different cases. For example, Cases A and D both use a 70% training set ratio and a one-hot encoding method, but the number of iterations is 1,000 and 1,200 respectively, and the optimization goals are to improve model prediction accuracy and improve model training speed respectively. These data help us understand how to optimize model performance by flexibly adjusting parameters.

[0089] S3: Use the microservice architecture to integrate the optimized retrieval model parameters, divide the integrated retrieval model, match the division results through the user feedback mechanism, and complete the information retrieval service based on application system integration.

[0090] Integrating the optimized retrieval model parameters includes integrating the optimized sampling points into the microservice architecture of the application system, and the microservice architecture includes data acquisition service, data preprocessing service, model training service, model deployment service, user feedback service and information retrieval service;

[0091] The integrated retrieval model is divided into functional division and regional division. The functional division includes data preprocessing service, model training service and user feedback service.

[0092] The regional divisions include data acquisition services, model deployment services, and information retrieval services;

[0093] According to the division results of the retrieval model, the user feedback mechanism is traced back;

[0094] Verify the information of the user feedback mechanism and the division results. The verification steps are as follows:

[0095] When the information of the user feedback mechanism is inconsistent with the division result, the exported abnormal information data is trained for the second time;

[0096] When the information of the user feedback mechanism is classified into any of the seriousness levels, the user feedback mechanism information is recorded and the reasons for the classification of the information of the user feedback mechanism are analyzed;

[0097] When the information from the user feedback mechanism is not relevant to the partitioning results, the microservice architecture is adjusted.

[0098] Furthermore, the optimized sampling points are integrated into the microservice architecture of the application system, including data acquisition service, data preprocessing service, model training service, model deployment service, user feedback service and information retrieval service. The integrated retrieval model is divided according to functions and service areas. The functional division covers data preprocessing service, model training service and user feedback service, while the regional division involves data acquisition service, model deployment service and information retrieval service. This division method ensures the independence and flexibility of each service, and facilitates management and maintenance. According to the division results of the retrieval model, the system can complete the traceability of the user feedback mechanism. When the user feedback information is inconsistent with the division results, the system will re- The exported abnormal information data is trained to improve the accuracy of the model. For example, in one practice, the system detected that 10% of the user feedback did not match the model division results, and then started the retraining process. When the user feedback information belongs to any category of severity, such as fatal errors or warnings, the system will record the feedback information and conduct in-depth analysis of the reasons for such feedback for subsequent improvements. In the case where the user feedback information is completely irrelevant to the model division results, the system will make appropriate adjustments to the microservice architecture to ensure the efficiency of the service and user satisfaction. For example, if it is found that the correlation between user feedback and information retrieval services is lower than the expected 80%, the system will adjust the service configuration to better meet user needs.

[0099] S3.1: Adjustments to the microservice architecture include combining automated testing to automatically collect information from user feedback mechanisms that are not related to the segmentation results, changing the information knowledge graph of machine intelligence, adding the construction of machine intelligence retrieval models, and completing variable information retrieval services.

[0100] In a preferred embodiment, an application system integration system based on machine intelligence includes a processing module, which captures Internet fusion technology through multi-source data in the application system and processes the Internet fusion technology using a distributed computing framework; an optimization module, which builds a machine intelligence retrieval model based on the information knowledge graph of machine intelligence and optimizes the parameters of the retrieval model through machine learning; an integration module, which integrates the optimized retrieval model parameters using a microservice architecture, divides the integrated retrieval model, matches the division results through a user feedback mechanism, and completes information retrieval services based on application system integration.

[0101] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0102] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0103] In summary, the present invention can significantly improve the efficiency and accuracy of information retrieval services by adopting advanced means such as multi-source data capture and processing based on machine intelligence, optimization of retrieval model parameters, and microservice architecture integration. The present invention can not only effectively cope with the challenges of massive data on the Internet and achieve fast and accurate information retrieval, but also continuously optimize the retrieval model through a continuous user feedback mechanism to ensure the continuous improvement of the retrieval service quality. In addition, the present invention also has a high degree of flexibility and scalability, supports changing information retrieval needs and rapid iteration of services, and provides users with a more personalized and intelligent information retrieval experience.

[0104] Example 2

[0105] Reference Figure 1 and Figure 2 , which is the second embodiment of the present invention, and this embodiment provides an application system integration method based on machine intelligence. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0106] During the experiment of this technical solution, a large amount of information data was first collected from the Internet channels through the multi-source data interface within the application system. For example, during the experiment, an average of about 15TB of data was captured from more than 1,200 different websites or platforms every day. The preprocessing rules for multi-source data were defined, including the introduction of character encoding conversion functions, and the field positions of multi-source data were recorded. The recognition rate reached 99%. The field positions were trained through machine intelligence learning algorithms. After 1,200 iterations, the model was able to accurately identify and locate key fields in multi-source data. In order to ensure the effectiveness of the preprocessing rules, a 30-second request timeout was set in the application system display page, and exception information was recorded through the exception handling mechanism. The experimental results show that the exception handling mechanism reduces about 85% of invalid waiting time.

[0107] Abnormal information data was exported from the information knowledge graph for training, and the training data was annotated. The training data was converted into numerical variables through the one-hot encoding method, and 1,500 iterations were performed to refine the numerical variable rules. The data was divided into 72% training set, 18% validation set, and 10% test set to ensure the comprehensiveness and accuracy of model training. Through these data sets, the probability model of the objective function was constructed, and the best sampling points were searched in the validation set. The test set further optimized these sampling points based on the information of the user feedback mechanism to ensure the accuracy and practicality of the model.

[0108] The optimized retrieval model parameters are integrated into the microservice architecture of the application system, which includes data acquisition service, data preprocessing service, model training service, model deployment service, user feedback service and information retrieval service. The model is divided according to function and service area, and feedback is provided by users. The experimental data of the present invention are shown in Table 2 below:

[0109] Table 2 Experimental data table of the present invention

[0110] Data Types Specific values Number of daily data sources More than 1,200 websites or platforms Average daily crawled data volume About 15 TB Field position recognition rate 99％ Field location training iterations 1200 times Request timeout 30 seconds Exception handling mechanism reduces invalid waiting time About 85% Number of iterations of numerical variable rule 1500 times The proportion of training set 72％ Validation set ratio 18％ Test set ratio 10％

[0111] Table 2 shows the specific parameter settings and experimental results of each key step in the process of building an application system integrated with information retrieval services based on machine intelligence. During the experiment, an average of about 15TB of data was captured from more than 1,200 websites or platforms every day, and the field position recognition rate reached 99%. The model was trained through 1,200 iterations to accurately identify key fields. A 30-second request timeout was set in the application system display page. The exception handling mechanism reduced about 85% of the invalid waiting time. The training data was converted to one-hot encoding through 1,500 iterations and was divided into 72% training set, 18% validation set and 10% test set. These data helped optimize the model parameters and ensure the accuracy and practicality of the model. The comparison between the present invention and the prior art is shown in Table 3 below:

[0112] Table 3 Comparison between the present invention and the prior art

[0113]

[0114]

[0115] Table 3 shows the significant advantages of the technical solution of the present invention in terms of data capture capability, field position recognition rate, exception handling mechanism, number of model training iterations, data set segmentation ratio, user feedback mechanism, and microservice architecture, which effectively improves the overall performance and user experience of information retrieval services.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for integrating an application system based on machine intelligence, characterized in that: include: Capture Internet fusion technology through multi-source data in the application system and process it using a distributed computing framework; According to the machine intelligence information knowledge graph, a machine intelligence retrieval model is constructed, and the parameters of the retrieval model are optimized through machine learning; The microservice architecture is used to integrate the optimized retrieval model parameters, divide the integrated retrieval model, match the division results through the user feedback mechanism, and complete the information retrieval service based on application system integration.

2. The method for integrating application systems based on machine intelligence according to claim 1, characterized in that: The Internet fusion technology includes collecting massive information data through the Internet channel, and the Internet fusion technology includes capturing massive information data through the multi-source data in the application system. The steps of the capture are as follows: Define pre-processing rules for multi-source data within the application system; Add exception handling mechanism to verify the defined preprocessing rules; Use automated tools to manage pre-processing rules that do not show any anomalies, triggering the application system display page that captures massive amounts of information data; Defining the preprocessing rules of the multi-source data in the application system includes introducing a character encoding conversion function, recording the field positions of the multi-source data and identifying the field positions of the multi-source data in the stage of defining the preprocessing rules of the multi-source data in the application system; Use machine intelligence learning algorithms to train field locations, and use sample data obtained during the training process to train the model and locate the field locations. The adding of the exception handling mechanism includes setting a defined request timeout in the application system display page; When the definition process captures an abnormal character encoding in the application system display page, the preprocessing rule stage of defining the multi-source data in the application system times out, and the abnormal information is recorded at this time.

3. The method for integrating application systems based on machine intelligence according to claim 2, characterized in that: The verification of the defined preprocessing rules includes verifying the preprocessing rules by using test execution, the scenarios of the preprocessing rules include abnormal information and normal information, and the test execution includes automated testing and manual testing; When abnormal information is recorded, manual testing is used to verify the preprocessing rules; When no abnormal information is recorded, normal information is recorded at this time, and the preprocessing rules are verified by automated testing; The verification of the pre-processing rules by manual testing includes dynamically adjusting the pre-processing rules according to changes in the abnormal information and information from the user feedback mechanism; The use of test execution to verify preprocessing rules includes dynamically adjusting the number of retries and the time interval according to the number of recorded exception information and the time interval, generating an automated test based on the dynamically adjusted number of retries and the time interval, and completing the function of automatically feeding back exception information.

4. The method for integrating application systems based on machine intelligence according to claim 3, characterized in that: The processing of the Internet fusion technology includes allocating the weight of the abnormal information based on the character encoding conversion function and using the task scheduling algorithm of the distributed computing framework; The allocation of the weight of the abnormal information includes calculating the dynamic adjustment factor based on the adjustment task queue length and the resource utilization rate of the application system during the dynamic adjustment process. The specific formula for calculating the dynamic adjustment factor is: Among them, γ and δ represent weight coefficients, represents the ratio of the task queue length, (1-U) represents the remaining resource utilization of the application system, U represents the resource utilization of the application system, and D i represents the dynamic adjustment factor; By dynamically adjusting the factors, and building a machine intelligence information knowledge graph based on the severity and frequency of abnormal information, the severity includes fatal errors, errors, warnings, and notifications, and the frequency includes analyzing the number of occurrences of the severity; According to the changes in the dynamic adjustment factor, the node positions of the information knowledge graph are dynamically updated to complete the construction of the machine intelligent retrieval model.

5. The method for integrating application systems based on machine intelligence according to claim 4, characterized in that: The optimizing of the parameters of the retrieval model includes deriving abnormal information data from the information knowledge graph for training, and labeling the training data; The exporting of abnormal information data from the information knowledge graph for training includes standardizing the character encoding conversion function, wherein the standardization includes converting the character encoding into a numerical variable using one-hot encoding, obtaining a numerical variable rule by iterating the numerical variable N times, traversing all numerical variable rules, evaluating the performance of each numerical variable rule, and performing data segmentation on the performance, wherein the data segmentation includes a training set, a validation set, and a test set; The training set includes a probability model for constructing an objective function according to the next numerical variable rule of N iterations; The validation set includes sampling points for searching the probability model in all numerical variable rules; The test set includes optimizing sampling points based on information from a user feedback mechanism.

6. The method for integrating application systems based on machine intelligence according to claim 5, characterized in that: Integrating the optimized retrieval model parameters includes integrating the optimized sampling points into the microservice architecture of the application system, wherein the microservice architecture includes data acquisition service, data preprocessing service, model training service, model deployment service, user feedback service and information retrieval service; The division of the integrated retrieval model includes functional division and regional division, and the functional division includes data preprocessing service, model training service and user feedback service; The said regional division includes data acquisition services, model deployment services and information retrieval services; According to the division results of the retrieval model, the user feedback mechanism is traced back; Verify the information of the user feedback mechanism and the division result. The verification steps are as follows: When the information of the user feedback mechanism is inconsistent with the division result, the exported abnormal information data is trained for the second time; When the information of the user feedback mechanism is classified into any of the above-mentioned severity levels, the user feedback mechanism information is recorded and the reasons for the classification of the information of the user feedback mechanism are analyzed; When the information from the user feedback mechanism is not relevant to the partitioning results, the microservice architecture is adjusted.

7. The method for integrating application systems based on machine intelligence according to claim 6, characterized in that: The adjustment of the microservice architecture includes combining automated testing to automatically collect information about user feedback mechanisms that are not related to the division results, changing the information knowledge graph of machine intelligence, adding a machine intelligence retrieval model, and completing a variety of information retrieval services.

8. An application system integration system based on machine intelligence, based on the application system integration method based on machine intelligence according to any one of claims 1 to 7, characterized in that: include, A processing module, which captures Internet fusion technology through multi-source data in the application system and processes the Internet fusion technology using a distributed computing framework; The optimization module builds a machine intelligence retrieval model based on the machine intelligence information knowledge graph and optimizes the parameters of the retrieval model through machine learning; The integration module uses the microservice architecture to integrate the optimized retrieval model parameters, divides the integrated retrieval model, matches the division results through the user feedback mechanism, and completes the information retrieval service based on application system integration.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the machine intelligence-based application system integration method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the machine intelligence-based application system integration method according to any one of claims 1 to 7 are implemented.

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